Implement randomization method from XavQiuAhm2021

Fix duplicated startup constraint

.JuliaFormatter.toml

@@ -0,0 +1,5 @@
+always_for_in = true
+always_use_return = true
+margin = 80
+remove_extra_newlines = true
+short_to_long_function_def = true

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,25 @@
+---
+name: Bug report
+about: Something is broken in the package
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## Description
+
+A clear and concise description of what the bug is.
+
+## Steps to Reproduce
+
+Please describe how can the developers reproduce the problem in their own computers. Code snippets and sample input files are specially helpful. For example:
+
+1. Install the package
+2. Run the code below with the attached input file...
+3. The following error appears...
+
+## System Information
+ - Operating System: [e.g. Ubuntu 20.04]
+ - Julia version: [e.g. 1.4]
+ - Package version: [e.g. 0.0.1]

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,8 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Feature Request
+    url: https://github.com/ANL-CEEESA/UnitCommitment.jl/discussions/categories/feature-requests
+    about:  Submit ideas for new features and small enhancements
+  - name: Help & FAQ
+    url: https://github.com/ANL-CEEESA/UnitCommitment.jl/discussions/categories/help-faq
+    about: Ask questions about the package and get help from the community

.github/workflows/benchmark.yml

@@ -1,28 +0,0 @@
-name: Benchmark
-on: push
-jobs:
-  benchmark:
-    runs-on: [self-hosted, benchmark]
-    if: "contains(github.event.head_commit.message, '[benchmark]')"
-    timeout-minutes: 10080
-    steps:
-    - uses: actions/checkout@v1
-    - name: Benchmark
-      run: |
-        julia --project=@. -e 'using Pkg; Pkg.instantiate()'
-        make build/sysimage.so
-        make -C benchmark clean
-        make -C benchmark -kj4
-        make -C benchmark tables
-        make -C benchmark clean-mps clean-sol
-    - name: Upload logs
-      uses: actions/upload-artifact@v2
-      with:
-        name: Logs
-        path: benchmark/results/*
-    - name: Upload tables & charts
-      uses: actions/upload-artifact@v2
-      with:
-        name: Tables
-        path: benchmark/tables/*
-

.github/workflows/lint.yml

@@ -0,0 +1,28 @@
+name: lint
+on:
+  push:
+  pull_request:
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: '1'
+      - uses: actions/checkout@v1
+      - name: Format check
+        shell: julia --color=yes {0}
+        run: |
+          using Pkg
+          Pkg.add(PackageSpec(name="JuliaFormatter", version="0.14.4"))
+          using JuliaFormatter
+          format("src", verbose=true)
+          format("test", verbose=true)
+          format("benchmark", verbose=true)
+          out = String(read(Cmd(`git diff`)))
+          if isempty(out)
+              exit(0)
+          end
+          @error "Some files have not been formatted !!!"
+          write(stdout, out)
+          exit(1)

.github/workflows/test.yml

@@ -1,20 +1,16 @@
 name: Tests
 on:
   push:
-    paths:
-      - '**.jl'
-      - '**.toml'
   pull_request:
-    paths:
-      - '**.jl'
-      - '**.toml'
+  schedule:
+    - cron: '45 10 * * *'
 jobs:
   test:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        julia-version: ['1.3', '1.4', '1']
-        julia-arch: [x64, x86]
+        julia-version: ['1.4', '1.5', '1.6']
+        julia-arch: [x64]
         os: [ubuntu-latest, windows-latest, macOS-latest]
         exclude:
           - os: macOS-latest

.gitignore

@@ -8,9 +8,13 @@
 benchmark/results
 benchmark/runs
 benchmark/tables
+benchmark/tmp.json
 build
 instances/**/*.json
 instances/_source
 local
 notebooks
 TODO.md
+docs/_build
+.vscode
+Manifest.toml

CHANGELOG.md

@@ -1,11 +1,60 @@
-# UnitCommitment.jl
+# Changelog
 
-### Version 0.1.1 (Nov 16, 2020)
+All notable changes to this project will be documented in this file.
 
-* Fixes to MATLAB and PGLIB-UC instances
-* Add OR-LIB and Tejada19 instances
-* Improve documentation
+- The format is based on [Keep a Changelog][changelog].
+- This project adheres to [Semantic Versioning][semver].
+- For versions before 1.0, we follow the [Pkg.jl convention][pkjjl]
+  that `0.a.b` is compatible with `0.a.c`.
 
-### Version 0.1.0 (Nov 6, 2020)
+[changelog]: https://keepachangelog.com/en/1.0.0/
+[semver]: https://semver.org/spec/v2.0.0.html
+[pkjjl]: https://pkgdocs.julialang.org/v1/compatibility/#compat-pre-1.0
 
-* Initial public release
+## [0.2.2] - 2021-07-21
+### Fixed
+- Fix small bug in validation scripts related to startup costs
+- Fix duplicated startup constraints (@mtanneau, #12)
+
+## [0.2.1] - 2021-06-02
+### Added
+- Add multiple ramping formulations (ArrCon2000, MorLatRam2013, DamKucRajAta2016, PanGua2016)
+- Add multiple piecewise-linear costs formulations (Garver1962, CarArr2006, KnuOstWat2018)
+- Allow benchmark scripts to compare multiple formulations
+
+## [0.2.0] - 2021-05-28
+### Added
+- Add sub-hourly unit commitment.
+- Add `UnitCommitment.write(filename, solution)`.
+- Add current mathematical formulation to the documentation.
+
+### Changed
+- Rename "Time (h)" parameter to "Time horizon (h)".
+- Rename `UnitCommitment.get_solution` to `UnitCommitment.solution`, for better
+  consistency with JuMP style.
+- Add an underscore to the name of all functions that do not appear in the
+  documentation (e.g. `something` becomes `_something`) These functions are not
+  part of the public API and may change without notice, even in PATCH releases.
+- The function `UnitCommitment.build_model` now returns a plain JuMP model. The
+  struct `UnitCommitmentModel` has been completely removed. Accessing model
+  elements can now be accomplished as follows:
+    - `model.vars.x[idx]` becomes `model[:x][idx]`
+    - `model.eqs.y[idx]` becomes `model[:eq_y][idx]`
+    - `model.expr.z[idx]` becomes `model[:expr_z][idx]`
+    - `model.obj` becomes `model[:obj]`
+    - `model.isf` becomes `model[:isf]`
+    - `model.lodf` becomes `model[:lodf]`
+
+### Fixed
+- Properly validate solutions with price-sensitive loads.
+
+## [0.1.1] - 2020-11-16
+### Added
+- Add OR-LIB and Tejada19 instances.
+- Improve documentation.
+
+## Fixed
+- Fixes to MATLAB and PGLIB-UC instances.
+
+## [0.1.0] - 2020-11-06
+- Initial public release.

Makefile

@@ -3,27 +3,30 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 
 JULIA := julia --color=yes --project=@.
-MKDOCS := ~/.local/bin/mkdocs
-VERSION := 0.1
+VERSION := 0.2
 
-build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
+build/sysimage.so: src/utils/sysimage.jl Project.toml Manifest.toml
 	mkdir -p build
 	mkdir -p benchmark/results/test
-	cd benchmark; $(JULIA) --trace-compile=../build/precompile.jl run.jl test/case14.1.sol.json
-	$(JULIA) src/sysimage.jl
+	cd benchmark; $(JULIA) --trace-compile=../build/precompile.jl benchmark.jl test/case14
+	$(JULIA) src/utils/sysimage.jl
 
 clean:
 	rm -rf build/*
 
 docs:
-	$(MKDOCS) build -d ../docs/$(VERSION)/
-	rm ../docs/$(VERSION)/*.ipynb
+	cd docs; make clean; make dirhtml
+	rsync -avP --delete-after docs/_build/dirhtml/ ../docs/$(VERSION)/
 	
-install-deps-docs:
-	pip install --user mkdocs mkdocs-cinder python-markdown-math
-
 test: build/sysimage.so
 	@echo Running tests...
 	$(JULIA) --sysimage build/sysimage.so -e 'using Pkg; Pkg.test("UnitCommitment")' | tee build/test.log
 
-.PHONY: docs test
+
+format:
+	julia -e 'using JuliaFormatter; format(["src", "test", "benchmark"], verbose=true);'
+
+install-deps:
+	julia -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter", version="0.14.4"))'
+
+.PHONY: docs test format install-deps

Manifest.toml

@@ -1,367 +0,0 @@
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-uuid = "7da25872-d9ce-5375-a4d3-7a845f58efdd"
-version = "0.108.5+3"
-
-[[PackageCompiler]]
-deps = ["Libdl", "Pkg", "UUIDs"]
-git-tree-sha1 = "3eee77c94646163f15bd8626acf494360897f890"
-uuid = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
-version = "1.2.3"
-
-[[Parsers]]
-deps = ["Dates"]
-git-tree-sha1 = "6fa4202675c05ba0f8268a6ddf07606350eda3ce"
-uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "1.0.11"
-
-[[Pkg]]
-deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
-uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-
-[[Printf]]
-deps = ["Unicode"]
-uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-
-[[REPL]]
-deps = ["InteractiveUtils", "Markdown", "Sockets"]
-uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
-
-[[Random]]
-deps = ["Serialization"]
-uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-
-[[SHA]]
-uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
-
-[[Serialization]]
-uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
-
-[[SharedArrays]]
-deps = ["Distributed", "Mmap", "Random", "Serialization"]
-uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
-
-[[Sockets]]
-uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
-
-[[SparseArrays]]
-deps = ["LinearAlgebra", "Random"]
-uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-
-[[SpecialFunctions]]
-deps = ["OpenSpecFun_jll"]
-git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
-uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "0.10.3"
-
-[[StaticArrays]]
-deps = ["LinearAlgebra", "Random", "Statistics"]
-git-tree-sha1 = "da4cf579416c81994afd6322365d00916c79b8ae"
-uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "0.12.5"
-
-[[Statistics]]
-deps = ["LinearAlgebra", "SparseArrays"]
-uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-
-[[Test]]
-deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
-uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[[TranscodingStreams]]
-deps = ["Random", "Test"]
-git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
-uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.9.5"
-
-[[UUIDs]]
-deps = ["Random", "SHA"]
-uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-
-[[Unicode]]
-uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
-
-[[ZipFile]]
-deps = ["Libdl", "Printf", "Zlib_jll"]
-git-tree-sha1 = "c3a5637e27e914a7a445b8d0ad063d701931e9f7"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.9.3"
-
-[[Zlib_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6"
-uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.11+18"

Project.toml

@@ -2,10 +2,11 @@ name = "UnitCommitment"
 uuid = "64606440-39ea-11e9-0f29-3303a1d3d877"
 authors = ["Santos Xavier, Alinson <axavier@anl.gov>"]
 repo = "https://github.com/ANL-CEEESA/UnitCommitment.jl"
-version = "0.1.1"
+version = "0.2.2"
 
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
@@ -14,11 +15,13 @@ Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 Cbc = "0.7"
 DataStructures = "0.18"
+Distributions = "0.25"
 GZip = "0.5"
 JSON = "0.21"
 JuMP = "0.21"
@@ -28,7 +31,8 @@ julia = "1"
 
 [extras]
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
+Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Cbc", "Test"]
+test = ["Cbc", "Test", "Gurobi"]

README.md

@@ -1,44 +1,144 @@
-<a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/actions?query=workflow%3ATest+branch%3Adev"><img src="https://github.com/iSoron/UnitCommitment.jl/workflows/Tests/badge.svg"></img></a>
-<a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/actions?query=workflow%3ABenchmark+branch%3Adev+is%3Asuccess"><img src="https://github.com/iSoron/UnitCommitment.jl/workflows/Benchmark/badge.svg"></img></a>
-<a href="https://doi.org/10.5281/zenodo.4269874"><img src="https://zenodo.org/badge/doi/10.5281/zenodo.4269874.svg" alt="DOI"></a>
+<h1 align="center">UnitCommitment.jl</h1>
+<p align="center">
+  <a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/actions?query=workflow%3ATest+branch%3Adev">
+    <img src="https://github.com/iSoron/UnitCommitment.jl/workflows/Tests/badge.svg"></img>
+  </a>
+  <a href="https://doi.org/10.5281/zenodo.4269874">
+    <img src="https://zenodo.org/badge/doi/10.5281/zenodo.4269874.svg" alt="DOI"></img>
+  </a>
+  <a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/releases/">
+    <img src="https://img.shields.io/github/v/release/ANL-CEEESA/UnitCommitment.jl?include_prereleases&label=pre-release">
+  </a>
+  <a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/discussions">
+    <img src="https://img.shields.io/badge/GitHub-Discussions-%23fc4ebc" />
+  </a>
+</p>
 
+**UnitCommitment.jl** (UC.jl) is an optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and Julia/JuMP implementations of state-of-the-art mixed-integer programming formulations.
 
-# UnitCommitment.jl
+## Package Components
 
-**UnitCommitment.jl** (UC.jl) is an optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and JuMP implementations of state-of-the-art mixed-integer programming formulations.
-
-### Package Components
-
-* **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
-* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature and extended to make them more challenging and realistic.
-* **Model Implementation**: The package provides a Julia/JuMP implementation of state-of-the-art formulations and solution methods for SCUC. Our goal is to keep this implementation up-to-date, as new methods are proposed in the literature.
+* **Data Format:** The package proposes an extensible and fully-documented JSON-based data format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
+* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature, converted into a common data format, and extended using data-driven methods to make them more challenging and realistic.
+* **Model Implementation**: The package provides Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
 * **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
 
-### Documentation
+[ArrCon2000]: https://doi.org/10.1109/59.871739
+[CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
+[DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
+[Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
+[KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
+[MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
+[PanGua2016]: https://doi.org/10.1287/opre.2016.1520
+[XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
 
-* [Usage](https://anl-ceeesa.github.io/UnitCommitment.jl/0.1/usage/)
-* [Data Format](https://anl-ceeesa.github.io/UnitCommitment.jl/0.1/format/)
-* [Instances](https://anl-ceeesa.github.io/UnitCommitment.jl/0.1/instances/)
+## Sample Usage
 
-### Authors
-* **Alinson Santos Xavier** (Argonne National Laboratory)
+```julia
+using Cbc
+using JuMP
+using UnitCommitment
+
+import UnitCommitment:
+    Formulation,
+    KnuOstWat2018,
+    MorLatRam2013,
+    ShiftFactorsFormulation
+
+# Read benchmark instance
+instance = UnitCommitment.read_benchmark(
+    "matpower/case118/2017-02-01",
+)
+
+# Construct model (using state-of-the-art defaults)
+model = UnitCommitment.build_model(
+    instance = instance,
+    optimizer = Cbc.Optimizer,
+)
+
+# Construct model (using customized formulation)
+model = UnitCommitment.build_model(
+    instance = instance,
+    optimizer = Cbc.Optimizer,
+    formulation = Formulation(
+        pwl_costs = KnuOstWat2018.PwlCosts(),
+        ramping = MorLatRam2013.Ramping(),
+        startup_costs = MorLatRam2013.StartupCosts(),
+        transmission = ShiftFactorsFormulation(
+            isf_cutoff = 0.005,
+            lodf_cutoff = 0.001,
+        ),
+    ),
+)
+
+# Modify the model (e.g. add custom constraints)
+@constraint(
+    model,
+    model[:is_on]["g3", 1] + model[:is_on]["g4", 1] <= 1,
+)
+
+# Solve model
+UnitCommitment.optimize!(model)
+
+# Extract solution
+solution = UnitCommitment.solution(model)
+UnitCommitment.write("/tmp/output.json", solution)
+```
+
+## Documentation
+
+1. [Usage](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/usage/)
+2. [Data Format](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/format/)
+3. [Instances](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/instances/)
+4. [JuMP Model](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/model/)
+
+## Authors
+* **Alinson S. Xavier** (Argonne National Laboratory)
+* **Aleksandr M. Kazachkov** (University of Florida)
 * **Feng Qiu** (Argonne National Laboratory)
 
-### Acknowledgments
+## Acknowledgments
 
-* We would like to thank **Aleksandr M. Kazachkov** (University of Florida), **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
+* We would like to **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
 
-* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
+* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357
 
-### Citing
+* Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.
 
-If you use UnitCommitment.jl in your research, we request that you cite the package as follows:
+## Citing
 
-* **Alinson S. Xavier, Feng Qiu**. "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
+If you use UnitCommitment.jl in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
 
-If you make use of the provided instances files, we request that you additionally cite the original sources, as described in the [instances page](https://anl-ceeesa.github.io/UnitCommitment.jl/0.1/instances/).
+* **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu**. "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
 
-### License
+If you use the instances, we additionally request that you cite the original sources, as described in the [instances page](docs/instances.md).
 
-Released under the modified BSD license. See `LICENSE.md` for more details.
+## License
+
+```text
+UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment
+Copyright © 2020-2021, UChicago Argonne, LLC. All Rights Reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted
+provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of
+   conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of
+   conditions and the following disclaimer in the documentation and/or other materials provided
+   with the distribution.
+3. Neither the name of the copyright holder nor the names of its contributors may be used to
+   endorse or promote products derived from this software without specific prior written
+   permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
+AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+```
 

benchmark/Makefile

@@ -1,105 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-SHELL := /bin/bash
-JULIA := julia --project=. --sysimage ../build/sysimage.so
-TIMESTAMP := $(shell date "+%Y-%m-%d %H:%M")
-SRC_FILES := $(wildcard ../src/*.jl)
-
-INSTANCES_PGLIB := \
-        pglib-uc/ca/2014-09-01_reserves_0 \
-        pglib-uc/ca/2014-09-01_reserves_1 \
-        pglib-uc/ca/2015-03-01_reserves_0 \
-        pglib-uc/ca/2015-06-01_reserves_0 \
-        pglib-uc/ca/Scenario400_reserves_1 \
-        pglib-uc/ferc/2015-01-01_lw \
-        pglib-uc/ferc/2015-05-01_lw \
-        pglib-uc/ferc/2015-07-01_hw \
-        pglib-uc/ferc/2015-10-01_lw \
-        pglib-uc/ferc/2015-12-01_lw \
-        pglib-uc/rts_gmlc/2020-04-03 \
-        pglib-uc/rts_gmlc/2020-09-20 \
-        pglib-uc/rts_gmlc/2020-10-27 \
-        pglib-uc/rts_gmlc/2020-11-25 \
-        pglib-uc/rts_gmlc/2020-12-23
-
-INSTANCES_MATPOWER := \
-	matpower/case118/2017-02-01 \
-	matpower/case118/2017-08-01 \
-	matpower/case300/2017-02-01 \
-	matpower/case300/2017-08-01 \
-	matpower/case1354pegase/2017-02-01 \
-	matpower/case1888rte/2017-02-01 \
-	matpower/case1951rte/2017-08-01 \
-	matpower/case2848rte/2017-02-01 \
-	matpower/case2868rte/2017-08-01 \
-	matpower/case3375wp/2017-08-01 \
-	matpower/case6468rte/2017-08-01 \
-	matpower/case6515rte/2017-08-01
-
-INSTANCES_ORLIB := \
-	or-lib/20_0_1_w \
-	or-lib/20_0_5_w \
-	or-lib/50_0_2_w \
-	or-lib/75_0_2_w \
-	or-lib/100_0_1_w \
-	or-lib/100_0_4_w \
-	or-lib/100_0_5_w \
-	or-lib/200_0_3_w \
-	or-lib/200_0_7_w \
-	or-lib/200_0_9_w
-
-INSTANCES_TEJADA19 := \
-	tejada19/UC_24h_290g \
-	tejada19/UC_24h_623g \
-	tejada19/UC_24h_959g \
-	tejada19/UC_24h_1577g \
-	tejada19/UC_24h_1888g \
-	tejada19/UC_168h_72g \
-	tejada19/UC_168h_86g \
-	tejada19/UC_168h_130g \
-	tejada19/UC_168h_131g \
-	tejada19/UC_168h_199g
-
-SAMPLES := 1 2 3 4 5
-SOLUTIONS_MATPOWER := $(foreach s,$(SAMPLES),$(addprefix results/,$(addsuffix .$(s).sol.json,$(INSTANCES_MATPOWER))))
-SOLUTIONS_PGLIB := $(foreach s,$(SAMPLES),$(addprefix results/,$(addsuffix .$(s).sol.json,$(INSTANCES_PGLIB))))
-SOLUTIONS_ORLIB := $(foreach s,$(SAMPLES),$(addprefix results/,$(addsuffix .$(s).sol.json,$(INSTANCES_ORLIB))))
-SOLUTIONS_TEJADA19 := $(foreach s,$(SAMPLES),$(addprefix results/,$(addsuffix .$(s).sol.json,$(INSTANCES_TEJADA19))))
-
-.PHONY: tables save small large clean-mps matpower pglib orlib
-
-all: matpower pglib orlib tejada19
-
-matpower: $(SOLUTIONS_MATPOWER)
-
-pglib: $(SOLUTIONS_PGLIB)
-
-orlib: $(SOLUTIONS_ORLIB)
-
-tejada19: $(SOLUTIONS_TEJADA19)
-
-clean:
-	@rm -rf tables/benchmark* tables/compare* results
-    
-clean-mps:
-	@rm -fv results/*/*.mps.gz results/*/*/*.mps.gz
-
-clean-sol:
-	@rm -rf results/*/*.sol.* results/*/*/*.sol.*
-        
-save:
-	mkdir -p "runs/$(TIMESTAMP)"
-	rsync -avP results tables "runs/$(TIMESTAMP)/"
-
-results/%.sol.json: run.jl
-	@echo "run  $*"
-	@mkdir -p $(dir results/$*)
-	@$(JULIA) run.jl $* 2>&1 | cat > results/$*.log
-	@echo "run  $* [done]"
-
-tables:
-	@mkdir -p tables
-	@python scripts/table.py
-	#@python scripts/compare.py tables/reference.csv tables/benchmark.csv

benchmark/Manifest.toml

@@ -1,417 +0,0 @@
-# This file is machine-generated - editing it directly is not advised
-
-[[Base64]]
-uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
-
-[[BenchmarkTools]]
-deps = ["JSON", "Logging", "Printf", "Statistics", "UUIDs"]
-git-tree-sha1 = "9e62e66db34540a0c919d72172cc2f642ac71260"
-uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-version = "0.5.0"
-
-[[BinaryProvider]]
-deps = ["Libdl", "Logging", "SHA"]
-git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058"
-uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
-version = "0.5.10"
-
-[[Bzip2_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "3663bfffede2ef41358b6fc2e1d8a6d50b3c3904"
-uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
-version = "1.0.6+2"
-
-[[CEnum]]
-git-tree-sha1 = "1b77a77c3b28e0b3f413f7567c9bb8dd9bdccd14"
-uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
-version = "0.3.0"
-
-[[Calculus]]
-deps = ["LinearAlgebra"]
-git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad"
-uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
-version = "0.5.1"
-
-[[Cbc]]
-deps = ["BinaryProvider", "CEnum", "Cbc_jll", "Libdl", "MathOptInterface", "SparseArrays"]
-git-tree-sha1 = "72e4299de0995a60a6230079adc7e47580870815"
-uuid = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
-version = "0.7.0"
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benchmark/benchmark.jl

@@ -0,0 +1,158 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using Distributed
+using Pkg
+Pkg.activate(".")
+
+@everywhere using Pkg
+@everywhere Pkg.activate(".")
+
+@everywhere using UnitCommitment
+@everywhere using JuMP
+@everywhere using Gurobi
+@everywhere using JSON
+@everywhere using Logging
+@everywhere using Printf
+@everywhere using LinearAlgebra
+@everywhere using Random
+
+@everywhere import UnitCommitment:
+    ArrCon2000,
+    CarArr2006,
+    DamKucRajAta2016,
+    Formulation,
+    Gar1962,
+    KnuOstWat2018,
+    MorLatRam2013,
+    PanGua2016,
+    XavQiuWanThi2019
+
+@everywhere UnitCommitment._setup_logger()
+
+function main()
+    cases = [
+        "pglib-uc/ca/2014-09-01_reserves_0",
+        "pglib-uc/ca/2014-09-01_reserves_1",
+        "pglib-uc/ca/2015-03-01_reserves_0",
+        "pglib-uc/ca/2015-06-01_reserves_0",
+        "pglib-uc/ca/Scenario400_reserves_1",
+        "pglib-uc/ferc/2015-01-01_lw",
+        "pglib-uc/ferc/2015-05-01_lw",
+        "pglib-uc/ferc/2015-07-01_hw",
+        "pglib-uc/ferc/2015-10-01_lw",
+        "pglib-uc/ferc/2015-12-01_lw",
+        "pglib-uc/rts_gmlc/2020-04-03",
+        "pglib-uc/rts_gmlc/2020-09-20",
+        "pglib-uc/rts_gmlc/2020-10-27",
+        "pglib-uc/rts_gmlc/2020-11-25",
+        "pglib-uc/rts_gmlc/2020-12-23",
+        "or-lib/20_0_1_w",
+        "or-lib/20_0_5_w",
+        "or-lib/50_0_2_w",
+        "or-lib/75_0_2_w",
+        "or-lib/100_0_1_w",
+        "or-lib/100_0_4_w",
+        "or-lib/100_0_5_w",
+        "or-lib/200_0_3_w",
+        "or-lib/200_0_7_w",
+        "or-lib/200_0_9_w",
+        "tejada19/UC_24h_290g",
+        "tejada19/UC_24h_623g",
+        "tejada19/UC_24h_959g",
+        "tejada19/UC_24h_1577g",
+        "tejada19/UC_24h_1888g",
+        "tejada19/UC_168h_72g",
+        "tejada19/UC_168h_86g",
+        "tejada19/UC_168h_130g",
+        "tejada19/UC_168h_131g",
+        "tejada19/UC_168h_199g",
+    ]
+    formulations = Dict(
+        "Default" => Formulation(),
+        "ArrCon2000" => Formulation(ramping = ArrCon2000.Ramping()),
+        "CarArr2006" => Formulation(pwl_costs = CarArr2006.PwlCosts()),
+        "DamKucRajAta2016" =>
+            Formulation(ramping = DamKucRajAta2016.Ramping()),
+        "Gar1962" => Formulation(pwl_costs = Gar1962.PwlCosts()),
+        "KnuOstWat2018" =>
+            Formulation(pwl_costs = KnuOstWat2018.PwlCosts()),
+        "MorLatRam2013" => Formulation(ramping = MorLatRam2013.Ramping()),
+        "PanGua2016" => Formulation(ramping = PanGua2016.Ramping()),
+    )
+    trials = [i for i in 1:5]
+    combinations = [
+        (c, f.first, f.second, t) for c in cases for f in formulations for
+        t in trials
+    ]
+    shuffle!(combinations)
+    @sync @distributed for c in combinations
+        _run_combination(c...)
+    end
+end
+
+@everywhere function _run_combination(
+    case,
+    formulation_name,
+    formulation,
+    trial,
+)
+    name = "$formulation_name/$case"
+    dirname = "results/$name"
+    mkpath(dirname)
+    if isfile("$dirname/$trial.json")
+        @info @sprintf("%-4s %-16s %s", "skip", formulation_name, case)
+        return
+    end
+    @info @sprintf("%-4s %-16s %s", "run", formulation_name, case)
+    open("$dirname/$trial.log", "w") do file
+        redirect_stdout(file) do
+            redirect_stderr(file) do
+                return _run_sample(case, formulation, "$dirname/$trial")
+            end
+        end
+    end
+    @info @sprintf("%-4s %-16s %s", "done", formulation_name, case)
+end
+
+@everywhere function _run_sample(case, formulation, prefix)
+    total_time = @elapsed begin
+        @info "Reading: $case"
+        time_read = @elapsed begin
+            instance = UnitCommitment.read_benchmark(case)
+        end
+        @info @sprintf("Read problem in %.2f seconds", time_read)
+        BLAS.set_num_threads(4)
+        model = UnitCommitment.build_model(
+            instance = instance,
+            formulation = formulation,
+            optimizer = optimizer_with_attributes(
+                Gurobi.Optimizer,
+                "Threads" => 4,
+                "Seed" => rand(1:1000),
+            ),
+            variable_names = true,
+        )
+        @info "Optimizing..."
+        BLAS.set_num_threads(1)
+        UnitCommitment.optimize!(
+            model,
+            XavQiuWanThi2019.Method(time_limit = 3600.0, gap_limit = 1e-4),
+        )
+    end
+    @info @sprintf("Total time was %.2f seconds", total_time)
+    @info "Writing solution: $prefix.json"
+    solution = UnitCommitment.solution(model)
+    UnitCommitment.write("$prefix.json", solution)
+    @info "Verifying solution..."
+    return UnitCommitment.validate(instance, solution)
+    # @info "Exporting model..."
+    # return JuMP.write_to_file(model, model_filename)
+end
+
+if length(ARGS) > 0
+    _run_sample(ARGS[1], UnitCommitment.Formulation(), "tmp")
+else
+    main()
+end

benchmark/run.jl

@@ -1,61 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment
-using JuMP
-using Gurobi
-using JSON
-using Logging
-using Printf
-using LinearAlgebra
-
-function main()
-    basename, suffix = split(ARGS[1], ".")
-    solution_filename = "results/$basename.$suffix.sol.json"
-    model_filename = "results/$basename.$suffix.mps.gz"
-
-    time_limit = 60 * 20
-
-    BLAS.set_num_threads(4)
-    global_logger(TimeLogger(initial_time = time()))
-
-    total_time = @elapsed begin
-        @info "Reading: $basename"
-        time_read = @elapsed begin
-            instance = UnitCommitment.read_benchmark(basename)
-        end
-        @info @sprintf("Read problem in %.2f seconds", time_read)
-
-        time_model = @elapsed begin
-            model = build_model(instance=instance,
-                                optimizer=optimizer_with_attributes(Gurobi.Optimizer,
-                                                                    "Threads" => 4,
-                                                                    "Seed" => rand(1:1000),
-                                                                    ))
-        end
-
-        @info "Optimizing..."
-        BLAS.set_num_threads(1)
-        UnitCommitment.optimize!(model, time_limit=time_limit, gap_limit=1e-3)
-
-    end
-    @info @sprintf("Total time was %.2f seconds", total_time)
-
-    @info "Writing: $solution_filename"
-    solution = UnitCommitment.get_solution(model)
-    open(solution_filename, "w") do file
-        JSON.print(file, solution, 2)
-    end
-
-    @info "Verifying solution..."
-    UnitCommitment.validate(instance, solution) 
-
-    @info "Setting variable names..."
-    UnitCommitment.set_variable_names!(model)
-
-    @info "Exporting model..."
-    JuMP.write_to_file(model.mip, model_filename)
-end
-
-main()

benchmark/scripts/compare.py

@@ -5,60 +5,82 @@
 import pandas as pd
 import numpy as np
 import seaborn as sns
+import matplotlib
 import matplotlib.pyplot as plt
 import sys
 
-#easy_cutoff = 120
+matplotlib.use("Agg")
+sns.set("talk")
+sns.set_palette(
+    [
+        "#9b59b6",
+        "#3498db",
+        "#95a5a6",
+        "#e74c3c",
+        "#34495e",
+        "#2ecc71",
+    ]
+)
 
-b1 = pd.read_csv(sys.argv[1], index_col=0)
-b2 = pd.read_csv(sys.argv[2], index_col=0)
+filename = sys.argv[1]
+m1 = sys.argv[2]
+m2 = sys.argv[3]
 
-c1 = b1.groupby(["Group", "Instance", "Sample"])[["Optimization time (s)", "Primal bound"]].mean()
-c2 = b2.groupby(["Group", "Instance", "Sample"])[["Optimization time (s)", "Primal bound"]].mean()
-c1.columns = ["A Time (s)", "A Value"]
-c2.columns = ["B Time (s)", "B Value"]
+# Prepare data
+data = pd.read_csv(filename, index_col=0)
+b1 = (
+    data[data["Group"] == m1]
+    .groupby(["Instance", "Sample"])
+    .mean()[["Optimization time (s)"]]
+)
+b2 = (
+    data[data["Group"] == m2]
+    .groupby(["Instance", "Sample"])
+    .mean()[["Optimization time (s)"]]
+)
+b1.columns = [f"{m1} time (s)"]
+b2.columns = [f"{m2} time (s)"]
+merged = pd.merge(b1, b2, left_index=True, right_index=True).reset_index().dropna()
+merged["Speedup"] = merged[f"{m1} time (s)"] / merged[f"{m2} time (s)"]
+merged["Group"] = merged["Instance"].str.replace(r"\/.*", "", regex=True)
+merged = merged.sort_values(by=["Instance", "Sample"], ascending=True)
+merged = merged[(merged[f"{m1} time (s)"] > 0) & (merged[f"{m2} time (s)"] > 0)]
 
-merged = pd.concat([c1, c2], axis=1)
-merged["Speedup"] = merged["A Time (s)"] / merged["B Time (s)"]
-merged["Time diff (s)"] = merged["B Time (s)"] - merged["A Time (s)"]
-merged["Value diff (%)"] = np.round((merged["B Value"] - merged["A Value"]) / merged["A Value"] * 100.0, 5)
-merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Speedup"] = float("nan")
-merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Time diff (s)"] = float("nan")
-#merged = merged[(merged["A Time (s)"] >= easy_cutoff) | (merged["B Time (s)"] >= easy_cutoff)]
-merged.reset_index(inplace=True)
-merged["Name"] = merged["Group"] + "/" + merged["Instance"]
-#merged = merged.sort_values(by="Speedup", ascending=False)
-
-
-k = len(merged.groupby("Name"))
-plt.figure(figsize=(12, 0.50 * k))
-plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = True
-plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
-sns.set_style("whitegrid")
-sns.set_palette("Set1")
-sns.barplot(data=merged,
-            x="Speedup",
-            y="Name",
-            color="tab:red",
-            capsize=0.15,
-            errcolor="k",
-            errwidth=1.25)
-plt.axvline(1.0, linestyle="--", color="k")
-plt.tight_layout()
+# Plot results
+k1 = len(merged.groupby("Instance").mean())
+k2 = len(merged.groupby("Group").mean())
+k = k1 + k2
+fig = plt.figure(
+    constrained_layout=True,
+    figsize=(15, max(5, 0.75 * k)),
+)
+plt.suptitle(f"{m1} vs {m2}")
+gs1 = fig.add_gridspec(nrows=k, ncols=1)
+ax1 = fig.add_subplot(gs1[0:k1, 0:1])
+ax2 = fig.add_subplot(gs1[k1:, 0:1], sharex=ax1)
+sns.barplot(
+    data=merged,
+    x="Speedup",
+    y="Instance",
+    color="tab:purple",
+    errcolor="k",
+    errwidth=1.25,
+    ax=ax1,
+)
+sns.barplot(
+    data=merged,
+    x="Speedup",
+    y="Group",
+    color="tab:purple",
+    errcolor="k",
+    errwidth=1.25,
+    ax=ax2,
+)
+ax1.axvline(1.0, linestyle="--", color="k")
+ax2.axvline(1.0, linestyle="--", color="k")
 
 print("Writing tables/compare.png")
 plt.savefig("tables/compare.png", dpi=150)
 
 print("Writing tables/compare.csv")
-merged.loc[:, ["Group",
-               "Instance",
-               "Sample",
-               "A Time (s)",
-               "B Time (s)",
-               "Speedup",
-               "Time diff (s)",
-               "A Value",
-               "B Value",
-               "Value diff (%)",
-              ]
-          ].to_csv("tables/compare.csv", index_label="Index")
+merged.to_csv("tables/compare.csv", index_label="Index")

benchmark/scripts/table.py

@@ -6,11 +6,13 @@ from pathlib import Path
 import pandas as pd
 import re
 from tabulate import tabulate
+from colorama import init, Fore, Back, Style
+
+init()
 
 
 def process_all_log_files():
-    pathlist = list(Path(".").glob('results/*/*/*.log'))
-    pathlist += list(Path(".").glob('results/*/*.log'))
+    pathlist = list(Path(".").glob("results/**/*.log"))
     rows = []
     for path in pathlist:
         if ".ipy" in str(path):
@@ -22,13 +24,13 @@ def process_all_log_files():
     df.index = range(len(df))
     print("Writing tables/benchmark.csv")
     df.to_csv("tables/benchmark.csv", index_label="Index")
-        
-        
+
+
 def process(filename):
     parts = filename.replace(".log", "").split("/")
-    group_name = "/".join(parts[1:-1])
-    instance_name = parts[-1]
-    instance_name, sample_name = instance_name.split(".")
+    group_name = parts[1]
+    instance_name = "/".join(parts[2:-1])
+    sample_name = parts[-1]
     nodes = 0.0
     optimize_time = 0.0
     simplex_iterations = 0.0
@@ -45,56 +47,75 @@ def process(filename):
     read_time, model_time, isf_time, total_time = None, None, None, None
     cb_calls, cb_time = 0, 0.0
     transmission_count, transmission_time, transmission_calls = 0, 0.0, 0
-    
+
     # m = re.search("case([0-9]*)", instance_name)
     # n_buses = int(m.group(1))
     n_buses = 0
-    
+    validation_errors = 0
+
     with open(filename) as file:
         for line in file.readlines():
-            m = re.search(r"Explored ([0-9.e+]*) nodes \(([0-9.e+]*) simplex iterations\) in ([0-9.e+]*) seconds", line)
+            m = re.search(
+                r"Explored ([0-9.e+]*) nodes \(([0-9.e+]*) simplex iterations\) in ([0-9.e+]*) seconds",
+                line,
+            )
             if m is not None:
                 nodes += int(m.group(1))
                 simplex_iterations += int(m.group(2))
                 optimize_time += float(m.group(3))
-                
-            m = re.search(r"Best objective ([0-9.e+]*), best bound ([0-9.e+]*), gap ([0-9.e+]*)\%", line)
+
+            m = re.search(
+                r"Best objective ([0-9.e+]*), best bound ([0-9.e+]*), gap ([0-9.e+]*)\%",
+                line,
+            )
             if m is not None:
                 primal_bound = float(m.group(1))
                 dual_bound = float(m.group(2))
                 gap = round(float(m.group(3)), 3)
-                
-            m = re.search(r"Root relaxation: objective ([0-9.e+]*), ([0-9.e+]*) iterations, ([0-9.e+]*) seconds", line)
+
+            m = re.search(
+                r"Root relaxation: objective ([0-9.e+]*), ([0-9.e+]*) iterations, ([0-9.e+]*) seconds",
+                line,
+            )
             if m is not None:
                 root_obj = float(m.group(1))
                 root_iterations += int(m.group(2))
                 root_time += float(m.group(3))
-                
-            m = re.search(r"Presolved: ([0-9.e+]*) rows, ([0-9.e+]*) columns, ([0-9.e+]*) nonzeros", line)
+
+            m = re.search(
+                r"Presolved: ([0-9.e+]*) rows, ([0-9.e+]*) columns, ([0-9.e+]*) nonzeros",
+                line,
+            )
             if m is not None:
                 n_rows_presolved = int(m.group(1))
                 n_cols_presolved = int(m.group(2))
                 n_nz_presolved = int(m.group(3))
-                
-            m = re.search(r"Optimize a model with ([0-9.e+]*) rows, ([0-9.e+]*) columns and ([0-9.e+]*) nonzeros", line)
+
+            m = re.search(
+                r"Optimize a model with ([0-9.e+]*) rows, ([0-9.e+]*) columns and ([0-9.e+]*) nonzeros",
+                line,
+            )
             if m is not None:
                 n_rows_orig = int(m.group(1))
                 n_cols_orig = int(m.group(2))
                 n_nz_orig = int(m.group(3))
-                
-            m = re.search(r"Variable types: ([0-9.e+]*) continuous, ([0-9.e+]*) integer \(([0-9.e+]*) binary\)", line)
+
+            m = re.search(
+                r"Variable types: ([0-9.e+]*) continuous, ([0-9.e+]*) integer \(([0-9.e+]*) binary\)",
+                line,
+            )
             if m is not None:
                 n_cont_vars_presolved = int(m.group(1))
-                n_bin_vars_presolved = int(m.group(3))  
-            
+                n_bin_vars_presolved = int(m.group(3))
+
             m = re.search(r"Read problem in ([0-9.e+]*) seconds", line)
             if m is not None:
                 read_time = float(m.group(1))
-            
+
             m = re.search(r"Computed ISF in ([0-9.e+]*) seconds", line)
             if m is not None:
                 isf_time = float(m.group(1))
-            
+
             m = re.search(r"Built model in ([0-9.e+]*) seconds", line)
             if m is not None:
                 model_time = float(m.group(1))
@@ -103,7 +124,10 @@ def process(filename):
             if m is not None:
                 total_time = float(m.group(1))
 
-            m = re.search(r"User-callback calls ([0-9.e+]*), time in user-callback ([0-9.e+]*) sec", line)
+            m = re.search(
+                r"User-callback calls ([0-9.e+]*), time in user-callback ([0-9.e+]*) sec",
+                line,
+            )
             if m is not None:
                 cb_calls = int(m.group(1))
                 cb_time = float(m.group(2))
@@ -116,7 +140,15 @@ def process(filename):
             m = re.search(r".*MW overflow", line)
             if m is not None:
                 transmission_count += 1
-                
+
+            m = re.search(r".*Found ([0-9]*) validation errors", line)
+            if m is not None:
+                validation_errors += int(m.group(1))
+                print(
+                    f"{Fore.YELLOW}{Style.BRIGHT}Warning:{Style.RESET_ALL} {validation_errors:8d} "
+                    f"{Style.DIM}validation errors in {Style.RESET_ALL}{group_name}/{instance_name}/{sample_name}"
+                )
+
     return {
         "Group": group_name,
         "Instance": instance_name,
@@ -148,36 +180,51 @@ def process(filename):
         "Transmission screening constraints": transmission_count,
         "Transmission screening time": transmission_time,
         "Transmission screening calls": transmission_calls,
+        "Validation errors": validation_errors,
     }
 
+
 def generate_chart():
     import pandas as pd
+    import matplotlib
     import matplotlib.pyplot as plt
     import seaborn as sns
 
+    matplotlib.use("Agg")
+    sns.set("talk")
+    sns.set_palette(
+        [
+            "#9b59b6",
+            "#3498db",
+            "#95a5a6",
+            "#e74c3c",
+            "#34495e",
+            "#2ecc71",
+        ]
+    )
+
     tables = []
     files = ["tables/benchmark.csv"]
     for f in files:
         table = pd.read_csv(f, index_col=0)
-        table.loc[:, "Instance"] = table.loc[:,"Group"] + "/" + table.loc[:,"Instance"]
         table.loc[:, "Filename"] = f
         tables += [table]
     benchmark = pd.concat(tables, sort=True)
-    benchmark = benchmark.sort_values(by="Instance")
-    k = len(benchmark.groupby("Instance"))
-    plt.figure(figsize=(12, 0.50 * k))
-    sns.set_style("whitegrid")
-    sns.set_palette("Set1")
-    sns.barplot(y="Instance",
-                x="Total time (s)",
-                color="tab:red",
-                capsize=0.15,
-                errcolor="k",
-                errwidth=1.25,
-                data=benchmark);
+    benchmark = benchmark.sort_values(by=["Group", "Instance"])
+    k1 = len(benchmark.groupby("Instance"))
+    k2 = len(benchmark.groupby("Group"))
+    plt.figure(figsize=(12, 0.25 * k1 * k2))
+    sns.barplot(
+        y="Instance",
+        x="Total time (s)",
+        hue="Group",
+        errcolor="k",
+        errwidth=1.25,
+        data=benchmark,
+    )
     plt.tight_layout()
     print("Writing tables/benchmark.png")
-    plt.savefig("tables/benchmark.png", dpi=150);
+    plt.savefig("tables/benchmark.png", dpi=150)
 
 
 if __name__ == "__main__":

docs/Makefile

@@ -0,0 +1,14 @@
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/_static/custom.css

@@ -0,0 +1,49 @@
+h1.site-logo {
+    font-size: 30px !important;
+}
+
+h1.site-logo small {
+    font-size: 20px !important;
+}
+
+h1.site-logo {
+    font-size: 30px !important;
+}
+
+h1.site-logo small {
+    font-size: 20px !important;
+}
+
+tbody, thead, pre {
+    border: 1px solid rgba(0, 0, 0, 0.25);
+}
+
+table td, th {
+    padding: 8px;
+}
+
+table p {
+    margin-bottom: 0;
+}
+
+table td code {
+    white-space: nowrap;
+}
+
+table tr,
+table th {
+    border-bottom: 1px solid rgba(0, 0, 0, 0.1);
+}
+
+table tr:last-child {
+    border-bottom: 0;
+}
+
+pre {
+    box-shadow: inherit !important;
+    background-color: #fff;
+}
+
+.text-align\:center {
+    text-align: center;
+}

docs/conf.py

@@ -0,0 +1,16 @@
+project = "UnitCommitment.jl"
+copyright = "2020-2021, UChicago Argonne, LLC"
+author = ""
+release = "0.2"
+extensions = ["myst_parser"]
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+html_theme = "sphinx_book_theme"
+html_static_path = ["_static"]
+html_css_files = ["custom.css"]
+html_theme_options = {
+    "repository_url": "https://github.com/ANL-CEEESA/UnitCommitment.jl/",
+    "use_repository_button": True,
+    "extra_navbar": "",
+}
+html_title = f"UnitCommitment.jl<br/><small>{release}</small>"

docs/format.md

@@ -1,7 +1,18 @@
+```{sectnum}
+---
+start: 2
+depth: 2
+suffix: .
+---
+```
+
+
 Data Format
 ===========
 
-## 1. Input Data Format
+
+Input Data Format
+-----------------
 
 Instances are specified by JSON files containing the following main sections:
 
@@ -15,27 +26,30 @@ Instances are specified by JSON files containing the following main sections:
 
 Each section is described in detail below. For a complete example, see [case14](https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances/matpower/case14).
 
-### 1.1 Parameters
+### Parameters
 
-This section describes system-wide parameters, such as power balance penalties, and optimization parameters, such as the length of the planning horizon.
+This section describes system-wide parameters, such as power balance and reserve shortfall penalties, and optimization parameters, such as the length of the planning horizon and the time.
 
 | Key                            | Description                                       | Default  | Time series?
 | :----------------------------- | :------------------------------------------------ | :------: | :------------:
-| `Time (h)`                     | Length of the planning horizon (in hours) | Required | N
-| `Power balance penalty ($/MW)` | Penalty for system-wide shortage or surplus in production (in $/MW). This is charged per time period. For example, if there is a shortage of 1 MW for three time periods, three times this amount will be charged. | `1000.0` | Y
+| `Time horizon (h)` | Length of the planning horizon (in hours). | Required | N
+| `Time step (min)` | Length of each time step (in minutes). Must be a divisor of 60 (e.g. 60, 30, 20, 15, etc). | `60` | N
+| `Power balance penalty ($/MW)` | Penalty for system-wide shortage or surplus in production (in $/MW). This is charged per time step. For example, if there is a shortage of 1 MW for three time steps, three times this amount will be charged. | `1000.0` | Y
+| `Reserve shortfall penalty ($/MW)` | Penalty for system-wide shortage in meeting reserve requirements (in $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
 
 
 #### Example
 ```json
 {
     "Parameters": {
-        "Time (h)": 4,
-        "Power balance penalty ($/MW)": 1000.0
+        "Time horizon (h)": 4,
+        "Power balance penalty ($/MW)": 1000.0,
+        "Reserve shortfall penalty ($/MW)": -1.0
     }
 }
 ```
 
-### 1.2 Buses
+### Buses
 
 This section describes the characteristics of each bus in the system. 
 
@@ -64,40 +78,40 @@ This section describes the characteristics of each bus in the system.
 ```
 
 
-### 1.3 Generators
+### Generators
 
 This section describes all generators in the system, including thermal units, renewable units and virtual units.
 
 | Key                       | Description                                      | Default | Time series?
 | :------------------------ | :------------------------------------------------| ------- | :-----------:
-| `Bus`                     | Identifier of the bus where this generator is located (string) | Required | N
+| `Bus`                     | Identifier of the bus where this generator is located (string). | Required | N
 | `Production cost curve (MW)` and `Production cost curve ($)` | Parameters describing the piecewise-linear production costs. See below for more details. | Required | Y
-| `Startup costs ($)` and `Startup delays (h)` | Parameters describing how much it costs to start the generator after it has been shut down for a certain amount of time. If `Startup costs ($)` and `Startup delays (h)` are set to `[300.0, 400.0]` and `[1, 4]`, for example, and the generator is shut down at time `t`, then it costs 300 to start up the generator at times `t+1`, `t+2` or `t+3`, and 400 to start the generator at time `t+4` or any time after that.  The number of startup cost points is unlimited, and may be different for each generator. Startup delays must be strictly increasing. | `[0.0]` and `[1]` | N
-| `Minimum uptime (h)`      | Minimum amount of time the generator must stay operational after starting up (in hours). For example, if the generator starts up at time 1 and `Minimum uptime (h)` is set to 4, then the generator can only shut down at time 5. | `1` | N
-| `Minimum downtime (h)`    | Minimum amount of time the generator must stay offline after shutting down (in hours). For example, if the generator shuts down at time 1 and `Minimum downtime (h)` is set to 4, then the generator can only start producing power again at time 5. | `1` | N
-| `Ramp up limit (MW)`      | Maximum increase in production from one time period to the next (in MW). For example, if the generator is producing 100 MW at time 1 and if this parameter is set to 40 MW, then the generator will produce at most 140 MW at time 2. | `+inf` | N
-| `Ramp down limit (MW)`    | Maximum decrease in production from one time period to the next (in MW). For example, if the generator is producing 100 MW at time 1 and this parameter is set to 40 MW, then the generator will produce at least 60 MW at time 2. | `+inf` | N
-| `Startup limit (MW)`   | Maximum amount of power a generator can produce immediately after starting up (in MW). | `+inf` | N
-| `Shutdown limit (MW)`     | Maximum amount of power a generator can produce immediately before shutting down (in MW). Specifically, the generator can only shut down at time `t+1` if its production at time `t` is below this limit.  | `+inf` | N
-| `Initial status (h)`  | If set to a positive number, indicates the amount of time the generator has been on at the beginning of the simulation, and if set to a negative number, the amount of time the generator has been off. For example, if `Initial status (h)` is `-2`, this means that the generator was off at simulation time `-2` and `-1`. The simulation starts at time `0`. | Required | N
-| `Initial power (MW)`  | Amount of power the generator at time period `-1`, immediately before the planning horizon starts. | Required | N
-| `Must run?`               | If `true`, the generator should be committed, even that is not economical (Boolean). | `false` | Y
+| `Startup costs ($)` and `Startup delays (h)` | Parameters describing how much it costs to start the generator after it has been shut down for a certain amount of time. If `Startup costs ($)` and `Startup delays (h)` are set to `[300.0, 400.0]` and `[1, 4]`, for example, and the generator is shut down at time `00:00` (h:min), then it costs \$300 to start up the generator at any time between `01:00` and `03:59`, and \$400 to start the generator at time `04:00` or any time after that.  The number of startup cost points is unlimited, and may be different for each generator. Startup delays must be strictly increasing and the first entry must equal `Minimum downtime (h)`. | `[0.0]` and `[1]` | N
+| `Minimum uptime (h)`      | Minimum amount of time the generator must stay operational after starting up (in hours). For example, if the generator starts up at time `00:00` (h:min) and `Minimum uptime (h)` is set to 4, then the generator can only shut down at time `04:00`. | `1` | N
+| `Minimum downtime (h)`    | Minimum amount of time the generator must stay offline after shutting down (in hours). For example, if the generator shuts down at time `00:00` (h:min) and `Minimum downtime (h)` is set to 4, then the generator can only start producing power again at time `04:00`. | `1` | N
+| `Ramp up limit (MW)`      | Maximum increase in production from one time step to the next (in MW). For example, if the generator is producing 100 MW at time step 1 and if this parameter is set to 40 MW, then the generator will produce at most 140 MW at time step 2. | `+inf` | N
+| `Ramp down limit (MW)`    | Maximum decrease in production from one time step to the next (in MW). For example, if the generator is producing 100 MW at time step 1 and this parameter is set to 40 MW, then the generator will produce at least 60 MW at time step 2. | `+inf` | N
+| `Startup limit (MW)`   | Maximum amount of power a generator can produce immediately after starting up (in MW). For example, if `Startup limit (MW)` is set to 100 MW and the unit is off at time step 1, then it may produce at most 100 MW at time step 2.| `+inf` | N
+| `Shutdown limit (MW)`     | Maximum amount of power a generator can produce immediately before shutting down (in MW). Specifically, the generator can only shut down at time step `t+1` if its production at time step `t` is below this limit.  | `+inf` | N
+| `Initial status (h)`  | If set to a positive number, indicates the amount of time (in hours) the generator has been on at the beginning of the simulation, and if set to a negative number, the amount of time the generator has been off. For example, if `Initial status (h)` is `-2`, this means that the generator was off since `-02:00` (h:min). The simulation starts at time `00:00`. If `Initial status (h)` is `3`, this means that the generator was on since `-03:00`. A value of zero is not acceptable. | Required | N
+| `Initial power (MW)`  | Amount of power the generator at time step `-1`, immediately before the planning horizon starts. | Required | N
+| `Must run?`               | If `true`, the generator should be committed, even if that is not economical (Boolean). | `false` | Y
 | `Provides spinning reserves?`    | If `true`, this generator may provide spinning reserves (Boolean). | `true` | Y
 
 #### Production costs and limits
 
-Production costs are represented as piecewise-linear curves. Figure 1 shows an example cost curve with three segments, where it costs 1400, 1600, 2200 and 2400 dollars to generate, respectively, 100, 110, 130 and 135 MW of power. To model this generator, `Production cost curve (MW)` should be set to `[100, 110, 130, 135]`, and `Production cost curve ($)`  should be set to `[1400, 1600, 2200, 2400]`.
+Production costs are represented as piecewise-linear curves. Figure 1 shows an example cost curve with three segments, where it costs \$1400, \$1600, \$2200 and \$2400 to generate, respectively, 100, 110, 130 and 135 MW of power. To model this generator, `Production cost curve (MW)` should be set to `[100, 110, 130, 135]`, and `Production cost curve ($)`  should be set to `[1400, 1600, 2200, 2400]`.
 Note that this curve also specifies the production limits. Specifically, the first point identifies the minimum power output when the unit is operational, while the last point identifies the maximum power output.
 
 <center>
-    <img src="../images/cost_curve.png" style="max-width: 500px"/>
+    <img src="../_static/cost_curve.png" style="max-width: 500px"/>
     <div><b>Figure 1.</b> Piecewise-linear production cost curve.</div>
     <br/>
 </center>
 
 #### Additional remarks:
 
-* For time-dependent production limits or time-dependent production costs, the usage of nested arrays is allowed. For example,  if `Production cost curve (MW)` is set to `[5.0, [10.0, 12.0, 15.0, 20.0]]`, then the unit may generate at most 10, 12, 15 and 20 MW of power during time periods 1, 2, 3 and 4, respectively. The minimum output for all time periods is fixed to at 5 MW.
+* For time-dependent production limits or time-dependent production costs, the usage of nested arrays is allowed. For example,  if `Production cost curve (MW)` is set to `[5.0, [10.0, 12.0, 15.0, 20.0]]`, then the unit may generate at most 10, 12, 15 and 20 MW of power during time steps 1, 2, 3 and 4, respectively. The minimum output for all time periods is fixed to at 5 MW.
 * There is no limit to the number of piecewise-linear segments, and different generators may have a different number of segments.
 * If `Production cost curve (MW)` and `Production cost curve ($)` both contain a single element, then the generator must produce exactly that amount of power when operational. To specify that the generator may produce any amount of power up to a certain limit `P`, the parameter `Production cost curve (MW)` should be set to `[0, P]`. 
 * Production cost curves must be convex.
@@ -133,7 +147,7 @@ Note that this curve also specifies the production limits. Specifically, the fir
 }
 ```
 
-### 1.4 Price-sensitive loads
+### Price-sensitive loads
 
 This section describes components in the system which may increase or reduce their energy consumption according to the energy prices. Fixed loads (as described in the `buses` section) are always served, regardless of the price, unless there is significant congestion in the system or insufficient production capacity. Price-sensitive loads, on the other hand, are only served if it is economical to do so. 
 
@@ -157,7 +171,7 @@ This section describes components in the system which may increase or reduce the
 }
 ```
 
-### 1.5 Transmission Lines
+### Transmission Lines
 
 This section describes the characteristics of transmission system, such as its topology and the susceptance of each transmission line.
 
@@ -167,9 +181,9 @@ This section describes the characteristics of transmission system, such as its t
 | `Target bus`           | Identifier of the bus where the transmission line reaches. | Required | N
 | `Reactance (ohms)`     | Reactance of the transmission line (in ohms). | Required | N
 | `Susceptance (S)`      | Susceptance  of the transmission line (in siemens). | Required | N
-| `Normal flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in its regular, fully-operational state. May be `null` is there is no limit. | `+inf` | Y
+| `Normal flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in its regular, fully-operational state. | `+inf` | Y
 | `Emergency flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in degraded state (for example, after the failure of another transmission line). | `+inf` | Y
-| `Flow limit penalty ($/MW)` | Penalty for violating the flow limits of the transmission line (in $/MW). This is charged per time period. For example, if there is a thermal violation of 1 MW for three time periods, three times this amount will be charged. | `5000.0` | Y
+| `Flow limit penalty ($/MW)` | Penalty for violating the flow limits of the transmission line (in $/MW). This is charged per time step. For example, if there is a thermal violation of 1 MW for three time steps, then three times this amount will be charged. | `5000.0` | Y
 
 #### Example
 
@@ -190,7 +204,7 @@ This section describes the characteristics of transmission system, such as its t
 ```
 
 
-### 1.6 Reserves
+### Reserves
 
 This section describes the hourly amount of operating reserves required.
 
@@ -214,7 +228,7 @@ This section describes the hourly amount of operating reserves required.
 }
 ```
 
-### 1.7 Contingencies
+### Contingencies
 
 This section describes credible contingency scenarios in the optimization, such as the loss of a transmission line or generator.
 
@@ -239,11 +253,11 @@ This section describes credible contingency scenarios in the optimization, such
 }
 ```
 
-### 1.8 Additional remarks
+### Additional remarks
 
 #### Time series parameters
 
-Many numerical properties in the JSON file can be specified either as a single floating point number if they are time-independent, or as an array containing exactly `T` elements, where `T` is the length of the planning horizon, if they are time-dependent. For example, both formats below are valid when `T=3`:
+Many numerical properties in the JSON file can be specified either as a single floating point number if they are time-independent, or as an array containing exactly `T` elements, if they are time-dependent, where `T` is the number of time steps in the planning horizon. For example, both formats below are valid when `T=3`:
 
 ```json
 {
@@ -252,13 +266,29 @@ Many numerical properties in the JSON file can be specified either as a single f
 }
 ```
 
-#### Current limitations
+The value `T` depends on both `Time horizon (h)` and `Time step (min)`, as the table below illustrates.
 
-* All reserves are system-wide (no zonal reserves)
+Time horizon (h) | Time step (min) | T
+:---------------:|:---------------:|:----:
+24               | 60              | 24
+24               | 15              | 96
+24               | 5               | 288
+36               | 60              | 36
+36               | 15              | 144
+36               | 5               | 432
+
+Output Data Format
+------------------
+
+The output data format is also JSON-based, but it is not currently documented since we expect it to change significantly in a future version of the package.
+
+
+Current limitations
+-------------------
+
+* All reserves are system-wide. Zonal reserves are not currently supported.
 * Network topology remains the same for all time periods
-* Only N-1 transmission contingencies are supported. Generator contingencies are not supported.
+* Only N-1 transmission contingencies are supported. Generator contingencies are not currently supported.
 * Time-varying minimum production amounts are not currently compatible with ramp/startup/shutdown limits.
 
-## 2. Output Data Format
 
-The output data format is also JSON-based, but it is not currently documented since we expect it to change significantly in a future version of the package.

docs/index.md

@@ -0,0 +1,82 @@
+# UnitCommitment.jl
+
+**UnitCommitment.jl** (UC.jl) is a Julia/JuMP optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and Julia/JuMP implementations of state-of-the-art mixed-integer programming formulations.
+
+## Package Components
+
+* **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
+* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature, converted into a common data format, and extended using data-driven methods to make them more challenging and realistic.
+* **Model Implementation**: The package provides a Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
+* **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
+
+[ArrCon2000]: https://doi.org/10.1109/59.871739
+[CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
+[DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
+[Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
+[KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
+[MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
+[PanGua2016]: https://doi.org/10.1287/opre.2016.1520
+[XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
+
+### Authors
+* **Alinson S. Xavier** (Argonne National Laboratory)
+* **Aleksandr M. Kazachkov** (University of Florida)
+* **Feng Qiu** (Argonne National Laboratory)
+
+### Acknowledgments
+
+* We would like to thank **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
+
+* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357
+
+* Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.
+
+### Citing
+
+If you use UnitCommitment.jl in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
+
+* **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu**, "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
+
+If you use the instances, we additionally request that you cite the original sources, as described in the [instances page](instances.md).
+
+### License
+
+```text
+UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment
+Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted
+provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of
+   conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of
+   conditions and the following disclaimer in the documentation and/or other materials provided
+   with the distribution.
+3. Neither the name of the copyright holder nor the names of its contributors may be used to
+   endorse or promote products derived from this software without specific prior written
+   permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
+AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+```
+
+## Site contents
+
+```{toctree}
+---
+maxdepth: 2
+---
+usage.md
+format.md
+instances.md
+model.md
+```
+

docs/instances.md

@@ -1,4 +1,13 @@
-# Instances
+```{sectnum}
+---
+start: 3
+depth: 2
+suffix: .
+---
+```
+
+Instances
+=========
 
 UnitCommitment.jl provides a large collection of benchmark instances collected
 from the literature and converted to a [common data format](format.md). In some cases, as indicated below, the original instances have been extended, with realistic parameters, using data-driven methods. 
@@ -7,7 +16,9 @@ If you use these instances in your research, we request that you cite UnitCommit
 Raw instances files are [available at our GitHub repository](https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances). Benchmark instances can also be loaded with
 `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md).
 
-## 1. MATPOWER
+
+MATPOWER
+--------
 
 [MATPOWER](https://github.com/MATPOWER/matpower) is an open-source package for solving power flow problems in MATLAB and Octave. It contains a number of power flow test cases, which have been widely used in the power systems literature.
 
@@ -25,7 +36,7 @@ Because most MATPOWER test cases were originally designed for power flow studies
 
 For each MATPOWER test case, UC.jl provides two variations (`2017-02-01` and `2017-08-01`) corresponding respectively to a winter and to a summer test case.
 
-### 1.1 MATPOWER/UW-PSTCA
+### MATPOWER/UW-PSTCA
 
 A variety of smaller IEEE test cases, [compiled by University of Washington](http://labs.ece.uw.edu/pstca/), corresponding mostly to small portions of the American Electric Power System in the 1960s.
 
@@ -43,7 +54,7 @@ A variety of smaller IEEE test cases, [compiled by University of Washington](htt
 | `matpower/case300/2017-08-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
 
 
-### 1.2 MATPOWER/Polish
+### MATPOWER/Polish
 
 Test cases based on the Polish 400, 220 and 110 kV networks, originally provided by **Roman Korab** (Politechnika Śląska) and corrected by the MATPOWER team.
 
@@ -66,7 +77,7 @@ Test cases based on the Polish 400, 220 and 110 kV networks, originally provided
 | `matpower/case3375wp/2017-02-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
 | `matpower/case3375wp/2017-08-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
 
-### 1.3 MATPOWER/PEGASE
+### MATPOWER/PEGASE
 
 Test cases from the [Pan European Grid Advanced Simulation and State Estimation (PEGASE) project](https://cordis.europa.eu/project/id/211407), describing part of the European high voltage transmission network.
 
@@ -83,7 +94,7 @@ Test cases from the [Pan European Grid Advanced Simulation and State Estimation
 | `matpower/case13659pegase/2017-02-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
 | `matpower/case13659pegase/2017-08-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
 
-### 1.4 MATPOWER/RTE
+### MATPOWER/RTE
 
 Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://www.rte-france.com) representing the size and complexity of the French very high voltage transmission network.
 
@@ -107,11 +118,12 @@ Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://
 | `matpower/case6515rte/2017-08-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
 
 
-## 2. PGLIB-UC Instances
+PGLIB-UC Instances
+------------------
 
 [PGLIB-UC](https://github.com/power-grid-lib/pglib-uc) is a benchmark library curated and maintained by the [IEEE PES Task Force on Benchmarks for Validation of Emerging Power System Algorithms](https://power-grid-lib.github.io/). These test cases have been used in [KnOsWa20].
 
-### 2.1 PGLIB-UC/California
+### PGLIB-UC/California
 
 Test cases based on publicly available data from the California ISO. For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).
 
@@ -139,7 +151,7 @@ Test cases based on publicly available data from the California ISO. For more de
 | `pglib-uc/ca/Scenario400_reserves_5` | 1 | 611 | 0 | 0 | [KnOsWa20]
 
 
-### 2.2 PGLIB-UC/FERC
+### PGLIB-UC/FERC
 
 Test cases based on a publicly available [unit commitment test case produced by the Federal Energy Regulatory Commission](https://www.ferc.gov/industries-data/electric/power-sales-and-markets/increasing-efficiency-through-improved-software-1). For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).
 
@@ -171,7 +183,7 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `pglib-uc/ferc/2015-12-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
 
 
-### 2.3 PGLIB-UC/RTS-GMLC
+### PGLIB-UC/RTS-GMLC
 
 [RTS-GMLC](https://github.com/GridMod/RTS-GMLC) is an updated version of the RTS-96 test system produced by the United States Department of Energy's [Grid Modernization Laboratory Consortium](https://gmlc.doe.gov/). The PGLIB-UC/RTS-GMLC instances are modified versions of the original RTS-GMLC instances, with modified ramp-rates and without a transmission network. For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).
 
@@ -190,7 +202,9 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `pglib-uc/rts_gmlc/2020-11-25` | 1 | 154 | 0 | 0 | [BaBlEh19]
 | `pglib-uc/rts_gmlc/2020-12-23` | 1 | 154 | 0 | 0 | [BaBlEh19]
 
-## 3. OR-LIB/UC
+
+OR-LIB/UC
+---------
 
 [OR-LIB](http://people.brunel.ac.uk/~mastjjb/jeb/info.html) is a collection of test data sets for a variety of operations research problems, including unit commitment. The UC instances in OR-LIB are synthetic instances generated by a [random problem generator](http://groups.di.unipi.it/optimize/Data/UC.html) developed by the [Operations Research Group at University of Pisa](http://groups.di.unipi.it/optimize/). These test cases have been used in [FrGe06] and many other publications.
 
@@ -239,7 +253,9 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `or-lib/200_0_8_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
 | `or-lib/200_0_9_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
 
-## 4. Tejada19
+
+Tejada19
+--------
 
  Test cases used in [TeLuSa19]. These instances are similar to OR-LIB/UC, in the sense that they use the same random problem generator, but are much larger.
 
@@ -295,9 +311,11 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `tejada19/UC_168h_192g` | 168 | 1 | 192 | 0 | 0 | [TeLuSa19]
 | `tejada19/UC_168h_199g` | 168 | 1 | 199 | 0 | 0 | [TeLuSa19]
 
-## 5. References
 
-* [UCJL] **Alinson S. Xavier, Feng Qiu.** "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874)
+References
+----------
+
+* [UCJL] **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu.** "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874)
 
 * [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)
 

docs/model.md

@@ -0,0 +1,244 @@
+```{sectnum}
+---
+start: 4
+depth: 2
+suffix: .
+---
+```
+
+JuMP Model
+==========
+
+In this page, we describe the JuMP optimization model produced by the function `UnitCommitment.build_model`. A detailed understanding of this model is not necessary if you are just interested in using the package to solve some standard unit commitment cases, but it may be useful, for example, if you need to solve a slightly different problem, with additional variables and constraints. The notation in this page generally follows [KnOsWa20].
+
+Decision variables
+------------------
+
+### Generators
+
+Name | Symbol | Description | Unit
+-----|:--------:|-------------|:------:
+`is_on[g,t]` | $u_{g}(t)$ | True if generator `g` is on at time `t`. | Binary
+`switch_on[g,t]` | $v_{g}(t)$ | True is generator `g` switches on at time `t`. | Binary
+`switch_off[g,t]` | $w_{g}(t)$ | True if generator `g` switches off at time `t`. | Binary
+`prod_above[g,t]` |$p'_{g}(t)$ | Amount of power produced by generator `g` above its minimum power output at time `t`. For example, if the minimum power of generator `g` is 100 MW and `g` is producing 115 MW of power at time `t`, then `prod_above[g,t]` equals `15.0`. | MW
+`segprod[g,t,k]` | $p^k_g(t)$ | Amount of power from piecewise linear segment `k` produced by generator `g` at time `t`. For example, if cost curve for generator `g` is defined by the points `(100, 1400)`, `(110, 1600)`, `(130, 2200)` and `(135, 2400)`, and if the generator is producing 115 MW of power at time `t`, then `segprod[g,t,:]` equals `[10.0, 5.0, 0.0]`.| MW
+`reserve[g,t]` | $r_g(t)$ | Amount of reserves provided by generator `g` at time `t`. | MW
+`startup[g,t,s]` | $\delta^s_g(t)$ | True if generator `g` switches on at time `t` incurring start-up costs from start-up category `s`. | Binary
+
+
+### Buses
+
+Name | Symbol | Description | Unit
+-----|:------:|-------------|:------:
+`net_injection[b,t]` | $n_b(t)$ | Net injection at bus `b` at time `t`. | MW
+`curtail[b,t]` | $s^+_b(t)$ | Amount of load curtailed at bus `b` at time `t` | MW
+
+
+### Price-sensitive loads
+
+Name | Symbol | Description | Unit
+-----|:------:|-------------|:------:
+`loads[s,t]` | $d_{s}(t)$ | Amount of power served to price-sensitive load `s` at time `t`. | MW
+
+### Transmission lines
+
+Name | Symbol | Description | Unit
+-----|:------:|-------------|:------:
+`flow[l,t]` | $f_l(t)$ | Power flow on line `l` at time `t`. | MW
+`overflow[l,t]` | $f^+_l(t)$ | Amount of flow above the limit for line `l` at time `t`. | MW
+
+```{warning}
+
+Since transmission and N-1 security constraints are enforced in a lazy way, most of the `flow[l,t]` variables are never added to the model. Accessing `model[:flow][l,t]` without first checking that the variable exists will likely generate an error.
+```
+
+Objective function
+------------------
+
+$$
+\begin{align}
+    \text{minimize} \;\; &
+        \sum_{t \in \mathcal{T}}
+          \sum_{g \in \mathcal{G}}
+          C^\text{min}_g(t) u_g(t) \\
+    &
+        + \sum_{t \in \mathcal{T}}
+          \sum_{g \in \mathcal{G}}
+          \sum_{g \in \mathcal{K}_g}
+          C^k_g(t) p^k_g(t) \\
+    &
+        + \sum_{t \in \mathcal{T}}
+          \sum_{g \in \mathcal{G}}
+          \sum_{s \in \mathcal{S}_g}
+          C^s_{g}(t) \delta^s_g(t) \\
+    &
+        + \sum_{t \in \mathcal{T}}
+          \sum_{l \in \mathcal{L}}
+          C^\text{overflow}_{l}(t) f^+_l(t) \\
+    &
+        + \sum_{t \in \mathcal{T}}
+           \sum_{b \in \mathcal{B}}
+           C^\text{curtail}(t) s^+_b(t) \\
+    &
+        - \sum_{t \in \mathcal{T}}
+           \sum_{s \in \mathcal{PS}}
+           R_{s}(t) d_{s}(t) \\
+          
+\end{align}
+$$
+where
+- $\mathcal{B}$ is the set of buses
+- $\mathcal{G}$ is the set of generators
+- $\mathcal{L}$ is the set of transmission lines
+- $\mathcal{PS}$ is the set of price-sensitive loads
+- $\mathcal{S}_g$ is the set of start-up categories for generator $g$
+- $\mathcal{T}$ is the set of time steps
+- $C^\text{curtail}(t)$ is the curtailment penalty (in \$/MW)
+- $C^\text{min}_g(t)$ is the cost of keeping generator $g$ on and producing at minimum power during time $t$ (in \$)
+- $C^\text{overflow}_{l}(t)$ is the flow limit penalty for line $l$ at time $t$ (in \$/MW)
+- $C^k_g(t)$ is the cost for generator $g$ to produce 1 MW of power at time $t$ under piecewise linear segment $k$
+- $C^s_{g}(t)$ is the cost of starting up generator $g$ at time $t$ under start-up category $s$ (in \$)
+- $R_{s}(t)$ is the revenue obtained from serving price-sensitive load $s$ at time $t$ (in \$/MW)
+
+
+Constraints
+-----------
+
+TODO
+
+
+Inspecting and modifying the model
+----------------------------------
+
+### Accessing decision variables
+
+After building a model using `UnitCommitment.build_model`, it is possible to obtain a reference to the decision variables by calling `model[:varname][index]`. For example, `model[:is_on]["g1",1]` returns a direct reference to the JuMP variable indicating whether generator named "g1" is on at time 1. The script below illustrates how to build a model, solve it and display the solution without using the function `UnitCommitment.solution`.
+
+```julia
+using Cbc
+using Printf
+using JuMP
+using UnitCommitment
+
+# Load benchmark instance
+instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
+
+# Build JuMP model
+model = UnitCommitment.build_model(
+    instance=instance,
+    optimizer=Cbc.Optimizer,
+)
+
+# Solve the model
+UnitCommitment.optimize!(model)
+
+# Display commitment status
+for g in instance.units
+    for t in 1:instance.time
+        @printf(
+            "%-10s %5d %5.1f %5.1f %5.1f\n",
+            g.name,
+            t,
+            value(model[:is_on][g.name, t]),
+            value(model[:switch_on][g.name, t]),
+            value(model[:switch_off][g.name, t]),
+        )
+    end
+end
+```
+
+### Fixing variables, modifying objective function and adding constraints
+
+Since we now have a direct reference to the JuMP decision variables, it is possible to fix variables, change the coefficients in the objective function, or even add new constraints to the model before solving it. The script below shows how can this be accomplished. For more information on modifying an existing model, [see the JuMP documentation](https://jump.dev/JuMP.jl/stable/manual/variables/).
+
+```julia
+using Cbc
+using JuMP
+using UnitCommitment
+
+# Load benchmark instance
+instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
+
+# Construct JuMP model
+model = UnitCommitment.build_model(
+    instance=instance,
+    optimizer=Cbc.Optimizer,
+)
+
+# Fix a decision variable to 1.0
+JuMP.fix(
+    model[:is_on]["g1",1],
+    1.0,
+    force=true,
+)
+
+# Change the objective function
+JuMP.set_objective_coefficient(
+    model,
+    model[:switch_on]["g2",1],
+    1000.0,
+)
+
+# Create a new constraint
+@constraint(
+    model,
+    model[:is_on]["g3",1] + model[:is_on]["g4",1] <= 1,
+)
+
+# Solve the model
+UnitCommitment.optimize!(model)
+```
+
+### Adding new component to a bus
+
+The following snippet shows how to add a new grid component to a particular bus. For each time step, we create decision variables for the new grid component, add these variables to the objective function, then attach the component to a particular bus by modifying some existing model constraints. 
+
+```julia
+using Cbc
+using JuMP
+using UnitCommitment
+
+# Load instance and build base model
+instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
+model = UnitCommitment.build_model(
+    instance=instance,
+    optimizer=Cbc.Optimizer,
+)
+
+# Get the number of time steps in the original instance
+T = instance.time
+
+# Create decision variables for the new grid component.
+# In this example, we assume that the new component can
+# inject up to 10 MW of power at each time step, so we
+# create new continuous variables 0 ≤ x[t] ≤ 10.
+@variable(model, x[1:T], lower_bound=0.0, upper_bound=10.0)
+
+# For each time step
+for t in 1:T
+
+    # Add production costs to the objective function.
+    # In this example, we assume a cost of $5/MW.
+    set_objective_coefficient(model, x[t], 5.0)
+
+    # Attach the new component to bus b1, by modifying the
+    # constraint `eq_net_injection`.
+    set_normalized_coefficient(
+        model[:eq_net_injection]["b1", t],
+        x[t],
+        1.0,
+    )
+end
+
+# Solve the model
+UnitCommitment.optimize!(model)
+
+# Show optimal values for the x variables
+@show value.(x)
+```
+
+References
+----------
+* [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)
+

docs/usage.md

@@ -1,11 +1,21 @@
-# Usage
+```{sectnum}
+---
+start: 1
+depth: 2
+suffix: .
+---
+```
 
-## 1. Installation
+Usage
+=====
 
-UnitCommitment.jl was tested and developed with [Julia 1.5](https://julialang.org/). To install Julia, please follow the [installation guide on the official Julia website](https://julialang.org/downloads/platform.html). To install UnitCommitment.jl, run the Julia interpreter, type `]` to open the package manager, then type:
+Installation
+------------
+
+UnitCommitment.jl was tested and developed with [Julia 1.6](https://julialang.org/). To install Julia, please follow the [installation guide on the official Julia website](https://julialang.org/downloads/platform.html). To install UnitCommitment.jl, run the Julia interpreter, type `]` to open the package manager, then type:
 
 ```text
-pkg> add UnitCommitment
+pkg> add UnitCommitment@0.2
 ```
 
 To test that the package has been correctly installed, run:
@@ -18,52 +28,81 @@ If all tests pass, the package should now be ready to be used by any Julia scrip
 
 To solve the optimization models, a mixed-integer linear programming (MILP) solver is also required. Please see the [JuMP installation guide](https://jump.dev/JuMP.jl/stable/installation/) for more instructions on installing a solver. Typical open-source choices are [Cbc](https://github.com/JuliaOpt/Cbc.jl) and [GLPK](https://github.com/JuliaOpt/GLPK.jl). In the instructions below, Cbc will be used, but any other MILP solver listed in JuMP installation guide should also be compatible.
 
-## 2. Typical Usage
+Typical Usage
+-------------
 
-### 2.1 Solving user-provided instances
+### Solving user-provided instances
 
-The first step to use UC.jl is to construct a JSON file describing your unit commitment instance. See the [data format page]() for a complete description of the data format UC.jl expects. The next steps, as shown below, are to read the instance from file, construct the optimization model, run the optimization and extract the optimal solution.
+The first step to use UC.jl is to construct a JSON file describing your unit commitment instance. See [Data Format](format.md) for a complete description of the data format UC.jl expects. The next steps, as shown below, are to: (1) read the instance from file; (2) construct the optimization model; (3) run the optimization; and (4) extract the optimal solution.
 
 ```julia
 using Cbc
 using JSON
 using UnitCommitment
 
-# Read instance
+# 1. Read instance
 instance = UnitCommitment.read("/path/to/input.json")
 
-# Construct optimization model
-model = UnitCommitment.build_model(instance=instance,
-                                   optimizer=Cbc.Optimizer)
+# 2. Construct optimization model
+model = UnitCommitment.build_model(
+    instance=instance,
+    optimizer=Cbc.Optimizer,
+)
 
-# Solve model
+# 3. Solve model
 UnitCommitment.optimize!(model)
 
-# Extract solution and write it to a file
-solution = UnitCommitment.get_solution(model)
-open("/path/to/output.json", "w") do file
-    JSON.print(file, solution, 2)
-end
+# 4. Write solution to a file
+solution = UnitCommitment.solution(model)
+UnitCommitment.write("/path/to/output.json", solution)
 ```
 
-### 2.2 Solving benchmark instances
+### Solving benchmark instances
 
-As described in the [Instances page](instances.md), UnitCommitment.jl contains a number of benchmark instances collected from the literature. To solve one of these instances individually, instead of constructing your own, the function `read_benchmark` can be used:
+UnitCommitment.jl contains a large number of benchmark instances collected from the literature and converted into a common data format. To solve one of these instances individually, instead of constructing your own, the function `read_benchmark` can be used, as shown below. See [Instances](instances.md) for the complete list of available instances.
 
 ```julia
 using UnitCommitment
 instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
 ```
 
-## 3. Advanced usage
+Advanced usage
+--------------
 
+### Customizing the formulation
 
-### 3.1 Modifying the formulation
+By default, `build_model` uses a formulation that combines modeling components from different publications, and that has been carefully tested, using our own benchmark scripts, to provide good performance across a wide variety of instances. This default formulation is expected to change over time, as new methods are proposed in the literature. You can, however, construct your own formulation, based on the modeling components that you choose, as shown in the next example.
 
-For the time being, the recommended way of modifying the MILP formulation used by UC.jl is to create a local copy of our git repository and directly modify the source code of the package. In a future version, it will be possible to switch between multiple formulations, or to simply add/remove constraints after the model has been generated.
+```julia
+using Cbc
+using UnitCommitment
 
-### 3.2 Generating initial conditions
+import UnitCommitment:
+    Formulation,
+    KnuOstWat2018,
+    MorLatRam2013,
+    ShiftFactorsFormulation
 
+instance = UnitCommitment.read_benchmark(
+    "matpower/case118/2017-02-01",
+)
+
+model = UnitCommitment.build_model(
+    instance = instance,
+    optimizer = Cbc.Optimizer,
+    formulation = Formulation(
+        pwl_costs = KnuOstWat2018.PwlCosts(),
+        ramping = MorLatRam2013.Ramping(),
+        startup_costs = MorLatRam2013.StartupCosts(),
+        transmission = ShiftFactorsFormulation(
+            isf_cutoff = 0.005,
+            lodf_cutoff = 0.001,
+        ),
+    ),
+)
+```
+
+### Generating initial conditions
 
 When creating random unit commitment instances for benchmark purposes, it is often hard to compute, in advance, sensible initial conditions for all generators. Setting initial conditions naively (for example, making all generators initially off and producing no power) can easily cause the instance to become infeasible due to excessive ramping. Initial conditions can also make it hard to modify existing instances. For example, increasing the system load without carefully modifying the initial conditions may make the problem infeasible or unrealistically challenging to solve.
 
@@ -80,14 +119,18 @@ instance = UnitCommitment.read("instance.json")
 UnitCommitment.generate_initial_conditions!(instance, Cbc.Optimizer)
 
 # Construct and solve optimization model
-model = UnitCommitment.build_model(instance, Cbc.Optimizer)
+model = UnitCommitment.build_model(
+    instance=instance,
+    optimizer=Cbc.Optimizer,
+)
 UnitCommitment.optimize!(model)
 ```
 
-!!! warning
-    The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
+```{warning}
+The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
+```
     
-### 3.3 Verifying solutions
+### Verifying solutions
 
 When developing new formulations, it is very easy to introduce subtle errors in the model that result in incorrect solutions. To help with this, UC.jl includes a utility function that verifies if a given solution is feasible, and, if not, prints all the validation errors it found. The implementation of this function is completely independent from the implementation of the optimization model, and therefore can be used to validate it. The function can also be used to verify solutions produced by other optimization packages, as long as they follow the [UC.jl data format](format.md).
 

mkdocs.yml

@@ -1,26 +0,0 @@
-site_name: UnitCommitment.jl
-theme:
-  name: cinder
-  hljs_languages:
-    - julia
-copyright: "Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved."
-repo_url: https://github.com/ANL-CEEESA/unitcommitment.jl
-edit_uri: edit/dev/src/docs/
-nav:
-    - Home: index.md
-    - Usage: usage.md
-    - Format: format.md
-    - Instances: instances.md
-plugins:
-  - search
-markdown_extensions:
-  - admonition  
-  - mdx_math  
-  - fenced_code
-extra_javascript: 
-    - https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML
-    - js/mathjax.js
-docs_dir: src/docs
-site_dir: docs
-extra_css:
-    - "css/custom.css"

src/UnitCommitment.jl

@@ -3,13 +3,54 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 
 module UnitCommitment
-    include("log.jl")
-    include("dotdict.jl")
-    include("instance.jl")
-    include("screening.jl")
-    include("model.jl")
-    include("sensitivity.jl")
-    include("validate.jl")
-    include("convert.jl")
-    include("initcond.jl")
+
+include("instance/structs.jl")
+include("model/formulations/base/structs.jl")
+include("solution/structs.jl")
+
+include("model/formulations/ArrCon2000/structs.jl")
+include("model/formulations/CarArr2006/structs.jl")
+include("model/formulations/DamKucRajAta2016/structs.jl")
+include("model/formulations/Gar1962/structs.jl")
+include("model/formulations/KnuOstWat2018/structs.jl")
+include("model/formulations/MorLatRam2013/structs.jl")
+include("model/formulations/PanGua2016/structs.jl")
+include("solution/methods/XavQiuWanThi2019/structs.jl")
+
+include("import/egret.jl")
+include("instance/read.jl")
+include("model/build.jl")
+include("model/formulations/ArrCon2000/ramp.jl")
+include("model/formulations/base/bus.jl")
+include("model/formulations/base/line.jl")
+include("model/formulations/base/psload.jl")
+include("model/formulations/base/sensitivity.jl")
+include("model/formulations/base/system.jl")
+include("model/formulations/base/unit.jl")
+include("model/formulations/CarArr2006/pwlcosts.jl")
+include("model/formulations/DamKucRajAta2016/ramp.jl")
+include("model/formulations/Gar1962/pwlcosts.jl")
+include("model/formulations/Gar1962/status.jl")
+include("model/formulations/Gar1962/prod.jl")
+include("model/formulations/KnuOstWat2018/pwlcosts.jl")
+include("model/formulations/MorLatRam2013/ramp.jl")
+include("model/formulations/MorLatRam2013/scosts.jl")
+include("model/formulations/PanGua2016/ramp.jl")
+include("model/jumpext.jl")
+include("solution/fix.jl")
+include("solution/methods/XavQiuWanThi2019/enforce.jl")
+include("solution/methods/XavQiuWanThi2019/filter.jl")
+include("solution/methods/XavQiuWanThi2019/find.jl")
+include("solution/methods/XavQiuWanThi2019/optimize.jl")
+include("solution/optimize.jl")
+include("solution/solution.jl")
+include("solution/warmstart.jl")
+include("solution/write.jl")
+include("transform/initcond.jl")
+include("transform/slice.jl")
+include("transform/randomize/XavQiuAhm2021.jl")
+include("utils/log.jl")
+include("validation/repair.jl")
+include("validation/validate.jl")
+
 end

src/docs/css/custom.css

@@ -1,28 +0,0 @@
-.navbar-default {
-  border-bottom: 0px;
-  background-color: #fff;
-  box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
-}
-
-a, .navbar-default a {
-  color: #06a !important;
-  font-weight: normal;
-}
-
-.disabled > a {
-  color: #999 !important;
-}
-
-.navbar-default a:hover,
-.navbar-default .active,
-.active > a {
-  background-color: #f0f0f0 !important;
-}
-
-.icon-bar {
-  background-color: #666 !important;
-}
-
-.navbar-collapse {
-  border-color: #fff !important;
-}

src/docs/index.md

@@ -1,42 +0,0 @@
-# UnitCommitment.jl
-
-**UnitCommitment.jl** (UC.jl) is a Julia optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and Julia/JuMP implementations of state-of-the-art mixed-integer programming formulations.
-
-### Package Components
-
-* **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
-* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature and extended to make them more challenging and realistic.
-* **Model Implementation**: The package provides a Julia/JuMP implementation of state-of-the-art formulations and solution methods for SCUC. Our goal is to keep this implementation up-to-date, as new methods are proposed in the literature.
-* **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
-
-### Documentation
-
-* [Usage](usage.md)
-* [Data Format](format.md)
-* [Instances](instances.md)
-
-### Source code
-
-* [https://github.com/ANL-CEEESA/unitcommitment.jl](https://github.com/ANL-CEEESA/unitcommitment.jl)
-
-### Authors
-* **Alinson Santos Xavier** (Argonne National Laboratory)
-* **Feng Qiu** (Argonne National Laboratory)
-
-### Acknowledgments
-
-* We would like to thank **Aleksandr M. Kazachkov** (University of Florida), **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
-
-* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
-
-### Citing
-
-If you use UnitCommitment.jl in your research, we request that you cite the package as follows:
-
-* Alinson S. Xavier, Feng Qiu, "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
-
-If you make use of the provided instances files, we request that you additionally cite the original sources, as described in the [instances page](instances.md).
-
-### License
-
-Released under the modified BSD license. See `LICENSE.md` for more details.

src/docs/js/mathjax.js

@@ -1,8 +0,0 @@
-MathJax.Hub.Config({
-  "tex2jax": { inlineMath: [ [ '$', '$' ] ] }
-});
-MathJax.Hub.Config({
-  config: ["MMLorHTML.js"],
-  jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"],
-  extensions: ["MathMenu.js", "MathZoom.js"]
-});

src/dotdict.jl

@@ -1,68 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-struct DotDict
-    inner::Dict
-end
-
-DotDict() = DotDict(Dict())
-
-function Base.setproperty!(d::DotDict, key::Symbol, value)
-    setindex!(getfield(d, :inner), value, key)
-end
-
-function Base.getproperty(d::DotDict, key::Symbol)
-    (key == :inner ? getfield(d, :inner) : d.inner[key])
-end
-
-function Base.getindex(d::DotDict, key::Int64)
-    d.inner[Symbol(key)]
-end
-
-function Base.getindex(d::DotDict, key::Symbol)
-    d.inner[key]
-end
-
-function Base.keys(d::DotDict)
-    keys(d.inner)
-end
-
-function Base.values(d::DotDict)
-    values(d.inner)
-end
-
-function Base.iterate(d::DotDict)
-    iterate(values(d.inner))
-end
-
-function Base.iterate(d::DotDict, v::Int64)
-    iterate(values(d.inner), v)
-end
-
-function Base.length(d::DotDict)
-    length(values(d.inner))
-end
-
-function Base.show(io::IO, d::DotDict)
-    print(io, "DotDict with $(length(keys(d.inner))) entries:\n")
-    count = 0
-    for k in keys(d.inner)
-        count += 1
-        if count > 10
-            print(io, "  ...\n")
-            break
-        end
-        print(io, "  :$(k) => $(d.inner[k])\n")
-    end
-end
-
-function recursive_to_dot_dict(el)
-    if typeof(el) == Dict{String, Any}
-        return DotDict(Dict(Symbol(k) => recursive_to_dot_dict(el[k]) for k in keys(el)))
-    else
-        return el
-    end
-end
-
-export recursive_to_dot_dict

src/import/egret.jl

@@ -4,26 +4,25 @@
 
 using DataStructures, JSON, GZip
 
-function read_json(path::String)::OrderedDict
-    if endswith(path, ".gz")
-        file = GZip.gzopen(path)
-    else
-        file = open(path)
-    end
-    return JSON.parse(file, dicttype=()->DefaultOrderedDict(nothing))
-end
+"""
 
+    read_egret_solution(path::String)::OrderedDict
+
+Read a JSON solution file produced by EGRET and transforms it into a
+dictionary having the same structure as the one produced by
+UnitCommitment.solution(model).
+"""
 function read_egret_solution(path::String)::OrderedDict
-    egret = read_json(path)
+    egret = _read_json(path)
     T = length(egret["system"]["time_keys"])
-    
-    solution   = OrderedDict()
-    is_on      = solution["Is on"] = OrderedDict()
+
+    solution = OrderedDict()
+    is_on = solution["Is on"] = OrderedDict()
     production = solution["Production (MW)"] = OrderedDict()
-    reserve    = solution["Reserve (MW)"] = OrderedDict()
+    reserve = solution["Reserve (MW)"] = OrderedDict()
     production_cost = solution["Production cost (\$)"] = OrderedDict()
-    startup_cost    = solution["Startup cost (\$)"] = OrderedDict()
-    
+    startup_cost = solution["Startup cost (\$)"] = OrderedDict()
+
     for (gen_name, gen_dict) in egret["elements"]["generator"]
         if endswith(gen_name, "_T") || endswith(gen_name, "_R")
             gen_name = gen_name[1:end-2]
@@ -39,18 +38,18 @@ function read_egret_solution(path::String)::OrderedDict
         else
             reserve[gen_name] = zeros(T)
         end
-        startup_cost[gen_name] = zeros(T) 
+        startup_cost[gen_name] = zeros(T)
         production_cost[gen_name] = zeros(T)
         if "commitment_cost" in keys(gen_dict)
             for t in 1:T
                 x = gen_dict["commitment"]["values"][t]
                 commitment_cost = gen_dict["commitment_cost"]["values"][t]
                 prod_above_cost = gen_dict["production_cost"]["values"][t]
-                prod_base_cost  = gen_dict["p_cost"]["values"][1][2] * x
+                prod_base_cost = gen_dict["p_cost"]["values"][1][2] * x
                 startup_cost[gen_name][t] = commitment_cost - prod_base_cost
                 production_cost[gen_name][t] = prod_above_cost + prod_base_cost
             end
         end
     end
     return solution
-end
+end

src/instance.jl

@@ -1,349 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using Printf
-using JSON
-using DataStructures
-import Base: getindex, time
-import GZip
-
-
-mutable struct Bus
-    name::String
-    offset::Int
-    load::Array{Float64}
-    units::Array
-    price_sensitive_loads::Array
-end
-
-
-mutable struct CostSegment
-    mw::Array{Float64}
-    cost::Array{Float64}
-end
-
-
-mutable struct StartupCategory
-    delay::Int
-    cost::Float64
-end
-
-
-mutable struct Unit
-    name::String
-    bus::Bus
-    max_power::Array{Float64}
-    min_power::Array{Float64}
-    must_run::Array{Bool}
-    min_power_cost::Array{Float64}
-    cost_segments::Array{CostSegment}
-    min_uptime::Int
-    min_downtime::Int
-    ramp_up_limit::Float64
-    ramp_down_limit::Float64
-    startup_limit::Float64
-    shutdown_limit::Float64
-    initial_status::Union{Int,Nothing}
-    initial_power::Union{Float64,Nothing}
-    provides_spinning_reserves::Array{Bool}
-    startup_categories::Array{StartupCategory}
-end
-
-
-mutable struct TransmissionLine
-    name::String
-    offset::Int
-    source::Bus
-    target::Bus
-    reactance::Float64
-    susceptance::Float64
-    normal_flow_limit::Array{Float64}
-    emergency_flow_limit::Array{Float64}
-    flow_limit_penalty::Array{Float64}
-end
-
-
-mutable struct Reserves
-    spinning::Array{Float64}
-end
-
-
-mutable struct Contingency
-    name::String
-    lines::Array{TransmissionLine}
-    units::Array{Unit}
-end
-
-
-mutable struct PriceSensitiveLoad
-    name::String
-    bus::Bus
-    demand::Array{Float64}
-    revenue::Array{Float64}
-end
-
-
-mutable struct UnitCommitmentInstance
-    time::Int
-    power_balance_penalty::Array{Float64}
-    units::Array{Unit}
-    buses::Array{Bus}
-    lines::Array{TransmissionLine}
-    reserves::Reserves
-    contingencies::Array{Contingency}
-    price_sensitive_loads::Array{PriceSensitiveLoad}
-end
-
-
-function Base.show(io::IO, instance::UnitCommitmentInstance)
-    print(io, "UnitCommitmentInstance with ")
-    print(io, "$(length(instance.units)) units, ")
-    print(io, "$(length(instance.buses)) buses, ")
-    print(io, "$(length(instance.lines)) lines, ")
-    print(io, "$(length(instance.contingencies)) contingencies, ")
-    print(io, "$(length(instance.price_sensitive_loads)) price sensitive loads")
-end
-
-
-function read_benchmark(name::AbstractString) :: UnitCommitmentInstance
-    basedir = dirname(@__FILE__)
-    return UnitCommitment.read("$basedir/../instances/$name.json.gz")
-end
-
-
-function read(path::AbstractString)::UnitCommitmentInstance
-    if endswith(path, ".gz")
-        return read(GZip.gzopen(path))
-    else
-        return read(open(path))
-    end
-end
-
-
-function read(file::IO)::UnitCommitmentInstance
-    return from_json(JSON.parse(file, dicttype=()->DefaultOrderedDict(nothing)))
-end
-    
-function from_json(json; fix=true)
-    units = Unit[]
-    buses = Bus[]
-    contingencies = Contingency[]
-    lines = TransmissionLine[]
-    loads = PriceSensitiveLoad[]
-    T = json["Parameters"]["Time (h)"]
-    
-    name_to_bus = Dict{String, Bus}()
-    name_to_line = Dict{String, TransmissionLine}()
-    name_to_unit = Dict{String, Unit}()
-    
-    function timeseries(x; default=nothing)
-        x !== nothing || return default
-        x isa Array || return [x for t in 1:T]
-        return x
-    end
-    
-    function scalar(x; default=nothing)
-        x !== nothing || return default
-        x
-    end
-    
-    # Read parameters
-    power_balance_penalty = timeseries(json["Parameters"]["Power balance penalty (\$/MW)"],
-                                       default=[1000.0 for t in 1:T])
-    
-    # Read buses
-    for (bus_name, dict) in json["Buses"]
-        bus = Bus(bus_name,
-                  length(buses),
-                  timeseries(dict["Load (MW)"]),
-                  Unit[],
-                  PriceSensitiveLoad[])
-        name_to_bus[bus_name] = bus
-        push!(buses, bus)
-    end
-    
-    # Read units
-    for (unit_name, dict) in json["Generators"]
-        bus = name_to_bus[dict["Bus"]]
-        
-        # Read production cost curve
-        K = length(dict["Production cost curve (MW)"])
-        curve_mw   = hcat([timeseries(dict["Production cost curve (MW)"][k]) for k in 1:K]...)
-        curve_cost = hcat([timeseries(dict["Production cost curve (\$)"][k]) for k in 1:K]...)
-        min_power = curve_mw[:, 1]
-        max_power = curve_mw[:, K]
-        min_power_cost = curve_cost[:, 1]
-        segments = CostSegment[]
-        for k in 2:K
-            amount = curve_mw[:, k] - curve_mw[:, k-1]
-            cost = (curve_cost[:, k] - curve_cost[:, k-1]) ./ amount
-            replace!(cost, NaN=>0.0)
-            push!(segments, CostSegment(amount, cost))
-        end
-        
-        # Read startup costs
-        startup_delays = scalar(dict["Startup delays (h)"], default=[1])
-        startup_costs  = scalar(dict["Startup costs (\$)"], default=[0.])
-        startup_categories = StartupCategory[]
-        for k in 1:length(startup_delays)
-            push!(startup_categories, StartupCategory(startup_delays[k],
-                                                      startup_costs[k]))
-        end
-        
-        # Read and validate initial conditions
-        initial_power = scalar(dict["Initial power (MW)"], default=nothing)
-        initial_status = scalar(dict["Initial status (h)"], default=nothing)
-        if initial_power === nothing
-            initial_status === nothing || error("unit $unit_name has initial status but no initial power")
-        else
-            initial_status !== nothing || error("unit $unit_name has initial power but no initial status")
-            initial_status != 0 || error("unit $unit_name has invalid initial status")
-            if initial_status < 0 && initial_power > 1e-3
-                error("unit $unit_name has invalid initial power")
-            end
-        end
-        
-        unit = Unit(unit_name,
-                    bus,
-                    max_power,
-                    min_power,
-                    timeseries(dict["Must run?"], default=[false for t in 1:T]),
-                    min_power_cost,
-                    segments,
-                    scalar(dict["Minimum uptime (h)"], default=1),
-                    scalar(dict["Minimum downtime (h)"], default=1),
-                    scalar(dict["Ramp up limit (MW)"], default=1e6),
-                    scalar(dict["Ramp down limit (MW)"], default=1e6),
-                    scalar(dict["Startup limit (MW)"], default=1e6),
-                    scalar(dict["Shutdown limit (MW)"], default=1e6),
-                    initial_status,
-                    initial_power,
-                    timeseries(dict["Provides spinning reserves?"],
-                               default=[true for t in 1:T]),
-                    startup_categories)
-        push!(bus.units, unit)
-        name_to_unit[unit_name] = unit
-        push!(units, unit)
-    end
-    
-    # Read reserves
-    reserves = Reserves(zeros(T))
-    if "Reserves" in keys(json)
-        reserves.spinning = timeseries(json["Reserves"]["Spinning (MW)"],
-                                       default=zeros(T))
-    end
-    
-    # Read transmission lines
-    if "Transmission lines" in keys(json)
-        for (line_name, dict) in json["Transmission lines"]
-            line = TransmissionLine(line_name,
-                                    length(lines) + 1,
-                                    name_to_bus[dict["Source bus"]],
-                                    name_to_bus[dict["Target bus"]],
-                                    scalar(dict["Reactance (ohms)"]),
-                                    scalar(dict["Susceptance (S)"]),
-                                    timeseries(dict["Normal flow limit (MW)"],
-                                               default=[1e8 for t in 1:T]),
-                                    timeseries(dict["Emergency flow limit (MW)"],
-                                               default=[1e8 for t in 1:T]),
-                                    timeseries(dict["Flow limit penalty (\$/MW)"],
-                                               default=[5000.0 for t in 1:T]))
-            name_to_line[line_name] = line
-            push!(lines, line)
-        end
-    end
-    
-    # Read contingencies
-    if "Contingencies" in keys(json)
-        for (cont_name, dict) in json["Contingencies"]
-            affected_units = Unit[]
-            affected_lines = TransmissionLine[]
-            if "Affected lines" in keys(dict)
-                affected_lines = [name_to_line[l] for l in dict["Affected lines"]]
-            end
-            if "Affected units" in keys(dict)
-                affected_units = [name_to_unit[u] for u in dict["Affected units"]]
-            end
-            cont = Contingency(cont_name, affected_lines, affected_units)
-            push!(contingencies, cont)
-        end
-    end
-    
-    # Read price-sensitive loads
-    if "Price-sensitive loads" in keys(json)
-        for (load_name, dict) in json["Price-sensitive loads"]
-            bus = name_to_bus[dict["Bus"]]
-            load = PriceSensitiveLoad(load_name,
-                                      bus,
-                                      timeseries(dict["Demand (MW)"]),
-                                      timeseries(dict["Revenue (\$/MW)"]),
-                                     )
-            push!(bus.price_sensitive_loads, load)
-            push!(loads, load)
-        end
-    end
-    
-    instance = UnitCommitmentInstance(T,
-                                      power_balance_penalty,
-                                      units,
-                                      buses,
-                                      lines,
-                                      reserves,
-                                      contingencies,
-                                      loads)
-    if fix
-        UnitCommitment.fix!(instance)
-    end
-    return instance
-end
-
-
-"""
-    slice(instance, range)
-
-Creates a new instance, with only a subset of the time periods.
-This function does not modify the provided instance. The initial
-conditions are also not modified.
-
-Example
--------
-
-    # Build a 2-hour UC instance
-    instance = UnitCommitment.read_benchmark("test/case14")
-    modified = UnitCommitment.slice(instance, 1:2)
-
-"""
-function slice(instance::UnitCommitmentInstance, range::UnitRange{Int})::UnitCommitmentInstance
-    modified = deepcopy(instance)
-    modified.time = length(range)
-    modified.power_balance_penalty = modified.power_balance_penalty[range]
-    modified.reserves.spinning = modified.reserves.spinning[range]
-    for u in modified.units
-        u.max_power = u.max_power[range]
-        u.min_power = u.min_power[range]
-        u.must_run = u.must_run[range]
-        u.min_power_cost = u.min_power_cost[range]
-        u.provides_spinning_reserves = u.provides_spinning_reserves[range]
-        for s in u.cost_segments
-            s.mw = s.mw[range]
-            s.cost = s.cost[range]
-        end
-    end
-    for b in modified.buses
-        b.load = b.load[range]
-    end
-    for l in modified.lines
-        l.normal_flow_limit = l.normal_flow_limit[range]
-        l.emergency_flow_limit = l.emergency_flow_limit[range]
-        l.flow_limit_penalty = l.flow_limit_penalty[range]
-    end
-    for ps in modified.price_sensitive_loads
-        ps.demand = ps.demand[range]
-        ps.revenue = ps.revenue[range]
-    end
-    return modified
-end
-
-
-export UnitCommitmentInstance

src/instance/read.jl

@@ -0,0 +1,283 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using Printf
+using JSON
+using DataStructures
+using GZip
+import Base: getindex, time
+
+"""
+    read_benchmark(name::AbstractString)::UnitCommitmentInstance
+
+Read one of the benchmark unit commitment instances included in the package.
+See "Instances" section of the documentation for the entire list of benchmark
+instances available.
+
+Example
+-------
+
+    import UnitCommitment
+    instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
+"""
+function read_benchmark(name::AbstractString)::UnitCommitmentInstance
+    basedir = dirname(@__FILE__)
+    return UnitCommitment.read("$basedir/../../instances/$name.json.gz")
+end
+
+"""
+    read(path::AbstractString)::UnitCommitmentInstance
+
+Read a unit commitment instance from a file. The file may be gzipped.
+
+Example
+-------
+
+    import UnitCommitment
+    instance = UnitCommitment.read("/path/to/input.json.gz")
+"""
+function read(path::AbstractString)::UnitCommitmentInstance
+    if endswith(path, ".gz")
+        return _read(gzopen(path))
+    else
+        return _read(open(path))
+    end
+end
+
+function _read(file::IO)::UnitCommitmentInstance
+    return _from_json(
+        JSON.parse(file, dicttype = () -> DefaultOrderedDict(nothing)),
+    )
+end
+
+function _read_json(path::String)::OrderedDict
+    if endswith(path, ".gz")
+        file = GZip.gzopen(path)
+    else
+        file = open(path)
+    end
+    return JSON.parse(file, dicttype = () -> DefaultOrderedDict(nothing))
+end
+
+function _from_json(json; repair = true)
+    units = Unit[]
+    buses = Bus[]
+    contingencies = Contingency[]
+    lines = TransmissionLine[]
+    loads = PriceSensitiveLoad[]
+
+    function scalar(x; default = nothing)
+        x !== nothing || return default
+        return x
+    end
+
+    time_horizon = json["Parameters"]["Time (h)"]
+    if time_horizon === nothing
+        time_horizon = json["Parameters"]["Time horizon (h)"]
+    end
+    time_horizon !== nothing || error("Missing parameter: Time horizon (h)")
+    time_step = scalar(json["Parameters"]["Time step (min)"], default = 60)
+    (60 % time_step == 0) ||
+        error("Time step $time_step is not a divisor of 60")
+    time_multiplier = 60 ÷ time_step
+    T = time_horizon * time_multiplier
+
+    name_to_bus = Dict{String,Bus}()
+    name_to_line = Dict{String,TransmissionLine}()
+    name_to_unit = Dict{String,Unit}()
+
+    function timeseries(x; default = nothing)
+        x !== nothing || return default
+        x isa Array || return [x for t in 1:T]
+        return x
+    end
+
+    # Read parameters
+    power_balance_penalty = timeseries(
+        json["Parameters"]["Power balance penalty (\$/MW)"],
+        default = [1000.0 for t in 1:T],
+    )
+    shortfall_penalty = timeseries(
+        json["Parameters"]["Reserve shortfall penalty (\$/MW)"],
+        default = [-1.0 for t in 1:T],
+    )
+
+    # Read buses
+    for (bus_name, dict) in json["Buses"]
+        bus = Bus(
+            bus_name,
+            length(buses),
+            timeseries(dict["Load (MW)"]),
+            Unit[],
+            PriceSensitiveLoad[],
+        )
+        name_to_bus[bus_name] = bus
+        push!(buses, bus)
+    end
+
+    # Read units
+    for (unit_name, dict) in json["Generators"]
+        bus = name_to_bus[dict["Bus"]]
+
+        # Read production cost curve
+        K = length(dict["Production cost curve (MW)"])
+        curve_mw = hcat(
+            [timeseries(dict["Production cost curve (MW)"][k]) for k in 1:K]...,
+        )
+        curve_cost = hcat(
+            [timeseries(dict["Production cost curve (\$)"][k]) for k in 1:K]...,
+        )
+        min_power = curve_mw[:, 1]
+        max_power = curve_mw[:, K]
+        min_power_cost = curve_cost[:, 1]
+        segments = CostSegment[]
+        for k in 2:K
+            amount = curve_mw[:, k] - curve_mw[:, k-1]
+            cost = (curve_cost[:, k] - curve_cost[:, k-1]) ./ amount
+            replace!(cost, NaN => 0.0)
+            push!(segments, CostSegment(amount, cost))
+        end
+
+        # Read startup costs
+        startup_delays = scalar(dict["Startup delays (h)"], default = [1])
+        startup_costs = scalar(dict["Startup costs (\$)"], default = [0.0])
+        startup_categories = StartupCategory[]
+        for k in 1:length(startup_delays)
+            push!(
+                startup_categories,
+                StartupCategory(
+                    startup_delays[k] .* time_multiplier,
+                    startup_costs[k],
+                ),
+            )
+        end
+
+        # Read and validate initial conditions
+        initial_power = scalar(dict["Initial power (MW)"], default = nothing)
+        initial_status = scalar(dict["Initial status (h)"], default = nothing)
+        if initial_power === nothing
+            initial_status === nothing ||
+                error("unit $unit_name has initial status but no initial power")
+        else
+            initial_status !== nothing ||
+                error("unit $unit_name has initial power but no initial status")
+            initial_status != 0 ||
+                error("unit $unit_name has invalid initial status")
+            if initial_status < 0 && initial_power > 1e-3
+                error("unit $unit_name has invalid initial power")
+            end
+            initial_status *= time_multiplier
+        end
+
+        unit = Unit(
+            unit_name,
+            bus,
+            max_power,
+            min_power,
+            timeseries(dict["Must run?"], default = [false for t in 1:T]),
+            min_power_cost,
+            segments,
+            scalar(dict["Minimum uptime (h)"], default = 1) * time_multiplier,
+            scalar(dict["Minimum downtime (h)"], default = 1) * time_multiplier,
+            scalar(dict["Ramp up limit (MW)"], default = 1e6),
+            scalar(dict["Ramp down limit (MW)"], default = 1e6),
+            scalar(dict["Startup limit (MW)"], default = 1e6),
+            scalar(dict["Shutdown limit (MW)"], default = 1e6),
+            initial_status,
+            initial_power,
+            timeseries(
+                dict["Provides spinning reserves?"],
+                default = [true for t in 1:T],
+            ),
+            startup_categories,
+        )
+        push!(bus.units, unit)
+        name_to_unit[unit_name] = unit
+        push!(units, unit)
+    end
+
+    # Read reserves
+    reserves = Reserves(zeros(T))
+    if "Reserves" in keys(json)
+        reserves.spinning =
+            timeseries(json["Reserves"]["Spinning (MW)"], default = zeros(T))
+    end
+
+    # Read transmission lines
+    if "Transmission lines" in keys(json)
+        for (line_name, dict) in json["Transmission lines"]
+            line = TransmissionLine(
+                line_name,
+                length(lines) + 1,
+                name_to_bus[dict["Source bus"]],
+                name_to_bus[dict["Target bus"]],
+                scalar(dict["Reactance (ohms)"]),
+                scalar(dict["Susceptance (S)"]),
+                timeseries(
+                    dict["Normal flow limit (MW)"],
+                    default = [1e8 for t in 1:T],
+                ),
+                timeseries(
+                    dict["Emergency flow limit (MW)"],
+                    default = [1e8 for t in 1:T],
+                ),
+                timeseries(
+                    dict["Flow limit penalty (\$/MW)"],
+                    default = [5000.0 for t in 1:T],
+                ),
+            )
+            name_to_line[line_name] = line
+            push!(lines, line)
+        end
+    end
+
+    # Read contingencies
+    if "Contingencies" in keys(json)
+        for (cont_name, dict) in json["Contingencies"]
+            affected_units = Unit[]
+            affected_lines = TransmissionLine[]
+            if "Affected lines" in keys(dict)
+                affected_lines =
+                    [name_to_line[l] for l in dict["Affected lines"]]
+            end
+            if "Affected units" in keys(dict)
+                affected_units =
+                    [name_to_unit[u] for u in dict["Affected units"]]
+            end
+            cont = Contingency(cont_name, affected_lines, affected_units)
+            push!(contingencies, cont)
+        end
+    end
+
+    # Read price-sensitive loads
+    if "Price-sensitive loads" in keys(json)
+        for (load_name, dict) in json["Price-sensitive loads"]
+            bus = name_to_bus[dict["Bus"]]
+            load = PriceSensitiveLoad(
+                load_name,
+                bus,
+                timeseries(dict["Demand (MW)"]),
+                timeseries(dict["Revenue (\$/MW)"]),
+            )
+            push!(bus.price_sensitive_loads, load)
+            push!(loads, load)
+        end
+    end
+
+    instance = UnitCommitmentInstance(
+        T,
+        power_balance_penalty,
+        shortfall_penalty,
+        units,
+        buses,
+        lines,
+        reserves,
+        contingencies,
+        loads,
+    )
+    if repair
+        UnitCommitment.repair!(instance)
+    end
+    return instance
+end

src/instance/structs.jl

@@ -0,0 +1,100 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+mutable struct Bus
+    name::String
+    offset::Int
+    load::Vector{Float64}
+    units::Vector
+    price_sensitive_loads::Vector
+end
+
+mutable struct CostSegment
+    mw::Vector{Float64}
+    cost::Vector{Float64}
+end
+
+mutable struct StartupCategory
+    delay::Int
+    cost::Float64
+end
+
+mutable struct Unit
+    name::String
+    bus::Bus
+    max_power::Vector{Float64}
+    min_power::Vector{Float64}
+    must_run::Vector{Bool}
+    min_power_cost::Vector{Float64}
+    cost_segments::Vector{CostSegment}
+    min_uptime::Int
+    min_downtime::Int
+    ramp_up_limit::Float64
+    ramp_down_limit::Float64
+    startup_limit::Float64
+    shutdown_limit::Float64
+    initial_status::Union{Int,Nothing}
+    initial_power::Union{Float64,Nothing}
+    provides_spinning_reserves::Vector{Bool}
+    startup_categories::Vector{StartupCategory}
+end
+
+mutable struct TransmissionLine
+    name::String
+    offset::Int
+    source::Bus
+    target::Bus
+    reactance::Float64
+    susceptance::Float64
+    normal_flow_limit::Vector{Float64}
+    emergency_flow_limit::Vector{Float64}
+    flow_limit_penalty::Vector{Float64}
+end
+
+mutable struct Reserves
+    spinning::Vector{Float64}
+end
+
+mutable struct Contingency
+    name::String
+    lines::Vector{TransmissionLine}
+    units::Vector{Unit}
+end
+
+mutable struct PriceSensitiveLoad
+    name::String
+    bus::Bus
+    demand::Vector{Float64}
+    revenue::Vector{Float64}
+end
+
+mutable struct UnitCommitmentInstance
+    time::Int
+    power_balance_penalty::Vector{Float64}
+    "Penalty for failing to meet reserve requirement."
+    shortfall_penalty::Vector{Float64}
+    units::Vector{Unit}
+    buses::Vector{Bus}
+    lines::Vector{TransmissionLine}
+    reserves::Reserves
+    contingencies::Vector{Contingency}
+    price_sensitive_loads::Vector{PriceSensitiveLoad}
+end
+
+function Base.show(io::IO, instance::UnitCommitmentInstance)
+    print(io, "UnitCommitmentInstance(")
+    print(io, "$(length(instance.units)) units, ")
+    print(io, "$(length(instance.buses)) buses, ")
+    print(io, "$(length(instance.lines)) lines, ")
+    print(io, "$(length(instance.contingencies)) contingencies, ")
+    print(
+        io,
+        "$(length(instance.price_sensitive_loads)) price sensitive loads, ",
+    )
+    print(io, "$(instance.time) time steps")
+    print(io, ")")
+    return
+end
+
+export UnitCommitmentInstance

src/model.jl

@@ -1,646 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using JuMP, MathOptInterface, DataStructures
-import JuMP: value, fix, set_name
-
-
-# Extend some JuMP functions so that decision variables can be safely replaced by
-# (constant) floating point numbers.
-function value(x::Float64)
-    x
-end
-
-function fix(x::Float64, v::Float64; force)
-    abs(x - v) < 1e-6 || error("Value mismatch: $x != $v")
-end
-
-function set_name(x::Float64, n::String)
-    # nop
-end
-
-
-mutable struct UnitCommitmentModel
-    mip::JuMP.Model
-    vars::DotDict
-    eqs::DotDict
-    exprs::DotDict
-    instance::UnitCommitmentInstance
-    isf::Array{Float64, 2}
-    lodf::Array{Float64, 2}
-    obj::AffExpr
-end
-
-
-function build_model(;
-                     filename::Union{String, Nothing}=nothing,
-                     instance::Union{UnitCommitmentInstance, Nothing}=nothing,
-                     isf::Union{Array{Float64,2}, Nothing}=nothing,
-                     lodf::Union{Array{Float64,2}, Nothing}=nothing,
-                     isf_cutoff::Float64=0.005,
-                     lodf_cutoff::Float64=0.001,
-                     optimizer=nothing,
-                     model=nothing,
-                     variable_names::Bool=false,
-                    ) :: UnitCommitmentModel
-
-    if (filename === nothing) && (instance === nothing)
-        error("Either filename or instance must be specified")
-    end
-    
-    if filename !== nothing
-        @info "Reading: $(filename)"
-        time_read = @elapsed begin
-            instance = UnitCommitment.read(filename)
-        end
-        @info @sprintf("Read problem in %.2f seconds", time_read)
-    end
-    
-    if length(instance.buses) == 1
-        isf = zeros(0, 0)
-        lodf = zeros(0, 0)
-    else
-        if isf === nothing
-            @info "Computing injection shift factors..."
-            time_isf = @elapsed begin
-                isf = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                             buses=instance.buses)
-            end
-            @info @sprintf("Computed ISF in %.2f seconds", time_isf)
-            
-            @info "Computing line outage factors..."
-            time_lodf = @elapsed begin
-                lodf = UnitCommitment.line_outage_factors(lines=instance.lines,
-                                                          buses=instance.buses,
-                                                          isf=isf)
-            end
-            @info @sprintf("Computed LODF in %.2f seconds", time_lodf)
-            
-            @info @sprintf("Applying PTDF and LODF cutoffs (%.5f, %.5f)", isf_cutoff, lodf_cutoff)
-            isf[abs.(isf) .< isf_cutoff] .= 0
-            lodf[abs.(lodf) .< lodf_cutoff] .= 0
-        end
-    end
-
-    @info "Building model..."
-    time_model = @elapsed begin
-        if model === nothing
-            if optimizer === nothing
-                mip = Model()
-            else
-                mip = Model(optimizer)
-            end
-        else
-            mip = model
-        end
-        model = UnitCommitmentModel(mip,
-                                    DotDict(),  # vars
-                                    DotDict(),  # eqs
-                                    DotDict(),  # exprs
-                                    instance,
-                                    isf,
-                                    lodf,
-                                    AffExpr(),  # obj
-                                   )
-        for field in [:prod_above, :segprod, :reserve, :is_on, :switch_on, :switch_off,
-                      :net_injection, :curtail, :overflow, :loads, :startup]
-            setproperty!(model.vars, field, OrderedDict())
-        end
-        for field in [:startup_choose, :startup_restrict, :segprod_limit, :prod_above_def,
-                      :prod_limit, :binary_link, :switch_on_off, :ramp_up, :ramp_down,
-                      :startup_limit, :shutdown_limit, :min_uptime, :min_downtime, :power_balance,
-                      :net_injection_def, :min_reserve]
-            setproperty!(model.eqs, field, OrderedDict())
-        end
-        for field in [:inj, :reserve, :net_injection]
-            setproperty!(model.exprs, field, OrderedDict())
-        end
-        for lm in instance.lines
-            add_transmission_line!(model, lm)
-        end
-        for b in instance.buses
-            add_bus!(model, b)
-        end
-        for g in instance.units
-            add_unit!(model, g)
-        end
-        for ps in instance.price_sensitive_loads
-            add_price_sensitive_load!(model, ps)
-        end
-        build_net_injection_eqs!(model)
-        build_reserve_eqs!(model)
-        build_obj_function!(model)
-    end
-    @info @sprintf("Built model in %.2f seconds", time_model)
-
-    if variable_names
-        set_variable_names!(model)
-    end
-    
-    return model
-end
-
-
-function add_transmission_line!(model, lm)
-    vars, obj, T = model.vars, model.obj, model.instance.time
-    for t in 1:T
-        overflow = vars.overflow[lm.name, t] = @variable(model.mip, lower_bound=0)
-        add_to_expression!(obj, overflow, lm.flow_limit_penalty[t])
-    end
-end
-
-
-function add_bus!(model::UnitCommitmentModel, b::Bus)
-    mip, vars, exprs = model.mip, model.vars, model.exprs
-    for t in 1:model.instance.time
-        # Fixed load
-        exprs.net_injection[b.name, t] = AffExpr(-b.load[t])
-        
-        # Reserves
-        exprs.reserve[b.name, t] = AffExpr()
-
-         # Load curtailment
-        vars.curtail[b.name, t] = @variable(mip, lower_bound=0, upper_bound=b.load[t])
-        add_to_expression!(exprs.net_injection[b.name, t], vars.curtail[b.name, t], 1.0)
-        add_to_expression!(model.obj,
-                           vars.curtail[b.name, t],
-                           model.instance.power_balance_penalty[t])
-    end
-end
-
-
-function add_price_sensitive_load!(model::UnitCommitmentModel, ps::PriceSensitiveLoad)
-    mip, vars = model.mip, model.vars
-    for t in 1:model.instance.time
-        # Decision variable
-        vars.loads[ps.name, t] = @variable(mip, lower_bound=0, upper_bound=ps.demand[t])
-        
-        # Objective function terms
-        add_to_expression!(model.obj, vars.loads[ps.name, t], -ps.revenue[t])
-        
-        # Net injection
-        add_to_expression!(model.exprs.net_injection[ps.bus.name, t], vars.loads[ps.name, t], -1.0)
-    end
-end
-
-
-function add_unit!(model::UnitCommitmentModel, g::Unit)
-    mip, vars, eqs, exprs, T = model.mip, model.vars, model.eqs, model.exprs, model.instance.time
-    gi, K, S = g.name, length(g.cost_segments), length(g.startup_categories)
-    
-    if !all(g.must_run) && any(g.must_run)
-        error("Partially must-run units are not currently supported")
-    end
-    
-    if g.initial_power === nothing || g.initial_status === nothing
-        error("Initial conditions for $(g.name) must be provided")
-    end
-
-    is_initially_on = (g.initial_status > 0 ? 1.0 : 0.0)
-
-    # Decision variables
-    for t in 1:T
-        for k in 1:K
-            model.vars.segprod[gi, t, k] = @variable(model.mip, lower_bound=0)
-        end
-        model.vars.prod_above[gi, t] = @variable(model.mip, lower_bound=0)
-        if g.provides_spinning_reserves[t]
-            model.vars.reserve[gi, t] = @variable(model.mip, lower_bound=0)
-        else
-            model.vars.reserve[gi, t] = 0.0
-        end
-        for s in 1:S
-            model.vars.startup[gi, t, s] = @variable(model.mip, binary=true)
-        end
-        if g.must_run[t]
-            model.vars.is_on[gi, t] = 1.0
-            model.vars.switch_on[gi, t] = (t == 1 ? 1.0 - is_initially_on : 0.0)
-            model.vars.switch_off[gi, t] = 0.0
-        else
-            model.vars.is_on[gi, t] = @variable(model.mip, binary=true)
-            model.vars.switch_on[gi, t] = @variable(model.mip, binary=true)
-            model.vars.switch_off[gi, t] = @variable(model.mip, binary=true)
-        end
-    end
-
-    for t in 1:T
-        # Time-dependent start-up costs
-        for s in 1:S
-            # If unit is switching on, we must choose a startup category
-            eqs.startup_choose[gi, t, s] =
-                @constraint(mip, vars.switch_on[gi, t] == sum(vars.startup[gi, t, s] for s in 1:S))
-            
-            # If unit has not switched off in the last `delay` time periods, startup category is forbidden.
-            # The last startup category is always allowed.
-            if s < S
-                range = (t - g.startup_categories[s + 1].delay + 1):(t - g.startup_categories[s].delay)
-                initial_sum = (g.initial_status < 0 && (g.initial_status + 1 in range) ? 1.0 : 0.0)
-                eqs.startup_restrict[gi, t, s] =
-                    @constraint(mip, vars.startup[gi, t, s]
-                                        <= initial_sum + sum(vars.switch_off[gi, i] for i in range if i >= 1))
-            end
-            
-            # Objective function terms for start-up costs
-            add_to_expression!(model.obj,
-                               vars.startup[gi, t, s],
-                               g.startup_categories[s].cost)
-        end
-        
-        # Objective function terms for production costs
-        add_to_expression!(model.obj, vars.is_on[gi, t], g.min_power_cost[t])
-        for k in 1:K
-            add_to_expression!(model.obj, vars.segprod[gi, t, k], g.cost_segments[k].cost[t])
-        end
-
-        # Production limits (piecewise-linear segments)
-        for k in 1:K
-            eqs.segprod_limit[gi, t, k] =
-                @constraint(mip, vars.segprod[gi, t, k] <= g.cost_segments[k].mw[t] * vars.is_on[gi, t])
-        end
-
-        # Definition of production
-        eqs.prod_above_def[gi, t] =
-            @constraint(mip, vars.prod_above[gi, t] == sum(vars.segprod[gi, t, k] for k in 1:K))
-
-        # Production limit
-        eqs.prod_limit[gi, t] =
-            @constraint(mip,
-                        vars.prod_above[gi, t] + vars.reserve[gi, t]
-                                <= (g.max_power[t] - g.min_power[t]) * vars.is_on[gi, t])
-
-        # Binary variable equations for economic units
-        if !g.must_run[t]
-            
-            # Link binary variables
-            if t == 1
-                eqs.binary_link[gi, t] =
-                    @constraint(mip,
-                                vars.is_on[gi, t] - is_initially_on ==
-                                    vars.switch_on[gi, t] - vars.switch_off[gi, t])
-            else
-                eqs.binary_link[gi, t] =
-                    @constraint(mip,
-                                vars.is_on[gi, t] - vars.is_on[gi, t-1] ==
-                                    vars.switch_on[gi, t] - vars.switch_off[gi, t])
-            end
-
-            # Cannot switch on and off at the same time
-            eqs.switch_on_off[gi, t] =
-                @constraint(mip, vars.switch_on[gi, t] + vars.switch_off[gi, t] <= 1)
-        end
-        
-        # Ramp up limit
-        if t == 1
-            if is_initially_on == 1
-                eqs.ramp_up[gi, t] =
-                    @constraint(mip,
-                                vars.prod_above[gi, t] + vars.reserve[gi, t] <=
-                                        (g.initial_power - g.min_power[t]) + g.ramp_up_limit)
-            end
-        else
-            eqs.ramp_up[gi, t] = 
-                @constraint(mip,
-                            vars.prod_above[gi, t] + vars.reserve[gi, t] <=
-                                    vars.prod_above[gi, t-1] + g.ramp_up_limit)
-        end
-        
-        # Ramp down limit
-        if t == 1
-            if is_initially_on == 1
-                eqs.ramp_down[gi, t] =
-                    @constraint(mip,
-                                vars.prod_above[gi, t] >=
-                                        (g.initial_power - g.min_power[t]) - g.ramp_down_limit)
-            end
-        else
-            eqs.ramp_down[gi, t] =
-                @constraint(mip,
-                            vars.prod_above[gi, t] >=
-                                vars.prod_above[gi, t-1] - g.ramp_down_limit)
-        end
-        
-        # Startup limit
-        eqs.startup_limit[gi, t] = 
-            @constraint(mip,
-                        vars.prod_above[gi, t] + vars.reserve[gi, t] <=
-                            (g.max_power[t] - g.min_power[t]) * vars.is_on[gi, t]
-                            - max(0, g.max_power[t] - g.startup_limit) * vars.switch_on[gi, t])
-        
-        # Shutdown limit
-        if g.initial_power > g.shutdown_limit
-            eqs.shutdown_limit[gi, 0] = 
-                @constraint(mip, vars.switch_off[gi, 1] <= 0)
-        end
-        if t < T
-            eqs.shutdown_limit[gi, t] =
-                @constraint(mip,
-                            vars.prod_above[gi, t] <=
-                                (g.max_power[t] - g.min_power[t]) * vars.is_on[gi, t]
-                                - max(0, g.max_power[t] - g.shutdown_limit) * vars.switch_off[gi, t+1])
-        end
-        
-        # Minimum up-time
-        eqs.min_uptime[gi, t] =
-            @constraint(mip, 
-                        sum(vars.switch_on[gi, i]
-                            for i in (t - g.min_uptime + 1):t if i >= 1
-                           ) <= vars.is_on[gi, t])
-        
-        # # Minimum down-time
-        eqs.min_downtime[gi, t] =
-            @constraint(mip,
-                        sum(vars.switch_off[gi, i]
-                            for i in (t - g.min_downtime + 1):t if i >= 1
-                           ) <= 1 - vars.is_on[gi, t])
-        
-        # Minimum up/down-time for initial periods
-        if t == 1
-            if g.initial_status > 0
-                eqs.min_uptime[gi, 0] =
-                    @constraint(mip, sum(vars.switch_off[gi, i]
-                                         for i in 1:(g.min_uptime - g.initial_status) if i <= T) == 0)
-            else
-                eqs.min_downtime[gi, 0] =
-                    @constraint(mip, sum(vars.switch_on[gi, i]
-                                         for i in 1:(g.min_downtime + g.initial_status) if i <= T) == 0)
-            end
-        end
-        
-        # Add to net injection expression
-        add_to_expression!(exprs.net_injection[g.bus.name, t], vars.prod_above[g.name, t], 1.0)
-        add_to_expression!(exprs.net_injection[g.bus.name, t], vars.is_on[g.name, t], g.min_power[t])
-        
-        # Add to reserves expression
-        add_to_expression!(exprs.reserve[g.bus.name, t], vars.reserve[gi, t], 1.0)
-    end
-end
-
-
-function build_obj_function!(model::UnitCommitmentModel)
-    @objective(model.mip, Min, model.obj)
-end
-
-
-function build_net_injection_eqs!(model::UnitCommitmentModel)
-    T = model.instance.time
-    for t in 1:T, b in model.instance.buses
-        net = model.vars.net_injection[b.name, t] = @variable(model.mip)
-        model.eqs.net_injection_def[t, b.name] =
-            @constraint(model.mip, net == model.exprs.net_injection[b.name, t])
-    end
-    for t in 1:T
-        model.eqs.power_balance[t] =
-            @constraint(model.mip, sum(model.vars.net_injection[b.name, t]
-                                       for b in model.instance.buses) == 0)
-    end
-end
-
-
-function build_reserve_eqs!(model::UnitCommitmentModel)
-    reserves = model.instance.reserves
-    for t in 1:model.instance.time
-        model.eqs.min_reserve[t] =
-            @constraint(model.mip, sum(model.exprs.reserve[b.name, t]
-                                       for b in model.instance.buses) >= reserves.spinning[t])
-    end
-end
-
-
-function enforce_transmission(;
-                              model::UnitCommitmentModel,
-                              violation::Violation,
-                              isf::Array{Float64,2},
-                              lodf::Array{Float64,2})::Nothing
-    
-    instance, mip, vars = model.instance, model.mip, model.vars
-    limit::Float64 = 0.0
-        
-    if violation.outage_line === nothing
-        limit = violation.monitored_line.normal_flow_limit[violation.time]
-        @info @sprintf("    %8.3f MW overflow in %-5s time %3d (pre-contingency)",
-                       violation.amount,
-                       violation.monitored_line.name,
-                       violation.time)
-    else
-        limit = violation.monitored_line.emergency_flow_limit[violation.time]
-        @info @sprintf("    %8.3f MW overflow in %-5s time %3d (outage: line %s)",
-                       violation.amount,
-                       violation.monitored_line.name,
-                       violation.time,
-                       violation.outage_line.name)
-    end
-    
-    fm = violation.monitored_line.name
-    t = violation.time
-    flow = @variable(mip, base_name="flow[$fm,$t]")
-    
-    overflow = vars.overflow[violation.monitored_line.name, violation.time]
-    @constraint(mip, flow  <= limit + overflow)
-    @constraint(mip, -flow <= limit + overflow)
-    
-    if violation.outage_line === nothing
-        @constraint(mip, flow == sum(vars.net_injection[b.name, violation.time] *
-                                         isf[violation.monitored_line.offset, b.offset]
-                                     for b in instance.buses
-                                     if b.offset > 0))
-    else
-        @constraint(mip, flow == sum(vars.net_injection[b.name, violation.time] * (
-                                         isf[violation.monitored_line.offset, b.offset] + (
-                                             lodf[violation.monitored_line.offset, violation.outage_line.offset] *
-                                             isf[violation.outage_line.offset, b.offset]
-                                         )
-                                     )
-                                     for b in instance.buses
-                                     if b.offset > 0))
-    end
-    nothing
-end
-
-
-function set_variable_names!(model::UnitCommitmentModel)
-    @info "Setting variable and constraint names..."
-    time_varnames = @elapsed begin
-        set_jump_names!(model.vars)
-        set_jump_names!(model.eqs)
-    end
-    @info @sprintf("Set names in %.2f seconds", time_varnames)
-end
-
-
-function set_jump_names!(dict)
-    for name in keys(dict)
-        for idx in keys(dict[name])
-            idx_str = join(map(string, idx), ",")
-            set_name(dict[name][idx], "$name[$idx_str]")
-        end
-    end
-end
-
-
-function get_solution(model::UnitCommitmentModel)
-    instance, T = model.instance, model.instance.time
-    function timeseries(vars, collection)
-        return OrderedDict(b.name => [round(value(vars[b.name, t]), digits=5) for t in 1:T]
-                           for b in collection)
-    end
-    function production_cost(g)
-        return [value(model.vars.is_on[g.name, t]) * g.min_power_cost[t] +
-                sum(Float64[value(model.vars.segprod[g.name, t, k]) * g.cost_segments[k].cost[t]
-                            for k in 1:length(g.cost_segments)])
-                for t in 1:T]
-    end
-    function production(g)
-        return [value(model.vars.is_on[g.name, t]) * g.min_power[t] +
-                sum(Float64[value(model.vars.segprod[g.name, t, k])
-                            for k in 1:length(g.cost_segments)])
-                for t in 1:T]
-    end
-    function startup_cost(g)
-        S = length(g.startup_categories)
-        return [sum(g.startup_categories[s].cost * value(model.vars.startup[g.name, t, s])
-                    for s in 1:S)
-                for t in 1:T]
-    end
-    sol = OrderedDict()
-    sol["Production (MW)"] = OrderedDict(g.name => production(g) for g in instance.units)
-    sol["Production cost (\$)"] = OrderedDict(g.name => production_cost(g) for g in instance.units)
-    sol["Startup cost (\$)"] = OrderedDict(g.name => startup_cost(g) for g in instance.units)
-    sol["Is on"] = timeseries(model.vars.is_on, instance.units)
-    sol["Switch on"] = timeseries(model.vars.switch_on, instance.units)
-    sol["Switch off"] = timeseries(model.vars.switch_off, instance.units)
-    sol["Reserve (MW)"] = timeseries(model.vars.reserve, instance.units)
-    sol["Net injection (MW)"] = timeseries(model.vars.net_injection, instance.buses)
-    sol["Load curtail (MW)"] = timeseries(model.vars.curtail, instance.buses)
-    if !isempty(instance.lines)
-        sol["Line overflow (MW)"] = timeseries(model.vars.overflow, instance.lines)
-    end
-    if !isempty(instance.price_sensitive_loads)
-        sol["Price-sensitive loads (MW)"] = timeseries(model.vars.loads, instance.price_sensitive_loads)
-    end
-    return sol
-end
-
-
-function fix!(model::UnitCommitmentModel, solution)::Nothing
-    vars, instance, T = model.vars, model.instance, model.instance.time
-    for g in instance.units
-        for t in 1:T
-            is_on = round(solution["Is on"][g.name][t])
-            production = round(solution["Production (MW)"][g.name][t], digits=5)
-            reserve = round(solution["Reserve (MW)"][g.name][t], digits=5)
-            JuMP.fix(vars.is_on[g.name, t], is_on, force=true)
-            JuMP.fix(vars.prod_above[g.name, t], production - is_on * g.min_power[t], force=true)
-            JuMP.fix(vars.reserve[g.name, t], reserve, force=true)
-        end
-    end
-end
-
-
-function set_warm_start!(model::UnitCommitmentModel, solution)::Nothing
-    vars, instance, T = model.vars, model.instance, model.instance.time
-    for g in instance.units
-        for t in 1:T
-            JuMP.set_start_value(vars.is_on[g.name, t], solution["Is on"][g.name][t])
-            JuMP.set_start_value(vars.switch_on[g.name, t], solution["Switch on"][g.name][t])
-            JuMP.set_start_value(vars.switch_off[g.name, t], solution["Switch off"][g.name][t])
-        end
-    end
-end
-
-
-function optimize!(model::UnitCommitmentModel;
-                   time_limit=3600,
-                   gap_limit=1e-4,
-                   two_phase_gap=true,
-                   )::Nothing
-    
-    function set_gap(gap)
-        try
-            JuMP.set_optimizer_attribute(model.mip, "MIPGap", gap)
-            @info @sprintf("MIP gap tolerance set to %f", gap)
-        catch
-            @warn "Could not change MIP gap tolerance"
-        end
-    end
-    
-    instance = model.instance
-    initial_time = time()
-    
-    large_gap = false
-    has_transmission = (length(model.isf) > 0)
-    
-    if has_transmission && two_phase_gap
-        set_gap(1e-2)
-        large_gap = true
-    else
-        set_gap(gap_limit)
-    end
-    
-    while true
-        time_elapsed = time() - initial_time
-        time_remaining = time_limit - time_elapsed
-        if time_remaining < 0
-            @info "Time limit exceeded"
-            break
-        end
-        
-        @info @sprintf("Setting MILP time limit to %.2f seconds", time_remaining)
-        JuMP.set_time_limit_sec(model.mip, time_remaining)
-        
-        @info "Solving MILP..."
-        JuMP.optimize!(model.mip)
-        
-        has_transmission || break
-        
-        violations = find_violations(model)
-        if isempty(violations)
-            @info "No violations found" 
-            if large_gap
-                large_gap = false
-                set_gap(gap_limit)
-            else
-                break
-            end
-        else
-            enforce_transmission(model, violations)
-        end
-    end
-    
-    nothing
-end
-
-
-function find_violations(model::UnitCommitmentModel)
-    instance, vars = model.instance, model.vars
-    length(instance.buses) > 1 || return []
-    violations = []
-    @info "Verifying transmission limits..."
-    time_screening = @elapsed begin
-        non_slack_buses = [b for b in instance.buses if b.offset > 0]
-        net_injections = [value(vars.net_injection[b.name, t])
-                          for b in non_slack_buses, t in 1:instance.time]
-        overflow = [value(vars.overflow[lm.name, t])
-                    for lm in instance.lines, t in 1:instance.time]
-        violations = UnitCommitment.find_violations(instance=instance,
-                                                    net_injections=net_injections,
-                                                    overflow=overflow,
-                                                    isf=model.isf,
-                                                    lodf=model.lodf)
-    end
-    @info @sprintf("Verified transmission limits in %.2f seconds", time_screening)
-    return violations
-end
-
-
-function enforce_transmission(model::UnitCommitmentModel, violations::Array{Violation, 1})
-    for v in violations
-        enforce_transmission(model=model,
-                             violation=v,
-                             isf=model.isf,
-                             lodf=model.lodf)
-    end
-end
-
-
-export UnitCommitmentModel, build_model, get_solution, optimize!

src/model/build.jl

@@ -0,0 +1,64 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP, MathOptInterface, DataStructures
+import JuMP: value, fix, set_name
+
+"""
+    function build_model(;
+        instance::UnitCommitmentInstance,
+        optimizer = nothing,
+        variable_names::Bool = false,
+    )::JuMP.Model
+
+Build the JuMP model corresponding to the given unit commitment instance.
+
+Arguments
+=========
+- `instance`:
+    the instance.
+- `optimizer`:
+    the optimizer factory that should be attached to this model (e.g. Cbc.Optimizer).
+    If not provided, no optimizer will be attached.
+- `variable_names`: 
+    If true, set variable and constraint names. Important if the model is going
+    to be exported to an MPS file. For large models, this can take significant
+    time, so it's disabled by default.
+"""
+function build_model(;
+    instance::UnitCommitmentInstance,
+    optimizer = nothing,
+    formulation = Formulation(),
+    variable_names::Bool = false,
+)::JuMP.Model
+    @info "Building model..."
+    time_model = @elapsed begin
+        model = Model()
+        if optimizer !== nothing
+            set_optimizer(model, optimizer)
+        end
+        model[:obj] = AffExpr()
+        model[:instance] = instance
+        _setup_transmission(model, formulation.transmission)
+        for l in instance.lines
+            _add_transmission_line!(model, l, formulation.transmission)
+        end
+        for b in instance.buses
+            _add_bus!(model, b)
+        end
+        for g in instance.units
+            _add_unit!(model, g, formulation)
+        end
+        for ps in instance.price_sensitive_loads
+            _add_price_sensitive_load!(model, ps)
+        end
+        _add_system_wide_eqs!(model)
+        @objective(model, Min, model[:obj])
+    end
+    @info @sprintf("Built model in %.2f seconds", time_model)
+    if variable_names
+        _set_names!(model)
+    end
+    return model
+end

src/model/formulations/ArrCon2000/ramp.jl

@@ -0,0 +1,98 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_ramp_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_ramping::ArrCon2000.Ramping,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    # TODO: Move upper case constants to model[:instance]
+    RESERVES_WHEN_START_UP = true
+    RESERVES_WHEN_RAMP_UP = true
+    RESERVES_WHEN_RAMP_DOWN = true
+    RESERVES_WHEN_SHUT_DOWN = true
+    gn = g.name
+    RU = g.ramp_up_limit
+    RD = g.ramp_down_limit
+    SU = g.startup_limit
+    SD = g.shutdown_limit
+    reserve = model[:reserve]
+    eq_ramp_down = _init(model, :eq_ramp_down)
+    eq_ramp_up = _init(model, :eq_ramp_up)
+    is_initially_on = (g.initial_status > 0)
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    # Gar1962.StatusVars
+    is_on = model[:is_on]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+
+    for t in 1:model[:instance].time
+        # Ramp up limit
+        if t == 1
+            if is_initially_on
+                # min power is _not_ multiplied by is_on because if !is_on, then ramp up is irrelevant
+                eq_ramp_up[gn, t] = @constraint(
+                    model,
+                    g.min_power[t] +
+                    prod_above[gn, t] +
+                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) <=
+                    g.initial_power + RU
+                )
+            end
+        else
+            max_prod_this_period =
+                g.min_power[t] * is_on[gn, t] +
+                prod_above[gn, t] +
+                (
+                    RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
+                    reserve[gn, t] : 0.0
+                )
+            min_prod_last_period =
+                g.min_power[t-1] * is_on[gn, t-1] + prod_above[gn, t-1]
+
+            # Equation (24) in Kneuven et al. (2020)
+            eq_ramp_up[gn, t] = @constraint(
+                model,
+                max_prod_this_period - min_prod_last_period <=
+                RU * is_on[gn, t-1] + SU * switch_on[gn, t]
+            )
+        end
+
+        # Ramp down limit
+        if t == 1
+            if is_initially_on
+                # TODO If RD < SD, or more specifically if
+                #      min_power + RD < initial_power < SD
+                #      then the generator should be able to shut down at time t = 1,
+                #      but the constraint below will force the unit to produce power
+                eq_ramp_down[gn, t] = @constraint(
+                    model,
+                    g.initial_power - (g.min_power[t] + prod_above[gn, t]) <= RD
+                )
+            end
+        else
+            max_prod_last_period =
+                g.min_power[t-1] * is_on[gn, t-1] +
+                prod_above[gn, t-1] +
+                (
+                    RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN ?
+                    reserve[gn, t-1] : 0.0
+                )
+            min_prod_this_period =
+                g.min_power[t] * is_on[gn, t] + prod_above[gn, t]
+
+            # Equation (25) in Kneuven et al. (2020)
+            eq_ramp_down[gn, t] = @constraint(
+                model,
+                max_prod_last_period - min_prod_this_period <=
+                RD * is_on[gn, t] + SD * switch_off[gn, t]
+            )
+        end
+    end
+end

src/model/formulations/ArrCon2000/structs.jl

@@ -0,0 +1,18 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Arroyo, J. M., & Conejo, A. J. (2000). Optimal response of a thermal unit
+    to an electricity spot market. IEEE Transactions on power systems, 15(3), 
+    1098-1104. DOI: https://doi.org/10.1109/59.871739
+"""
+module ArrCon2000
+
+import ..RampingFormulation
+
+struct Ramping <: RampingFormulation end
+
+end

src/model/formulations/CarArr2006/pwlcosts.jl

@@ -0,0 +1,54 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_production_piecewise_linear_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_pwl_costs::CarArr2006.PwlCosts,
+    formulation_status_vars::StatusVarsFormulation,
+)::Nothing
+    eq_prod_above_def = _init(model, :eq_prod_above_def)
+    eq_segprod_limit = _init(model, :eq_segprod_limit)
+    segprod = model[:segprod]
+    gn = g.name
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    K = length(g.cost_segments)
+    for t in 1:model[:instance].time
+        gn = g.name
+        for k in 1:K
+            # Equation (45) in Kneuven et al. (2020)
+            # NB: when reading instance, UnitCommitment.jl already calculates
+            #     difference between max power for segments k and k-1 so the
+            #     value of cost_segments[k].mw[t] is the max production *for
+            #     that segment*
+            eq_segprod_limit[gn, t, k] = @constraint(
+                model,
+                segprod[gn, t, k] <= g.cost_segments[k].mw[t]
+            )
+
+            # Also add this as an explicit upper bound on segprod to make the
+            # solver's work a bit easier
+            set_upper_bound(segprod[gn, t, k], g.cost_segments[k].mw[t])
+
+            # Definition of production
+            # Equation (43) in Kneuven et al. (2020)
+            eq_prod_above_def[gn, t] = @constraint(
+                model,
+                prod_above[gn, t] == sum(segprod[gn, t, k] for k in 1:K)
+            )
+
+            # Objective function
+            # Equation (44) in Kneuven et al. (2020)
+            add_to_expression!(
+                model[:obj],
+                segprod[gn, t, k],
+                g.cost_segments[k].cost[t],
+            )
+        end
+    end
+end

src/model/formulations/CarArr2006/structs.jl

@@ -0,0 +1,19 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Carrión, M., & Arroyo, J. M. (2006). A computationally efficient
+    mixed-integer linear formulation for the thermal unit commitment problem.
+    IEEE Transactions on power systems, 21(3), 1371-1378.
+    DOI: https://doi.org/10.1109/TPWRS.2006.876672
+"""
+module CarArr2006
+
+import ..PiecewiseLinearCostsFormulation
+
+struct PwlCosts <: PiecewiseLinearCostsFormulation end
+
+end

src/model/formulations/DamKucRajAta2016/ramp.jl

@@ -0,0 +1,122 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_ramp_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_ramping::DamKucRajAta2016.Ramping,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    # TODO: Move upper case constants to model[:instance]
+    RESERVES_WHEN_START_UP = true
+    RESERVES_WHEN_RAMP_UP = true
+    RESERVES_WHEN_RAMP_DOWN = true
+    RESERVES_WHEN_SHUT_DOWN = true
+    known_initial_conditions = true
+    is_initially_on = (g.initial_status > 0)
+    SU = g.startup_limit
+    SD = g.shutdown_limit
+    RU = g.ramp_up_limit
+    RD = g.ramp_down_limit
+    gn = g.name
+    eq_str_ramp_down = _init(model, :eq_str_ramp_down)
+    eq_str_ramp_up = _init(model, :eq_str_ramp_up)
+    reserve = model[:reserve]
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    # Gar1962.StatusVars
+    is_on = model[:is_on]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+
+    for t in 1:model[:instance].time
+        time_invariant =
+            (t > 1) ? (abs(g.min_power[t] - g.min_power[t-1]) < 1e-7) : true
+
+        # if t > 1 && !time_invariant
+        #     @warn(
+        #         "Ramping according to Damcı-Kurt et al. (2016) requires " *
+        #         "time-invariant minimum power. This does not hold for " *
+        #         "generator $(gn): min_power[$t] = $(g.min_power[t]); " *
+        #         "min_power[$(t-1)] = $(g.min_power[t-1]). Reverting to " *
+        #         "Arroyo and Conejo (2000) formulation for this generator.",
+        #     )
+        # end
+
+        max_prod_this_period =
+            prod_above[gn, t] + (
+                RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
+                reserve[gn, t] : 0.0
+            )
+        min_prod_last_period = 0.0
+        if t > 1 && time_invariant
+            min_prod_last_period = prod_above[gn, t-1]
+
+            # Equation (35) in Kneuven et al. (2020)
+            # Sparser version of (24)
+            eq_str_ramp_up[gn, t] = @constraint(
+                model,
+                max_prod_this_period - min_prod_last_period <=
+                (SU - g.min_power[t] - RU) * switch_on[gn, t] +
+                RU * is_on[gn, t]
+            )
+        elseif (t == 1 && is_initially_on) || (t > 1 && !time_invariant)
+            if t > 1
+                min_prod_last_period =
+                    prod_above[gn, t-1] + g.min_power[t-1] * is_on[gn, t-1]
+            else
+                min_prod_last_period = max(g.initial_power, 0.0)
+            end
+
+            # Add the min prod at time t back in to max_prod_this_period to get _total_ production
+            # (instead of using the amount above minimum, as min prod for t < 1 is unknown)
+            max_prod_this_period += g.min_power[t] * is_on[gn, t]
+
+            # Modified version of equation (35) in Kneuven et al. (2020)
+            # Equivalent to (24)
+            eq_str_ramp_up[gn, t] = @constraint(
+                model,
+                max_prod_this_period - min_prod_last_period <=
+                (SU - RU) * switch_on[gn, t] + RU * is_on[gn, t]
+            )
+        end
+
+        max_prod_last_period =
+            min_prod_last_period + (
+                t > 1 && (RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN) ?
+                reserve[gn, t-1] : 0.0
+            )
+        min_prod_this_period = prod_above[gn, t]
+        on_last_period = 0.0
+        if t > 1
+            on_last_period = is_on[gn, t-1]
+        elseif (known_initial_conditions && g.initial_status > 0)
+            on_last_period = 1.0
+        end
+
+        if t > 1 && time_invariant
+            # Equation (36) in Kneuven et al. (2020)
+            eq_str_ramp_down[gn, t] = @constraint(
+                model,
+                max_prod_last_period - min_prod_this_period <=
+                (SD - g.min_power[t] - RD) * switch_off[gn, t] +
+                RD * on_last_period
+            )
+        elseif (t == 1 && is_initially_on) || (t > 1 && !time_invariant)
+            # Add back in min power
+            min_prod_this_period += g.min_power[t] * is_on[gn, t]
+
+            # Modified version of equation (36) in Kneuven et al. (2020)
+            # Equivalent to (25)
+            eq_str_ramp_down[gn, t] = @constraint(
+                model,
+                max_prod_last_period - min_prod_this_period <=
+                (SD - RD) * switch_off[gn, t] + RD * on_last_period
+            )
+        end
+    end
+end

src/model/formulations/DamKucRajAta2016/structs.jl

@@ -0,0 +1,18 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Damcı-Kurt, P., Küçükyavuz, S., Rajan, D., & Atamtürk, A. (2016). A polyhedral
+    study of production ramping. Mathematical Programming, 158(1), 175-205.
+    DOI: https://doi.org/10.1007/s10107-015-0919-9
+"""
+module DamKucRajAta2016
+
+import ..RampingFormulation
+
+struct Ramping <: RampingFormulation end
+
+end

src/model/formulations/Gar1962/prod.jl

@@ -0,0 +1,50 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_production_vars!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+)::Nothing
+    prod_above = _init(model, :prod_above)
+    segprod = _init(model, :segprod)
+    for t in 1:model[:instance].time
+        for k in 1:length(g.cost_segments)
+            segprod[g.name, t, k] = @variable(model, lower_bound = 0)
+        end
+        prod_above[g.name, t] = @variable(model, lower_bound = 0)
+    end
+    return
+end
+
+function _add_production_limit_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+)::Nothing
+    eq_prod_limit = _init(model, :eq_prod_limit)
+    is_on = model[:is_on]
+    prod_above = model[:prod_above]
+    reserve = model[:reserve]
+    gn = g.name
+    for t in 1:model[:instance].time
+        # Objective function terms for production costs
+        # Part of (69) of Kneuven et al. (2020) as C^R_g * u_g(t) term
+        add_to_expression!(model[:obj], is_on[gn, t], g.min_power_cost[t])
+
+        # Production limit
+        # Equation (18) in Kneuven et al. (2020)
+        #   as \bar{p}_g(t) \le \bar{P}_g u_g(t)
+        # amk: this is a weaker version of (20) and (21) in Kneuven et al. (2020)
+        #      but keeping it here in case those are not present
+        power_diff = max(g.max_power[t], 0.0) - max(g.min_power[t], 0.0)
+        if power_diff < 1e-7
+            power_diff = 0.0
+        end
+        eq_prod_limit[gn, t] = @constraint(
+            model,
+            prod_above[gn, t] + reserve[gn, t] <= power_diff * is_on[gn, t]
+        )
+    end
+end

src/model/formulations/Gar1962/pwlcosts.jl

@@ -0,0 +1,59 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_production_piecewise_linear_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_pwl_costs::Gar1962.PwlCosts,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    eq_prod_above_def = _init(model, :eq_prod_above_def)
+    eq_segprod_limit = _init(model, :eq_segprod_limit)
+    segprod = model[:segprod]
+    gn = g.name
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    # Gar1962.StatusVars
+    is_on = model[:is_on]
+
+    K = length(g.cost_segments)
+    for t in 1:model[:instance].time
+        # Definition of production
+        # Equation (43) in Kneuven et al. (2020)
+        eq_prod_above_def[gn, t] = @constraint(
+            model,
+            prod_above[gn, t] == sum(segprod[gn, t, k] for k in 1:K)
+        )
+
+        for k in 1:K
+            # Equation (42) in Kneuven et al. (2020)
+            # Without this, solvers will add a lot of implied bound cuts to
+            # have this same effect.
+            # NB: when reading instance, UnitCommitment.jl already calculates
+            #     difference between max power for segments k and k-1 so the
+            #     value of cost_segments[k].mw[t] is the max production *for
+            #     that segment*
+            eq_segprod_limit[gn, t, k] = @constraint(
+                model,
+                segprod[gn, t, k] <= g.cost_segments[k].mw[t] * is_on[gn, t]
+            )
+
+            # Also add this as an explicit upper bound on segprod to make the
+            # solver's work a bit easier
+            set_upper_bound(segprod[gn, t, k], g.cost_segments[k].mw[t])
+
+            # Objective function
+            # Equation (44) in Kneuven et al. (2020)
+            add_to_expression!(
+                model[:obj],
+                segprod[gn, t, k],
+                g.cost_segments[k].cost[t],
+            )
+        end
+    end
+    return
+end

src/model/formulations/Gar1962/status.jl

@@ -0,0 +1,61 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_status_vars!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    is_on = _init(model, :is_on)
+    switch_on = _init(model, :switch_on)
+    switch_off = _init(model, :switch_off)
+    for t in 1:model[:instance].time
+        if g.must_run[t]
+            is_on[g.name, t] = 1.0
+            switch_on[g.name, t] = (t == 1 ? 1.0 - _is_initially_on(g) : 0.0)
+            switch_off[g.name, t] = 0.0
+        else
+            is_on[g.name, t] = @variable(model, binary = true)
+            switch_on[g.name, t] = @variable(model, binary = true)
+            switch_off[g.name, t] = @variable(model, binary = true)
+        end
+    end
+    return
+end
+
+function _add_status_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    eq_binary_link = _init(model, :eq_binary_link)
+    eq_switch_on_off = _init(model, :eq_switch_on_off)
+    is_on = model[:is_on]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+    for t in 1:model[:instance].time
+        if !g.must_run[t]
+            # Link binary variables
+            if t == 1
+                eq_binary_link[g.name, t] = @constraint(
+                    model,
+                    is_on[g.name, t] - _is_initially_on(g) ==
+                    switch_on[g.name, t] - switch_off[g.name, t]
+                )
+            else
+                eq_binary_link[g.name, t] = @constraint(
+                    model,
+                    is_on[g.name, t] - is_on[g.name, t-1] ==
+                    switch_on[g.name, t] - switch_off[g.name, t]
+                )
+            end
+            # Cannot switch on and off at the same time
+            eq_switch_on_off[g.name, t] = @constraint(
+                model,
+                switch_on[g.name, t] + switch_off[g.name, t] <= 1
+            )
+        end
+    end
+    return
+end

src/model/formulations/Gar1962/structs.jl

@@ -0,0 +1,24 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Garver, L. L. (1962). Power generation scheduling by integer
+    programming-development of theory. Transactions of the American Institute
+    of Electrical Engineers. Part III: Power Apparatus and Systems, 81(3), 730-734.
+    DOI: https://doi.org/10.1109/AIEEPAS.1962.4501405
+
+"""
+module Gar1962
+
+import ..PiecewiseLinearCostsFormulation
+import ..ProductionVarsFormulation
+import ..StatusVarsFormulation
+
+struct ProdVars <: ProductionVarsFormulation end
+struct PwlCosts <: PiecewiseLinearCostsFormulation end
+struct StatusVars <: StatusVarsFormulation end
+
+end

src/model/formulations/KnuOstWat2018/pwlcosts.jl

@@ -0,0 +1,108 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_production_piecewise_linear_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_pwl_costs::KnuOstWat2018.PwlCosts,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    eq_prod_above_def = _init(model, :eq_prod_above_def)
+    eq_segprod_limit_a = _init(model, :eq_segprod_limit_a)
+    eq_segprod_limit_b = _init(model, :eq_segprod_limit_b)
+    eq_segprod_limit_c = _init(model, :eq_segprod_limit_c)
+    segprod = model[:segprod]
+    gn = g.name
+    K = length(g.cost_segments)
+    T = model[:instance].time
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    # Gar1962.StatusVars
+    is_on = model[:is_on]
+    switch_on = model[:switch_on]
+    switch_off = model[:switch_off]
+
+    for t in 1:T
+        for k in 1:K
+            # Pbar^{k-1)
+            Pbar0 =
+                g.min_power[t] +
+                (k > 1 ? sum(g.cost_segments[ell].mw[t] for ell in 1:k-1) : 0.0)
+            # Pbar^k
+            Pbar1 = g.cost_segments[k].mw[t] + Pbar0
+
+            Cv = 0.0
+            SU = g.startup_limit   # startup rate
+            if Pbar1 <= SU
+                Cv = 0.0
+            elseif Pbar0 < SU # && Pbar1 > SU
+                Cv = Pbar1 - SU
+            else # Pbar0 >= SU
+                # this will imply that we cannot produce along this segment if
+                # switch_on = 1
+                Cv = g.cost_segments[k].mw[t]
+            end
+            Cw = 0.0
+            SD = g.shutdown_limit  # shutdown rate
+            if Pbar1 <= SD
+                Cw = 0.0
+            elseif Pbar0 < SD # && Pbar1 > SD
+                Cw = Pbar1 - SD
+            else # Pbar0 >= SD
+                Cw = g.cost_segments[k].mw[t]
+            end
+
+            if g.min_uptime > 1
+                # Equation (46) in Kneuven et al. (2020)
+                eq_segprod_limit_a[gn, t, k] = @constraint(
+                    model,
+                    segprod[gn, t, k] <=
+                    g.cost_segments[k].mw[t] * is_on[gn, t] -
+                    Cv * switch_on[gn, t] -
+                    (t < T ? Cw * switch_off[gn, t+1] : 0.0)
+                )
+            else
+                # Equation (47a)/(48a) in Kneuven et al. (2020)
+                eq_segprod_limit_b[gn, t, k] = @constraint(
+                    model,
+                    segprod[gn, t, k] <=
+                    g.cost_segments[k].mw[t] * is_on[gn, t] -
+                    Cv * switch_on[gn, t] -
+                    (t < T ? max(0, Cv - Cw) * switch_off[gn, t+1] : 0.0)
+                )
+
+                # Equation (47b)/(48b) in Kneuven et al. (2020)
+                eq_segprod_limit_c[gn, t, k] = @constraint(
+                    model,
+                    segprod[gn, t, k] <=
+                    g.cost_segments[k].mw[t] * is_on[gn, t] -
+                    max(0, Cw - Cv) * switch_on[gn, t] -
+                    (t < T ? Cw * switch_off[gn, t+1] : 0.0)
+                )
+            end
+
+            # Definition of production
+            # Equation (43) in Kneuven et al. (2020)
+            eq_prod_above_def[gn, t] = @constraint(
+                model,
+                prod_above[gn, t] == sum(segprod[gn, t, k] for k in 1:K)
+            )
+
+            # Objective function
+            # Equation (44) in Kneuven et al. (2020)
+            add_to_expression!(
+                model[:obj],
+                segprod[gn, t, k],
+                g.cost_segments[k].cost[t],
+            )
+
+            # Also add an explicit upper bound on segprod to make the solver's
+            # work a bit easier
+            set_upper_bound(segprod[gn, t, k], g.cost_segments[k].mw[t])
+        end
+    end
+end

src/model/formulations/KnuOstWat2018/structs.jl

@@ -0,0 +1,18 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Knueven, B., Ostrowski, J., & Watson, J. P. (2018). Exploiting identical
+    generators in unit commitment. IEEE Transactions on Power Systems, 33(4),
+    4496-4507. DOI: https://doi.org/10.1109/TPWRS.2017.2783850
+"""
+module KnuOstWat2018
+
+import ..PiecewiseLinearCostsFormulation
+
+struct PwlCosts <: PiecewiseLinearCostsFormulation end
+
+end

src/model/formulations/MorLatRam2013/ramp.jl

@@ -0,0 +1,130 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_ramp_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_ramping::MorLatRam2013.Ramping,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    # TODO: Move upper case constants to model[:instance]
+    RESERVES_WHEN_START_UP = true
+    RESERVES_WHEN_RAMP_UP = true
+    RESERVES_WHEN_RAMP_DOWN = true
+    RESERVES_WHEN_SHUT_DOWN = true
+    is_initially_on = (g.initial_status > 0)
+    SU = g.startup_limit
+    SD = g.shutdown_limit
+    RU = g.ramp_up_limit
+    RD = g.ramp_down_limit
+    gn = g.name
+    eq_ramp_down = _init(model, :eq_ramp_down)
+    eq_ramp_up = _init(model, :eq_str_ramp_up)
+    reserve = model[:reserve]
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    # Gar1962.StatusVars
+    is_on = model[:is_on]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+
+    for t in 1:model[:instance].time
+        time_invariant =
+            (t > 1) ? (abs(g.min_power[t] - g.min_power[t-1]) < 1e-7) : true
+
+        # Ramp up limit
+        if t == 1
+            if is_initially_on
+                eq_ramp_up[gn, t] = @constraint(
+                    model,
+                    g.min_power[t] +
+                    prod_above[gn, t] +
+                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) <=
+                    g.initial_power + RU
+                )
+            end
+        else
+            # amk: without accounting for time-varying min power terms,
+            #      we might get an infeasible schedule, e.g. if min_power[t-1] = 0, min_power[t] = 10
+            #      and ramp_up_limit = 5, the constraint (p'(t) + r(t) <= p'(t-1) + RU)
+            #      would be satisfied with p'(t) = r(t) = p'(t-1) = 0
+            #      Note that if switch_on[t] = 1, then eqns (20) or (21) go into effect
+            if !time_invariant
+                # Use equation (24) instead
+                SU = g.startup_limit
+                max_prod_this_period =
+                    g.min_power[t] * is_on[gn, t] +
+                    prod_above[gn, t] +
+                    (
+                        RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
+                        reserve[gn, t] : 0.0
+                    )
+                min_prod_last_period =
+                    g.min_power[t-1] * is_on[gn, t-1] + prod_above[gn, t-1]
+                eq_ramp_up[gn, t] = @constraint(
+                    model,
+                    max_prod_this_period - min_prod_last_period <=
+                    RU * is_on[gn, t-1] + SU * switch_on[gn, t]
+                )
+            else
+                # Equation (26) in Kneuven et al. (2020)
+                # TODO: what if RU < SU? places too stringent upper bound
+                # prod_above[gn, t] when starting up, and creates diff with (24).
+                eq_ramp_up[gn, t] = @constraint(
+                    model,
+                    prod_above[gn, t] +
+                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) -
+                    prod_above[gn, t-1] <= RU
+                )
+            end
+        end
+
+        # Ramp down limit
+        if t == 1
+            if is_initially_on
+                # TODO If RD < SD, or more specifically if
+                #        min_power + RD < initial_power < SD
+                #      then the generator should be able to shut down at time t = 1,
+                #      but the constraint below will force the unit to produce power
+                eq_ramp_down[gn, t] = @constraint(
+                    model,
+                    g.initial_power - (g.min_power[t] + prod_above[gn, t]) <= RD
+                )
+            end
+        else
+            # amk: similar to ramp_up, need to account for time-dependent min_power
+            if !time_invariant
+                # Revert to (25)
+                SD = g.shutdown_limit
+                max_prod_last_period =
+                    g.min_power[t-1] * is_on[gn, t-1] +
+                    prod_above[gn, t-1] +
+                    (
+                        RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN ?
+                        reserve[gn, t-1] : 0.0
+                    )
+                min_prod_this_period =
+                    g.min_power[t] * is_on[gn, t] + prod_above[gn, t]
+                eq_ramp_down[gn, t] = @constraint(
+                    model,
+                    max_prod_last_period - min_prod_this_period <=
+                    RD * is_on[gn, t] + SD * switch_off[gn, t]
+                )
+            else
+                # Equation (27) in Kneuven et al. (2020)
+                # TODO: Similar to above, what to do if shutting down in time t
+                # and RD < SD? There is a difference with (25).
+                eq_ramp_down[gn, t] = @constraint(
+                    model,
+                    prod_above[gn, t-1] +
+                    (RESERVES_WHEN_RAMP_DOWN ? reserve[gn, t-1] : 0.0) -
+                    prod_above[gn, t] <= RD
+                )
+            end
+        end
+    end
+end

src/model/formulations/MorLatRam2013/scosts.jl

@@ -0,0 +1,50 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_startup_cost_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation::MorLatRam2013.StartupCosts,
+)::Nothing
+    eq_startup_choose = _init(model, :eq_startup_choose)
+    eq_startup_restrict = _init(model, :eq_startup_restrict)
+    S = length(g.startup_categories)
+    startup = model[:startup]
+    for t in 1:model[:instance].time
+        # If unit is switching on, we must choose a startup category
+        eq_startup_choose[g.name, t] = @constraint(
+            model,
+            model[:switch_on][g.name, t] ==
+            sum(startup[g.name, t, s] for s in 1:S)
+        )
+
+        for s in 1:S
+            # If unit has not switched off in the last `delay` time periods, startup category is forbidden.
+            # The last startup category is always allowed.
+            if s < S
+                range_start = t - g.startup_categories[s+1].delay + 1
+                range_end = t - g.startup_categories[s].delay
+                range = (range_start:range_end)
+                initial_sum = (
+                    g.initial_status < 0 && (g.initial_status + 1 in range) ? 1.0 : 0.0
+                )
+                eq_startup_restrict[g.name, t, s] = @constraint(
+                    model,
+                    startup[g.name, t, s] <=
+                    initial_sum + sum(
+                        model[:switch_off][g.name, i] for i in range if i >= 1
+                    )
+                )
+            end
+
+            # Objective function terms for start-up costs
+            add_to_expression!(
+                model[:obj],
+                startup[g.name, t, s],
+                g.startup_categories[s].cost,
+            )
+        end
+    end
+    return
+end

src/model/formulations/MorLatRam2013/structs.jl

@@ -0,0 +1,20 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Morales-España, G., Latorre, J. M., & Ramos, A. (2013). Tight and compact
+    MILP formulation for the thermal unit commitment problem. IEEE Transactions
+    on Power Systems, 28(4), 4897-4908. DOI: https://doi.org/10.1109/TPWRS.2013.2251373
+"""
+module MorLatRam2013
+
+import ..RampingFormulation
+import ..StartupCostsFormulation
+
+struct Ramping <: RampingFormulation end
+struct StartupCosts <: StartupCostsFormulation end
+
+end

src/model/formulations/PanGua2016/ramp.jl

@@ -0,0 +1,107 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_ramp_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_prod_vars::Gar1962.ProdVars,
+    formulation_ramping::PanGua2016.Ramping,
+    formulation_status_vars::Gar1962.StatusVars,
+)::Nothing
+    # TODO: Move upper case constants to model[:instance]
+    RESERVES_WHEN_SHUT_DOWN = true
+    gn = g.name
+    reserve = model[:reserve]
+    eq_str_prod_limit = _init(model, :eq_str_prod_limit)
+    eq_prod_limit_ramp_up_extra_period =
+        _init(model, :eq_prod_limit_ramp_up_extra_period)
+    eq_prod_limit_shutdown_trajectory =
+        _init(model, :eq_prod_limit_shutdown_trajectory)
+    UT = g.min_uptime
+    SU = g.startup_limit   # startup rate, i.e., max production right after startup
+    SD = g.shutdown_limit  # shutdown rate, i.e., max production right before shutdown
+    RU = g.ramp_up_limit   # ramp up rate
+    RD = g.ramp_down_limit # ramp down rate
+    T = model[:instance].time
+
+    # Gar1962.ProdVars
+    prod_above = model[:prod_above]
+
+    # Gar1962.StatusVars
+    is_on = model[:is_on]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+
+    for t in 1:T
+        Pbar = g.max_power[t]
+        if Pbar < 1e-7
+            # Skip this time period if max power = 0
+            continue
+        end
+
+        #TRD = floor((Pbar - SU) / RD) # ramp down time
+        # TODO check amk changed TRD wrt Kneuven et al.
+        TRD = ceil((Pbar - SD) / RD)  # ramp down time
+        TRU = floor((Pbar - SU) / RU) # ramp up time, can be negative if Pbar < SU
+
+        # TODO check initial time periods: what if generator has been running for x periods?
+        # But maybe ok as long as (35) and (36) are also used...
+        if UT > 1
+            # Equation (38) in Kneuven et al. (2020)
+            # Generalization of (20)
+            # Necessary that if any of the switch_on = 1 in the sum,
+            # then switch_off[gn, t+1] = 0
+            eq_str_prod_limit[gn, t] = @constraint(
+                model,
+                prod_above[gn, t] +
+                g.min_power[t] * is_on[gn, t] +
+                reserve[gn, t] <=
+                Pbar * is_on[gn, t] -
+                (t < T ? (Pbar - SD) * switch_off[gn, t+1] : 0.0) - sum(
+                    (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
+                    i in 0:min(UT - 2, TRU, t - 1)
+                )
+            )
+
+            if UT - 2 < TRU
+                # Equation (40) in Kneuven et al. (2020)
+                # Covers an additional time period of the ramp-up trajectory, compared to (38)
+                eq_prod_limit_ramp_up_extra_period[gn, t] = @constraint(
+                    model,
+                    prod_above[gn, t] +
+                    g.min_power[t] * is_on[gn, t] +
+                    reserve[gn, t] <=
+                    Pbar * is_on[gn, t] - sum(
+                        (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
+                        i in 0:min(UT - 1, TRU, t - 1)
+                    )
+                )
+            end
+
+            # Add in shutdown trajectory if KSD >= 0 (else this is dominated by (38))
+            KSD = min(TRD, UT - 1, T - t - 1)
+            if KSD > 0
+                KSU = min(TRU, UT - 2 - KSD, t - 1)
+                # Equation (41) in Kneuven et al. (2020)
+                eq_prod_limit_shutdown_trajectory[gn, t] = @constraint(
+                    model,
+                    prod_above[gn, t] +
+                    g.min_power[t] * is_on[gn, t] +
+                    (RESERVES_WHEN_SHUT_DOWN ? reserve[gn, t] : 0.0) <=
+                    Pbar * is_on[gn, t] - sum(
+                        (Pbar - (SD + i * RD)) * switch_off[gn, t+1+i] for
+                        i in 0:KSD
+                    ) - sum(
+                        (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
+                        i in 0:KSU
+                    ) - (
+                        (KSU >= TRU || KSU > t - 2) ? 0.0 :
+                        max(0, (SU + (KSU + 1) * RU) - (SD + TRD * RD)) *
+                        switch_on[gn, t-(KSU+1)]
+                    )
+                )
+            end
+        end
+    end
+end

src/model/formulations/PanGua2016/structs.jl

@@ -0,0 +1,18 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Formulation described in:
+
+    Pan, K., & Guan, Y. (2016). Strong formulations for multistage stochastic
+    self-scheduling unit commitment. Operations Research, 64(6), 1482-1498.
+    DOI: https://doi.org/10.1287/opre.2016.1520
+"""
+module PanGua2016
+
+import ..RampingFormulation
+
+struct Ramping <: RampingFormulation end
+
+end

src/model/formulations/base/bus.jl

@@ -0,0 +1,24 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_bus!(model::JuMP.Model, b::Bus)::Nothing
+    net_injection = _init(model, :expr_net_injection)
+    curtail = _init(model, :curtail)
+    for t in 1:model[:instance].time
+        # Fixed load
+        net_injection[b.name, t] = AffExpr(-b.load[t])
+
+        # Load curtailment
+        curtail[b.name, t] =
+            @variable(model, lower_bound = 0, upper_bound = b.load[t])
+
+        add_to_expression!(net_injection[b.name, t], curtail[b.name, t], 1.0)
+        add_to_expression!(
+            model[:obj],
+            curtail[b.name, t],
+            model[:instance].power_balance_penalty[t],
+        )
+    end
+    return
+end

src/model/formulations/base/line.jl

@@ -0,0 +1,61 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_transmission_line!(
+    model::JuMP.Model,
+    lm::TransmissionLine,
+    f::ShiftFactorsFormulation,
+)::Nothing
+    overflow = _init(model, :overflow)
+    for t in 1:model[:instance].time
+        overflow[lm.name, t] = @variable(model, lower_bound = 0)
+        add_to_expression!(
+            model[:obj],
+            overflow[lm.name, t],
+            lm.flow_limit_penalty[t],
+        )
+    end
+    return
+end
+
+function _setup_transmission(
+    model::JuMP.Model,
+    formulation::ShiftFactorsFormulation,
+)::Nothing
+    instance = model[:instance]
+    isf = formulation.precomputed_isf
+    lodf = formulation.precomputed_lodf
+    if length(instance.buses) == 1
+        isf = zeros(0, 0)
+        lodf = zeros(0, 0)
+    elseif isf === nothing
+        @info "Computing injection shift factors..."
+        time_isf = @elapsed begin
+            isf = UnitCommitment._injection_shift_factors(
+                lines = instance.lines,
+                buses = instance.buses,
+            )
+        end
+        @info @sprintf("Computed ISF in %.2f seconds", time_isf)
+        @info "Computing line outage factors..."
+        time_lodf = @elapsed begin
+            lodf = UnitCommitment._line_outage_factors(
+                lines = instance.lines,
+                buses = instance.buses,
+                isf = isf,
+            )
+        end
+        @info @sprintf("Computed LODF in %.2f seconds", time_lodf)
+        @info @sprintf(
+            "Applying PTDF and LODF cutoffs (%.5f, %.5f)",
+            formulation.isf_cutoff,
+            formulation.lodf_cutoff
+        )
+        isf[abs.(isf).<formulation.isf_cutoff] .= 0
+        lodf[abs.(lodf).<formulation.lodf_cutoff] .= 0
+    end
+    model[:isf] = isf
+    model[:lodf] = lodf
+    return
+end

src/model/formulations/base/psload.jl

@@ -0,0 +1,27 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_price_sensitive_load!(
+    model::JuMP.Model,
+    ps::PriceSensitiveLoad,
+)::Nothing
+    loads = _init(model, :loads)
+    net_injection = _init(model, :expr_net_injection)
+    for t in 1:model[:instance].time
+        # Decision variable
+        loads[ps.name, t] =
+            @variable(model, lower_bound = 0, upper_bound = ps.demand[t])
+
+        # Objective function terms
+        add_to_expression!(model[:obj], loads[ps.name, t], -ps.revenue[t])
+
+        # Net injection
+        add_to_expression!(
+            net_injection[ps.bus.name, t],
+            loads[ps.name, t],
+            -1.0,
+        )
+    end
+    return
+end

src/model/formulations/base/sensitivity.jl

@@ -0,0 +1,83 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using SparseArrays, Base.Threads, LinearAlgebra, JuMP
+
+"""
+    _injection_shift_factors(; buses, lines)
+
+Returns a (B-1)xL matrix M, where B is the number of buses and L is the number
+of transmission lines. For a given bus b and transmission line l, the entry
+M[l.offset, b.offset] indicates the amount of power (in MW) that flows through
+transmission line l when 1 MW of power is injected at the slack bus (the bus
+that has offset zero) and withdrawn from b.
+"""
+function _injection_shift_factors(;
+    buses::Array{Bus},
+    lines::Array{TransmissionLine},
+)
+    susceptance = _susceptance_matrix(lines)
+    incidence = _reduced_incidence_matrix(lines = lines, buses = buses)
+    laplacian = transpose(incidence) * susceptance * incidence
+    isf = susceptance * incidence * inv(Array(laplacian))
+    return isf
+end
+
+"""
+    _reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{TransmissionLine})
+
+Returns the incidence matrix for the network, with the column corresponding to
+the slack bus is removed. More precisely, returns a (B-1) x L matrix, where B
+is the number of buses and L is the number of lines. For each row, there is a 1
+element and a -1 element, indicating the source and target buses, respectively,
+for that line.
+"""
+function _reduced_incidence_matrix(;
+    buses::Array{Bus},
+    lines::Array{TransmissionLine},
+)
+    matrix = spzeros(Float64, length(lines), length(buses) - 1)
+    for line in lines
+        if line.source.offset > 0
+            matrix[line.offset, line.source.offset] = 1
+        end
+        if line.target.offset > 0
+            matrix[line.offset, line.target.offset] = -1
+        end
+    end
+    return matrix
+end
+
+"""
+    _susceptance_matrix(lines::Array{TransmissionLine})
+
+Returns a LxL diagonal matrix, where each diagonal entry is the susceptance of
+the corresponding transmission line.
+"""
+function _susceptance_matrix(lines::Array{TransmissionLine})
+    return Diagonal([l.susceptance for l in lines])
+end
+
+"""
+
+    _line_outage_factors(; buses, lines, isf)
+
+Returns a LxL matrix containing the Line Outage Distribution Factors (LODFs)
+for the given network. This matrix how does the pre-contingency flow change
+when each individual transmission line is removed.
+"""
+function _line_outage_factors(;
+    buses::Array{Bus,1},
+    lines::Array{TransmissionLine,1},
+    isf::Array{Float64,2},
+)::Array{Float64,2}
+    incidence = Array(_reduced_incidence_matrix(lines = lines, buses = buses))
+    lodf::Array{Float64,2} = isf * transpose(incidence)
+    _, n = size(lodf)
+    for i in 1:n
+        lodf[:, i] *= 1.0 / (1.0 - lodf[i, i])
+        lodf[i, i] = -1
+    end
+    return lodf
+end

src/model/formulations/base/structs.jl

@@ -0,0 +1,78 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+abstract type TransmissionFormulation end
+abstract type RampingFormulation end
+abstract type PiecewiseLinearCostsFormulation end
+abstract type StartupCostsFormulation end
+abstract type StatusVarsFormulation end
+abstract type ProductionVarsFormulation end
+
+struct Formulation
+    prod_vars::ProductionVarsFormulation
+    pwl_costs::PiecewiseLinearCostsFormulation
+    ramping::RampingFormulation
+    startup_costs::StartupCostsFormulation
+    status_vars::StatusVarsFormulation
+    transmission::TransmissionFormulation
+
+    function Formulation(;
+        prod_vars::ProductionVarsFormulation = Gar1962.ProdVars(),
+        pwl_costs::PiecewiseLinearCostsFormulation = KnuOstWat2018.PwlCosts(),
+        ramping::RampingFormulation = MorLatRam2013.Ramping(),
+        startup_costs::StartupCostsFormulation = MorLatRam2013.StartupCosts(),
+        status_vars::StatusVarsFormulation = Gar1962.StatusVars(),
+        transmission::TransmissionFormulation = ShiftFactorsFormulation(),
+    )
+        return new(
+            prod_vars,
+            pwl_costs,
+            ramping,
+            startup_costs,
+            status_vars,
+            transmission,
+        )
+    end
+end
+
+"""
+    struct ShiftFactorsFormulation <: TransmissionFormulation
+        isf_cutoff::Float64
+        lodf_cutoff::Float64
+        precomputed_isf::Union{Nothing,Matrix{Float64}}
+        precomputed_lodf::Union{Nothing,Matrix{Float64}}
+    end
+
+Transmission formulation based on Injection Shift Factors (ISF) and Line
+Outage Distribution Factors (LODF). Constraints are enforced in a lazy way.
+
+Arguments
+---------
+- `precomputed_isf::Union{Matrix{Float64},Nothing} = nothing`:
+    the injection shift factors matrix. If not provided, it will be computed.
+- `precomputed_lodf::Union{Matrix{Float64},Nothing} = nothing`: 
+    the line outage distribution factors matrix. If not provided, it will be
+    computed.
+- `isf_cutoff::Float64 = 0.005`: 
+    the cutoff that should be applied to the ISF matrix. Entries with magnitude
+    smaller than this value will be set to zero.
+- `lodf_cutoff::Float64 = 0.001`: 
+    the cutoff that should be applied to the LODF matrix. Entries with magnitude
+    smaller than this value will be set to zero.
+"""
+struct ShiftFactorsFormulation <: TransmissionFormulation
+    isf_cutoff::Float64
+    lodf_cutoff::Float64
+    precomputed_isf::Union{Nothing,Matrix{Float64}}
+    precomputed_lodf::Union{Nothing,Matrix{Float64}}
+
+    function ShiftFactorsFormulation(;
+        isf_cutoff = 0.005,
+        lodf_cutoff = 0.001,
+        precomputed_isf = nothing,
+        precomputed_lodf = nothing,
+    )
+        return new(isf_cutoff, lodf_cutoff, precomputed_isf, precomputed_lodf)
+    end
+end

src/model/formulations/base/system.jl

@@ -0,0 +1,56 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_system_wide_eqs!(model::JuMP.Model)::Nothing
+    _add_net_injection_eqs!(model)
+    _add_reserve_eqs!(model)
+    return
+end
+
+function _add_net_injection_eqs!(model::JuMP.Model)::Nothing
+    T = model[:instance].time
+    net_injection = _init(model, :net_injection)
+    eq_net_injection = _init(model, :eq_net_injection)
+    eq_power_balance = _init(model, :eq_power_balance)
+    for t in 1:T, b in model[:instance].buses
+        n = net_injection[b.name, t] = @variable(model)
+        eq_net_injection[b.name, t] =
+            @constraint(model, -n + model[:expr_net_injection][b.name, t] == 0)
+    end
+    for t in 1:T
+        eq_power_balance[t] = @constraint(
+            model,
+            sum(net_injection[b.name, t] for b in model[:instance].buses) == 0
+        )
+    end
+    return
+end
+
+function _add_reserve_eqs!(model::JuMP.Model)::Nothing
+    eq_min_reserve = _init(model, :eq_min_reserve)
+    instance = model[:instance]
+    for t in 1:instance.time
+        # Equation (68) in Kneuven et al. (2020)
+        # As in Morales-España et al. (2013a)
+        # Akin to the alternative formulation with max_power_avail
+        # from Carrión and Arroyo (2006) and Ostrowski et al. (2012)
+        shortfall_penalty = instance.shortfall_penalty[t]
+        eq_min_reserve[t] = @constraint(
+            model,
+            sum(model[:reserve][g.name, t] for g in instance.units) +
+            (shortfall_penalty >= 0 ? model[:reserve_shortfall][t] : 0.0) >=
+            instance.reserves.spinning[t]
+        )
+
+        # Account for shortfall contribution to objective
+        if shortfall_penalty >= 0
+            add_to_expression!(
+                model[:obj],
+                shortfall_penalty,
+                model[:reserve_shortfall][t],
+            )
+        end
+    end
+    return
+end

src/model/formulations/base/unit.jl

@@ -0,0 +1,218 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _add_unit!(model::JuMP.Model, g::Unit, formulation::Formulation)
+    if !all(g.must_run) && any(g.must_run)
+        error("Partially must-run units are not currently supported")
+    end
+    if g.initial_power === nothing || g.initial_status === nothing
+        error("Initial conditions for $(g.name) must be provided")
+    end
+
+    # Variables
+    _add_production_vars!(model, g, formulation.prod_vars)
+    _add_reserve_vars!(model, g)
+    _add_startup_shutdown_vars!(model, g)
+    _add_status_vars!(model, g, formulation.status_vars)
+
+    # Constraints and objective function
+    _add_min_uptime_downtime_eqs!(model, g)
+    _add_net_injection_eqs!(model, g)
+    _add_production_limit_eqs!(model, g, formulation.prod_vars)
+    _add_production_piecewise_linear_eqs!(
+        model,
+        g,
+        formulation.prod_vars,
+        formulation.pwl_costs,
+        formulation.status_vars,
+    )
+    _add_ramp_eqs!(
+        model,
+        g,
+        formulation.prod_vars,
+        formulation.ramping,
+        formulation.status_vars,
+    )
+    _add_startup_cost_eqs!(model, g, formulation.startup_costs)
+    _add_startup_shutdown_limit_eqs!(model, g)
+    _add_status_eqs!(model, g, formulation.status_vars)
+    return
+end
+
+_is_initially_on(g::Unit)::Float64 = (g.initial_status > 0 ? 1.0 : 0.0)
+
+function _add_reserve_vars!(model::JuMP.Model, g::Unit)::Nothing
+    reserve = _init(model, :reserve)
+    reserve_shortfall = _init(model, :reserve_shortfall)
+    for t in 1:model[:instance].time
+        if g.provides_spinning_reserves[t]
+            reserve[g.name, t] = @variable(model, lower_bound = 0)
+        else
+            reserve[g.name, t] = 0.0
+        end
+        reserve_shortfall[t] =
+            (model[:instance].shortfall_penalty[t] >= 0) ?
+            @variable(model, lower_bound = 0) : 0.0
+    end
+    return
+end
+
+function _add_reserve_eqs!(model::JuMP.Model, g::Unit)::Nothing
+    reserve = model[:reserve]
+    for t in 1:model[:instance].time
+        add_to_expression!(expr_reserve[g.bus.name, t], reserve[g.name, t], 1.0)
+    end
+    return
+end
+
+function _add_startup_shutdown_vars!(model::JuMP.Model, g::Unit)::Nothing
+    startup = _init(model, :startup)
+    for t in 1:model[:instance].time
+        for s in 1:length(g.startup_categories)
+            startup[g.name, t, s] = @variable(model, binary = true)
+        end
+    end
+    return
+end
+
+function _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
+    eq_shutdown_limit = _init(model, :eq_shutdown_limit)
+    eq_startup_limit = _init(model, :eq_startup_limit)
+    is_on = model[:is_on]
+    prod_above = model[:prod_above]
+    reserve = model[:reserve]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+    T = model[:instance].time
+    for t in 1:T
+        # Startup limit
+        eq_startup_limit[g.name, t] = @constraint(
+            model,
+            prod_above[g.name, t] + reserve[g.name, t] <=
+            (g.max_power[t] - g.min_power[t]) * is_on[g.name, t] -
+            max(0, g.max_power[t] - g.startup_limit) * switch_on[g.name, t]
+        )
+        # Shutdown limit
+        if g.initial_power > g.shutdown_limit
+            eq_shutdown_limit[g.name, 0] =
+                @constraint(model, switch_off[g.name, 1] <= 0)
+        end
+        if t < T
+            eq_shutdown_limit[g.name, t] = @constraint(
+                model,
+                prod_above[g.name, t] <=
+                (g.max_power[t] - g.min_power[t]) * is_on[g.name, t] -
+                max(0, g.max_power[t] - g.shutdown_limit) *
+                switch_off[g.name, t+1]
+            )
+        end
+    end
+    return
+end
+
+function _add_ramp_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation::RampingFormulation,
+)::Nothing
+    prod_above = model[:prod_above]
+    reserve = model[:reserve]
+    eq_ramp_up = _init(model, :eq_ramp_up)
+    eq_ramp_down = _init(model, :eq_ramp_down)
+    for t in 1:model[:instance].time
+        # Ramp up limit 
+        if t == 1
+            if _is_initially_on(g) == 1
+                eq_ramp_up[g.name, t] = @constraint(
+                    model,
+                    prod_above[g.name, t] + reserve[g.name, t] <=
+                    (g.initial_power - g.min_power[t]) + g.ramp_up_limit
+                )
+            end
+        else
+            eq_ramp_up[g.name, t] = @constraint(
+                model,
+                prod_above[g.name, t] + reserve[g.name, t] <=
+                prod_above[g.name, t-1] + g.ramp_up_limit
+            )
+        end
+
+        # Ramp down limit
+        if t == 1
+            if _is_initially_on(g) == 1
+                eq_ramp_down[g.name, t] = @constraint(
+                    model,
+                    prod_above[g.name, t] >=
+                    (g.initial_power - g.min_power[t]) - g.ramp_down_limit
+                )
+            end
+        else
+            eq_ramp_down[g.name, t] = @constraint(
+                model,
+                prod_above[g.name, t] >=
+                prod_above[g.name, t-1] - g.ramp_down_limit
+            )
+        end
+    end
+end
+
+function _add_min_uptime_downtime_eqs!(model::JuMP.Model, g::Unit)::Nothing
+    is_on = model[:is_on]
+    switch_off = model[:switch_off]
+    switch_on = model[:switch_on]
+    eq_min_uptime = _init(model, :eq_min_uptime)
+    eq_min_downtime = _init(model, :eq_min_downtime)
+    T = model[:instance].time
+    for t in 1:T
+        # Minimum up-time
+        eq_min_uptime[g.name, t] = @constraint(
+            model,
+            sum(switch_on[g.name, i] for i in (t-g.min_uptime+1):t if i >= 1) <= is_on[g.name, t]
+        )
+        # Minimum down-time
+        eq_min_downtime[g.name, t] = @constraint(
+            model,
+            sum(
+                switch_off[g.name, i] for i in (t-g.min_downtime+1):t if i >= 1
+            ) <= 1 - is_on[g.name, t]
+        )
+        # Minimum up/down-time for initial periods
+        if t == 1
+            if g.initial_status > 0
+                eq_min_uptime[g.name, 0] = @constraint(
+                    model,
+                    sum(
+                        switch_off[g.name, i] for
+                        i in 1:(g.min_uptime-g.initial_status) if i <= T
+                    ) == 0
+                )
+            else
+                eq_min_downtime[g.name, 0] = @constraint(
+                    model,
+                    sum(
+                        switch_on[g.name, i] for
+                        i in 1:(g.min_downtime+g.initial_status) if i <= T
+                    ) == 0
+                )
+            end
+        end
+    end
+end
+
+function _add_net_injection_eqs!(model::JuMP.Model, g::Unit)::Nothing
+    expr_net_injection = model[:expr_net_injection]
+    for t in 1:model[:instance].time
+        # Add to net injection expression
+        add_to_expression!(
+            expr_net_injection[g.bus.name, t],
+            model[:prod_above][g.name, t],
+            1.0,
+        )
+        add_to_expression!(
+            expr_net_injection[g.bus.name, t],
+            model[:is_on][g.name, t],
+            g.min_power[t],
+        )
+    end
+end

src/model/jumpext.jl

@@ -0,0 +1,48 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+# This file extends some JuMP functions so that decision variables can be safely
+# replaced by (constant) floating point numbers.
+
+import JuMP: value, fix, set_name
+
+function value(x::Float64)
+    return x
+end
+
+function fix(x::Float64, v::Float64; force)
+    return abs(x - v) < 1e-6 || error("Value mismatch: $x != $v")
+end
+
+function set_name(x::Float64, n::String)
+    # nop
+end
+
+function _init(model::JuMP.Model, key::Symbol)::OrderedDict
+    if !(key in keys(object_dictionary(model)))
+        model[key] = OrderedDict()
+    end
+    return model[key]
+end
+
+function _set_names!(model::JuMP.Model)
+    @info "Setting variable and constraint names..."
+    time_varnames = @elapsed begin
+        _set_names!(object_dictionary(model))
+    end
+    @info @sprintf("Set names in %.2f seconds", time_varnames)
+end
+
+function _set_names!(dict::Dict)
+    for name in keys(dict)
+        dict[name] isa AbstractDict || continue
+        for idx in keys(dict[name])
+            if dict[name][idx] isa AffExpr
+                continue
+            end
+            idx_str = join(map(string, idx), ",")
+            set_name(dict[name][idx], "$name[$idx_str]")
+        end
+    end
+end

src/screening.jl

@@ -1,198 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-# Copyright (C) 2019 Argonne National Laboratory
-# Written by Alinson Santos Xavier <axavier@anl.gov>
-
-
-using DataStructures
-using Base.Threads
-
-
-struct Violation
-    time::Int
-    monitored_line::TransmissionLine
-    outage_line::Union{TransmissionLine, Nothing}
-    amount::Float64  # Violation amount (in MW)
-end
-
-
-function Violation(;
-                   time::Int,
-                   monitored_line::TransmissionLine,
-                   outage_line::Union{TransmissionLine, Nothing},
-                   amount::Float64,
-                  ) :: Violation
-    return Violation(time, monitored_line, outage_line, amount)
-end
-
-
-mutable struct ViolationFilter
-    max_per_line::Int
-    max_total::Int
-    queues::Dict{Int, PriorityQueue{Violation, Float64}}
-end
-
-
-function ViolationFilter(;
-                         max_per_line::Int=1,
-                         max_total::Int=5,
-                        )::ViolationFilter
-    return ViolationFilter(max_per_line, max_total, Dict())
-end
-
-
-function offer(filter::ViolationFilter, v::Violation)::Nothing
-    if v.monitored_line.offset ∉ keys(filter.queues)
-        filter.queues[v.monitored_line.offset] = PriorityQueue{Violation, Float64}()
-    end
-    q::PriorityQueue{Violation, Float64} = filter.queues[v.monitored_line.offset]
-    if length(q) < filter.max_per_line
-        enqueue!(q, v => v.amount)
-    else
-        if v.amount > peek(q)[1].amount
-            dequeue!(q)
-            enqueue!(q, v => v.amount)
-        end
-    end
-    nothing
-end
-
-
-function query(filter::ViolationFilter)::Array{Violation, 1}
-    violations = Array{Violation,1}()
-    time_queue = PriorityQueue{Violation, Float64}()
-    for l in keys(filter.queues)
-        line_queue = filter.queues[l]
-        while length(line_queue) > 0
-            v = dequeue!(line_queue)
-            if length(time_queue) < filter.max_total
-                enqueue!(time_queue, v => v.amount)
-            else
-                if v.amount > peek(time_queue)[1].amount
-                    dequeue!(time_queue)
-                    enqueue!(time_queue, v => v.amount)
-                end
-            end
-        end
-    end
-    while length(time_queue) > 0
-        violations = [violations; dequeue!(time_queue)]
-    end
-    return violations
-end
-
-
-"""
-
-    function find_violations(instance::UnitCommitmentInstance,
-                             net_injections::Array{Float64, 2};
-                             isf::Array{Float64,2},
-                             lodf::Array{Float64,2},
-                             max_per_line::Int = 1,
-                             max_per_period::Int = 5,
-                            ) :: Array{Violation, 1}
-
-Find transmission constraint violations (both pre-contingency, as well as post-contingency).
-
-The argument `net_injection` should be a (B-1) x T matrix, where B is the number of buses
-and T is the number of time periods. The arguments `isf` and `lodf` can be computed using
-UnitCommitment.injection_shift_factors and UnitCommitment.line_outage_factors.
-The argument `overflow` specifies how much flow above the transmission limits (in MW) is allowed.
-It should be an L x T matrix, where L is the number of transmission lines.
-"""
-function find_violations(;
-                         instance::UnitCommitmentInstance,
-                         net_injections::Array{Float64, 2},
-                         overflow::Array{Float64, 2},
-                         isf::Array{Float64,2},
-                         lodf::Array{Float64,2},
-                         max_per_line::Int = 1,
-                         max_per_period::Int = 5,
-                        )::Array{Violation, 1}
-
-    B = length(instance.buses) - 1
-    L = length(instance.lines)
-    T = instance.time
-    K = nthreads()
-    
-    size(net_injections) == (B, T) || error("net_injections has incorrect size")
-    size(isf) == (L, B) || error("isf has incorrect size")
-    size(lodf) == (L, L) || error("lodf has incorrect size")
-    
-    filters = Dict(t => ViolationFilter(max_total=max_per_period,
-                                        max_per_line=max_per_line)
-                   for t in 1:T)
-    
-    pre_flow::Array{Float64} = zeros(L, K)           # pre_flow[lm, thread]
-    post_flow::Array{Float64} = zeros(L, L, K)       # post_flow[lm, lc, thread]
-    pre_v::Array{Float64} = zeros(L, K)              # pre_v[lm, thread]
-    post_v::Array{Float64} = zeros(L, L, K)          # post_v[lm, lc, thread]
-    
-    normal_limits::Array{Float64,2} = [l.normal_flow_limit[t] + overflow[l.offset, t]
-                                       for l in instance.lines, t in 1:T]
-    
-    emergency_limits::Array{Float64,2} = [l.emergency_flow_limit[t] + overflow[l.offset, t]
-                                          for l in instance.lines, t in 1:T]
-    
-    is_vulnerable::Array{Bool} = zeros(Bool, L)
-    for c in instance.contingencies
-        is_vulnerable[c.lines[1].offset] = true
-    end
-
-    @threads for t in 1:T
-        k = threadid()
-        
-        # Pre-contingency flows
-        pre_flow[:, k] = isf * net_injections[:, t]
-    
-        # Post-contingency flows
-        for lc in 1:L, lm in 1:L
-            post_flow[lm, lc, k] = pre_flow[lm, k] + pre_flow[lc, k] * lodf[lm, lc]
-        end
-        
-        # Pre-contingency violations
-        for lm in 1:L
-            pre_v[lm, k] = max(0.0,
-                               pre_flow[lm, k] - normal_limits[lm, t],
-                               - pre_flow[lm, k] - normal_limits[lm, t])
-        end
-        
-        # Post-contingency violations
-        for lc in 1:L, lm in 1:L
-            post_v[lm, lc, k] = max(0.0,
-                                    post_flow[lm, lc, k] - emergency_limits[lm, t],
-                                    - post_flow[lm, lc, k] - emergency_limits[lm, t])
-        end
-        
-        # Offer pre-contingency violations
-        for lm in 1:L
-            if pre_v[lm, k] > 1e-5
-                offer(filters[t], Violation(time=t,
-                                            monitored_line=instance.lines[lm],
-                                            outage_line=nothing,
-                                            amount=pre_v[lm, k]))
-            end
-        end
-        
-        # Offer post-contingency violations
-        for lm in 1:L, lc in 1:L
-            if post_v[lm, lc, k] > 1e-5 && is_vulnerable[lc]
-                offer(filters[t], Violation(time=t,
-                                            monitored_line=instance.lines[lm],
-                                            outage_line=instance.lines[lc],
-                                            amount=post_v[lm, lc, k]))
-            end
-        end
-    end
-    
-    violations = Violation[]
-    for t in 1:instance.time
-        append!(violations, query(filters[t]))
-    end
-    
-    return violations
-end
-
-
-export Violation, ViolationFilter, offer, query, find_violations

src/sensitivity.jl

@@ -1,80 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using SparseArrays, Base.Threads, LinearAlgebra, JuMP
-
-"""
-    injection_shift_factors(; buses, lines)
-
-Returns a (B-1)xL matrix M, where B is the number of buses and L is the number of transmission
-lines. For a given bus b and transmission line l, the entry M[l.offset, b.offset] indicates
-the amount of power (in MW) that flows through transmission line l when 1 MW of power is
-injected at the slack bus (the bus that has offset zero) and withdrawn from b.
-"""
-function injection_shift_factors(; buses, lines)
-    susceptance = susceptance_matrix(lines)
-    incidence = reduced_incidence_matrix(lines = lines, buses = buses)
-    laplacian = transpose(incidence) * susceptance * incidence
-    isf = susceptance * incidence * inv(Array(laplacian))
-    return isf
-end
-
-
-"""
-    reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{TransmissionLine})
-
-Returns the incidence matrix for the network, with the column corresponding to the slack
-bus is removed. More precisely, returns a (B-1) x L matrix, where B is the number of buses
-and L is the number of lines. For each row, there is a 1 element and a -1 element, indicating
-the source and target buses, respectively, for that line.
-"""
-function reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{TransmissionLine})
-    matrix = spzeros(Float64, length(lines), length(buses) - 1)
-    for line in lines
-        if line.source.offset > 0
-            matrix[line.offset, line.source.offset] = 1
-        end
-        if line.target.offset > 0
-            matrix[line.offset, line.target.offset] = -1
-        end
-    end
-    matrix
-end
-
-"""
-    susceptance_matrix(lines::Array{TransmissionLine})
-
-Returns a LxL diagonal matrix, where each diagonal entry is the susceptance of the
-corresponding transmission line.
-"""
-function susceptance_matrix(lines::Array{TransmissionLine})
-    return Diagonal([l.susceptance for l in lines])
-end
-
-
-"""
-
-    line_outage_factors(; buses, lines, isf)
-
-Returns a LxL matrix containing the Line Outage Distribution Factors (LODFs) for the
-given network. This matrix how does the pre-contingency flow change when each individual
-transmission line is removed.
-"""
-function line_outage_factors(;
-                             buses::Array{Bus, 1},
-                             lines::Array{TransmissionLine, 1},
-                             isf::Array{Float64,2},
-                            ) :: Array{Float64,2}
-
-    n_lines, n_buses = size(isf)
-    incidence = Array(reduced_incidence_matrix(lines=lines,
-                                               buses=buses))
-    lodf::Array{Float64,2} = isf * transpose(incidence)
-    m, n = size(lodf)
-    for i in 1:n
-        lodf[:, i] *= 1.0 / (1.0 - lodf[i, i])
-        lodf[i, i] = -1
-    end
-    return lodf
-end

src/solution/fix.jl

@@ -0,0 +1,33 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+    fix!(model::JuMP.Model, solution::AbstractDict)::Nothing
+
+Fix the value of all binary variables to the ones specified by the given
+solution. Useful for computing LMPs.
+"""
+function fix!(model::JuMP.Model, solution::AbstractDict)::Nothing
+    instance, T = model[:instance], model[:instance].time
+    is_on = model[:is_on]
+    prod_above = model[:prod_above]
+    reserve = model[:reserve]
+    for g in instance.units
+        for t in 1:T
+            is_on_value = round(solution["Is on"][g.name][t])
+            prod_value =
+                round(solution["Production (MW)"][g.name][t], digits = 5)
+            reserve_value =
+                round(solution["Reserve (MW)"][g.name][t], digits = 5)
+            JuMP.fix(is_on[g.name, t], is_on_value, force = true)
+            JuMP.fix(
+                prod_above[g.name, t],
+                prod_value - is_on_value * g.min_power[t],
+                force = true,
+            )
+            JuMP.fix(reserve[g.name, t], reserve_value, force = true)
+        end
+    end
+    return
+end

src/solution/methods/XavQiuWanThi2019/enforce.jl

@@ -0,0 +1,83 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _enforce_transmission(
+    model::JuMP.Model,
+    violations::Vector{_Violation},
+)::Nothing
+    for v in violations
+        _enforce_transmission(
+            model = model,
+            violation = v,
+            isf = model[:isf],
+            lodf = model[:lodf],
+        )
+    end
+    return
+end
+
+function _enforce_transmission(;
+    model::JuMP.Model,
+    violation::_Violation,
+    isf::Matrix{Float64},
+    lodf::Matrix{Float64},
+)::Nothing
+    instance = model[:instance]
+    limit::Float64 = 0.0
+    overflow = model[:overflow]
+    net_injection = model[:net_injection]
+
+    if violation.outage_line === nothing
+        limit = violation.monitored_line.normal_flow_limit[violation.time]
+        @info @sprintf(
+            "    %8.3f MW overflow in %-5s time %3d (pre-contingency)",
+            violation.amount,
+            violation.monitored_line.name,
+            violation.time,
+        )
+    else
+        limit = violation.monitored_line.emergency_flow_limit[violation.time]
+        @info @sprintf(
+            "    %8.3f MW overflow in %-5s time %3d (outage: line %s)",
+            violation.amount,
+            violation.monitored_line.name,
+            violation.time,
+            violation.outage_line.name,
+        )
+    end
+
+    fm = violation.monitored_line.name
+    t = violation.time
+    flow = @variable(model, base_name = "flow[$fm,$t]")
+
+    v = overflow[violation.monitored_line.name, violation.time]
+    @constraint(model, flow <= limit + v)
+    @constraint(model, -flow <= limit + v)
+
+    if violation.outage_line === nothing
+        @constraint(
+            model,
+            flow == sum(
+                net_injection[b.name, violation.time] *
+                isf[violation.monitored_line.offset, b.offset] for
+                b in instance.buses if b.offset > 0
+            )
+        )
+    else
+        @constraint(
+            model,
+            flow == sum(
+                net_injection[b.name, violation.time] * (
+                    isf[violation.monitored_line.offset, b.offset] + (
+                        lodf[
+                            violation.monitored_line.offset,
+                            violation.outage_line.offset,
+                        ] * isf[violation.outage_line.offset, b.offset]
+                    )
+                ) for b in instance.buses if b.offset > 0
+            )
+        )
+    end
+    return nothing
+end

src/solution/methods/XavQiuWanThi2019/filter.jl

@@ -0,0 +1,44 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function _offer(filter::_ViolationFilter, v::_Violation)::Nothing
+    if v.monitored_line.offset ∉ keys(filter.queues)
+        filter.queues[v.monitored_line.offset] =
+            PriorityQueue{_Violation,Float64}()
+    end
+    q::PriorityQueue{_Violation,Float64} =
+        filter.queues[v.monitored_line.offset]
+    if length(q) < filter.max_per_line
+        enqueue!(q, v => v.amount)
+    else
+        if v.amount > peek(q)[1].amount
+            dequeue!(q)
+            enqueue!(q, v => v.amount)
+        end
+    end
+    return nothing
+end
+
+function _query(filter::_ViolationFilter)::Array{_Violation,1}
+    violations = Array{_Violation,1}()
+    time_queue = PriorityQueue{_Violation,Float64}()
+    for l in keys(filter.queues)
+        line_queue = filter.queues[l]
+        while length(line_queue) > 0
+            v = dequeue!(line_queue)
+            if length(time_queue) < filter.max_total
+                enqueue!(time_queue, v => v.amount)
+            else
+                if v.amount > peek(time_queue)[1].amount
+                    dequeue!(time_queue)
+                    enqueue!(time_queue, v => v.amount)
+                end
+            end
+        end
+    end
+    while length(time_queue) > 0
+        violations = [violations; dequeue!(time_queue)]
+    end
+    return violations
+end

src/solution/methods/XavQiuWanThi2019/find.jl

@@ -0,0 +1,177 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+import Base.Threads: @threads
+
+function _find_violations(
+    model::JuMP.Model;
+    max_per_line::Int,
+    max_per_period::Int,
+)
+    instance = model[:instance]
+    net_injection = model[:net_injection]
+    overflow = model[:overflow]
+    length(instance.buses) > 1 || return []
+    violations = []
+    @info "Verifying transmission limits..."
+    time_screening = @elapsed begin
+        non_slack_buses = [b for b in instance.buses if b.offset > 0]
+        net_injection_values = [
+            value(net_injection[b.name, t]) for b in non_slack_buses,
+            t in 1:instance.time
+        ]
+        overflow_values = [
+            value(overflow[lm.name, t]) for lm in instance.lines,
+            t in 1:instance.time
+        ]
+        violations = UnitCommitment._find_violations(
+            instance = instance,
+            net_injections = net_injection_values,
+            overflow = overflow_values,
+            isf = model[:isf],
+            lodf = model[:lodf],
+            max_per_line = max_per_line,
+            max_per_period = max_per_period,
+        )
+    end
+    @info @sprintf(
+        "Verified transmission limits in %.2f seconds",
+        time_screening
+    )
+    return violations
+end
+
+"""
+    function _find_violations(
+        instance::UnitCommitmentInstance,
+        net_injections::Array{Float64, 2};
+        isf::Array{Float64,2},
+        lodf::Array{Float64,2},
+        max_per_line::Int,
+        max_per_period::Int,
+    )::Array{_Violation, 1}
+
+Find transmission constraint violations (both pre-contingency, as well as
+post-contingency).
+
+The argument `net_injection` should be a (B-1) x T matrix, where B is the
+number of buses and T is the number of time periods. The arguments `isf` and
+`lodf` can be computed using UnitCommitment.injection_shift_factors and
+UnitCommitment.line_outage_factors. The argument `overflow` specifies how much
+flow above the transmission limits (in MW) is allowed. It should be an L x T
+matrix, where L is the number of transmission lines.
+"""
+function _find_violations(;
+    instance::UnitCommitmentInstance,
+    net_injections::Array{Float64,2},
+    overflow::Array{Float64,2},
+    isf::Array{Float64,2},
+    lodf::Array{Float64,2},
+    max_per_line::Int,
+    max_per_period::Int,
+)::Array{_Violation,1}
+    B = length(instance.buses) - 1
+    L = length(instance.lines)
+    T = instance.time
+    K = nthreads()
+
+    size(net_injections) == (B, T) || error("net_injections has incorrect size")
+    size(isf) == (L, B) || error("isf has incorrect size")
+    size(lodf) == (L, L) || error("lodf has incorrect size")
+
+    filters = Dict(
+        t => _ViolationFilter(
+            max_total = max_per_period,
+            max_per_line = max_per_line,
+        ) for t in 1:T
+    )
+
+    pre_flow::Array{Float64} = zeros(L, K)           # pre_flow[lm, thread]
+    post_flow::Array{Float64} = zeros(L, L, K)       # post_flow[lm, lc, thread]
+    pre_v::Array{Float64} = zeros(L, K)              # pre_v[lm, thread]
+    post_v::Array{Float64} = zeros(L, L, K)          # post_v[lm, lc, thread]
+
+    normal_limits::Array{Float64,2} = [
+        l.normal_flow_limit[t] + overflow[l.offset, t] for
+        l in instance.lines, t in 1:T
+    ]
+
+    emergency_limits::Array{Float64,2} = [
+        l.emergency_flow_limit[t] + overflow[l.offset, t] for
+        l in instance.lines, t in 1:T
+    ]
+
+    is_vulnerable::Array{Bool} = zeros(Bool, L)
+    for c in instance.contingencies
+        is_vulnerable[c.lines[1].offset] = true
+    end
+
+    @threads for t in 1:T
+        k = threadid()
+
+        # Pre-contingency flows
+        pre_flow[:, k] = isf * net_injections[:, t]
+
+        # Post-contingency flows
+        for lc in 1:L, lm in 1:L
+            post_flow[lm, lc, k] =
+                pre_flow[lm, k] + pre_flow[lc, k] * lodf[lm, lc]
+        end
+
+        # Pre-contingency violations
+        for lm in 1:L
+            pre_v[lm, k] = max(
+                0.0,
+                pre_flow[lm, k] - normal_limits[lm, t],
+                -pre_flow[lm, k] - normal_limits[lm, t],
+            )
+        end
+
+        # Post-contingency violations
+        for lc in 1:L, lm in 1:L
+            post_v[lm, lc, k] = max(
+                0.0,
+                post_flow[lm, lc, k] - emergency_limits[lm, t],
+                -post_flow[lm, lc, k] - emergency_limits[lm, t],
+            )
+        end
+
+        # Offer pre-contingency violations
+        for lm in 1:L
+            if pre_v[lm, k] > 1e-5
+                _offer(
+                    filters[t],
+                    _Violation(
+                        time = t,
+                        monitored_line = instance.lines[lm],
+                        outage_line = nothing,
+                        amount = pre_v[lm, k],
+                    ),
+                )
+            end
+        end
+
+        # Offer post-contingency violations
+        for lm in 1:L, lc in 1:L
+            if post_v[lm, lc, k] > 1e-5 && is_vulnerable[lc]
+                _offer(
+                    filters[t],
+                    _Violation(
+                        time = t,
+                        monitored_line = instance.lines[lm],
+                        outage_line = instance.lines[lc],
+                        amount = post_v[lm, lc, k],
+                    ),
+                )
+            end
+        end
+    end
+
+    violations = _Violation[]
+    for t in 1:instance.time
+        append!(violations, _query(filters[t]))
+    end
+
+    return violations
+end

src/solution/methods/XavQiuWanThi2019/optimize.jl

@@ -0,0 +1,56 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function optimize!(model::JuMP.Model, method::XavQiuWanThi2019.Method)::Nothing
+    function set_gap(gap)
+        try
+            JuMP.set_optimizer_attribute(model, "MIPGap", gap)
+            @info @sprintf("MIP gap tolerance set to %f", gap)
+        catch
+            @warn "Could not change MIP gap tolerance"
+        end
+    end
+    initial_time = time()
+    large_gap = false
+    has_transmission = (length(model[:isf]) > 0)
+    if has_transmission && method.two_phase_gap
+        set_gap(1e-2)
+        large_gap = true
+    else
+        set_gap(method.gap_limit)
+    end
+    while true
+        time_elapsed = time() - initial_time
+        time_remaining = method.time_limit - time_elapsed
+        if time_remaining < 0
+            @info "Time limit exceeded"
+            break
+        end
+        @info @sprintf(
+            "Setting MILP time limit to %.2f seconds",
+            time_remaining
+        )
+        JuMP.set_time_limit_sec(model, time_remaining)
+        @info "Solving MILP..."
+        JuMP.optimize!(model)
+        has_transmission || break
+        violations = _find_violations(
+            model,
+            max_per_line = method.max_violations_per_line,
+            max_per_period = method.max_violations_per_period,
+        )
+        if isempty(violations)
+            @info "No violations found"
+            if large_gap
+                large_gap = false
+                set_gap(method.gap_limit)
+            else
+                break
+            end
+        else
+            _enforce_transmission(model, violations)
+        end
+    end
+    return
+end

src/solution/methods/XavQiuWanThi2019/structs.jl

@@ -0,0 +1,93 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Lazy constraint solution method described in:
+
+    Xavier, A. S., Qiu, F., Wang, F., & Thimmapuram, P. R. (2019). Transmission
+    constraint filtering in large-scale security-constrained unit commitment. 
+    IEEE Transactions on Power Systems, 34(3), 2457-2460.
+    DOI: https://doi.org/10.1109/TPWRS.2019.2892620
+"""
+module XavQiuWanThi2019
+import ..SolutionMethod
+"""
+    struct Method
+        time_limit::Float64
+        gap_limit::Float64
+        two_phase_gap::Bool
+        max_violations_per_line::Int
+        max_violations_per_period::Int
+    end
+
+Fields
+------
+
+- `time_limit`:
+    the time limit over the entire optimization procedure.
+- `gap_limit`: 
+    the desired relative optimality gap.
+- `two_phase_gap`: 
+    if true, solve the problem with large gap tolerance first, then reduce
+    the gap tolerance when no further violated constraints are found.
+- `max_violations_per_line`:
+    maximum number of violated transmission constraints to add to the
+    formulation per transmission line.
+- `max_violations_per_period`:
+    maximum number of violated transmission constraints to add to the
+    formulation per time period.
+
+"""
+struct Method <: SolutionMethod
+    time_limit::Float64
+    gap_limit::Float64
+    two_phase_gap::Bool
+    max_violations_per_line::Int
+    max_violations_per_period::Int
+
+    function Method(;
+        time_limit::Float64 = 86400.0,
+        gap_limit::Float64 = 1e-3,
+        two_phase_gap::Bool = true,
+        max_violations_per_line::Int = 1,
+        max_violations_per_period::Int = 5,
+    )
+        return new(
+            time_limit,
+            gap_limit,
+            two_phase_gap,
+            max_violations_per_line,
+            max_violations_per_period,
+        )
+    end
+end
+end
+
+import DataStructures: PriorityQueue
+
+struct _Violation
+    time::Int
+    monitored_line::TransmissionLine
+    outage_line::Union{TransmissionLine,Nothing}
+    amount::Float64
+
+    function _Violation(;
+        time::Int,
+        monitored_line::TransmissionLine,
+        outage_line::Union{TransmissionLine,Nothing},
+        amount::Float64,
+    )
+        return new(time, monitored_line, outage_line, amount)
+    end
+end
+
+mutable struct _ViolationFilter
+    max_per_line::Int
+    max_total::Int
+    queues::Dict{Int,PriorityQueue{_Violation,Float64}}
+
+    function _ViolationFilter(; max_per_line::Int = 1, max_total::Int = 5)
+        return new(max_per_line, max_total, Dict())
+    end
+end

src/solution/optimize.jl

@@ -0,0 +1,14 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+    function optimize!(model::JuMP.Model)::Nothing
+
+Solve the given unit commitment model. Unlike JuMP.optimize!, this uses more
+advanced methods to accelerate the solution process and to enforce transmission
+and N-1 security constraints.
+"""
+function optimize!(model::JuMP.Model)::Nothing
+    return UnitCommitment.optimize!(model, XavQiuWanThi2019.Method())
+end

src/solution/solution.jl

@@ -0,0 +1,71 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function solution(model::JuMP.Model)::OrderedDict
+    instance, T = model[:instance], model[:instance].time
+    function timeseries(vars, collection)
+        return OrderedDict(
+            b.name => [round(value(vars[b.name, t]), digits = 5) for t in 1:T]
+            for b in collection
+        )
+    end
+    function production_cost(g)
+        return [
+            value(model[:is_on][g.name, t]) * g.min_power_cost[t] + sum(
+                Float64[
+                    value(model[:segprod][g.name, t, k]) *
+                    g.cost_segments[k].cost[t] for
+                    k in 1:length(g.cost_segments)
+                ],
+            ) for t in 1:T
+        ]
+    end
+    function production(g)
+        return [
+            value(model[:is_on][g.name, t]) * g.min_power[t] + sum(
+                Float64[
+                    value(model[:segprod][g.name, t, k]) for
+                    k in 1:length(g.cost_segments)
+                ],
+            ) for t in 1:T
+        ]
+    end
+    function startup_cost(g)
+        S = length(g.startup_categories)
+        return [
+            sum(
+                g.startup_categories[s].cost *
+                value(model[:startup][g.name, t, s]) for s in 1:S
+            ) for t in 1:T
+        ]
+    end
+    sol = OrderedDict()
+    sol["Production (MW)"] =
+        OrderedDict(g.name => production(g) for g in instance.units)
+    sol["Production cost (\$)"] =
+        OrderedDict(g.name => production_cost(g) for g in instance.units)
+    sol["Startup cost (\$)"] =
+        OrderedDict(g.name => startup_cost(g) for g in instance.units)
+    sol["Is on"] = timeseries(model[:is_on], instance.units)
+    sol["Switch on"] = timeseries(model[:switch_on], instance.units)
+    sol["Switch off"] = timeseries(model[:switch_off], instance.units)
+    sol["Reserve (MW)"] = timeseries(model[:reserve], instance.units)
+    sol["Reserve shortfall (MW)"] = OrderedDict(
+        t =>
+            (instance.shortfall_penalty[t] >= 0) ?
+            round(value(model[:reserve_shortfall][t]), digits = 5) : 0.0 for
+        t in 1:instance.time
+    )
+    sol["Net injection (MW)"] =
+        timeseries(model[:net_injection], instance.buses)
+    sol["Load curtail (MW)"] = timeseries(model[:curtail], instance.buses)
+    if !isempty(instance.lines)
+        sol["Line overflow (MW)"] = timeseries(model[:overflow], instance.lines)
+    end
+    if !isempty(instance.price_sensitive_loads)
+        sol["Price-sensitive loads (MW)"] =
+            timeseries(model[:loads], instance.price_sensitive_loads)
+    end
+    return sol
+end

src/solution/structs.jl

@@ -0,0 +1,5 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+abstract type SolutionMethod end

src/solution/warmstart.jl

@@ -0,0 +1,22 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function set_warm_start!(model::JuMP.Model, solution::AbstractDict)::Nothing
+    instance, T = model[:instance], model[:instance].time
+    is_on = model[:is_on]
+    for g in instance.units
+        for t in 1:T
+            JuMP.set_start_value(is_on[g.name, t], solution["Is on"][g.name][t])
+            JuMP.set_start_value(
+                switch_on[g.name, t],
+                solution["Switch on"][g.name][t],
+            )
+            JuMP.set_start_value(
+                switch_off[g.name, t],
+                solution["Switch off"][g.name][t],
+            )
+        end
+    end
+    return
+end

src/solution/write.jl

@@ -0,0 +1,10 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+function write(filename::AbstractString, solution::AbstractDict)::Nothing
+    open(filename, "w") do file
+        return JSON.print(file, solution, 2)
+    end
+    return
+end

src/transform/initcond.jl

@@ -11,39 +11,39 @@ Generates feasible initial conditions for the given instance, by constructing
 and solving a single-period mixed-integer optimization problem, using the given
 optimizer. The instance is modified in-place.
 """
-function generate_initial_conditions!(instance::UnitCommitmentInstance,
-                                      optimizer)
+function generate_initial_conditions!(
+    instance::UnitCommitmentInstance,
+    optimizer,
+)::Nothing
     G = instance.units
     B = instance.buses
     t = 1
     mip = JuMP.Model(optimizer)
-    
+
     # Decision variables
     @variable(mip, x[G], Bin)
     @variable(mip, p[G] >= 0)
-    
+
     # Constraint: Minimum power
-    @constraint(mip,
-                min_power[g in G],
-                p[g] >= g.min_power[t] * x[g])
-    
+    @constraint(mip, min_power[g in G], p[g] >= g.min_power[t] * x[g])
+
     # Constraint: Maximum power
-    @constraint(mip,
-                max_power[g in G],
-                p[g] <= g.max_power[t] * x[g])
-    
+    @constraint(mip, max_power[g in G], p[g] <= g.max_power[t] * x[g])
+
     # Constraint: Production equals demand
-    @constraint(mip,
-                power_balance,
-                sum(b.load[t] for b in B) == sum(p[g] for g in G))
-    
+    @constraint(
+        mip,
+        power_balance,
+        sum(b.load[t] for b in B) == sum(p[g] for g in G)
+    )
+
     # Constraint: Must run
     for g in G
         if g.must_run[t]
             @constraint(mip, x[g] == 1)
         end
     end
-    
+
     # Objective function
     function cost_slope(g)
         mw = g.min_power[t]
@@ -58,12 +58,10 @@ function generate_initial_conditions!(instance::UnitCommitmentInstance,
             return c / mw
         end
     end
-    @objective(mip,
-               Min,
-               sum(p[g] * cost_slope(g) for g in G))
-    
+    @objective(mip, Min, sum(p[g] * cost_slope(g) for g in G))
+
     JuMP.optimize!(mip)
-    
+
     for g in G
         if JuMP.value(x[g]) > 0
             g.initial_power = JuMP.value(p[g])
@@ -73,4 +71,5 @@ function generate_initial_conditions!(instance::UnitCommitmentInstance,
             g.initial_status = -24
         end
     end
+    return
 end

src/transform/randomize/XavQiuAhm2021.jl

@@ -0,0 +1,209 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+Methods described in:
+
+    Xavier, Álinson S., Feng Qiu, and Shabbir Ahmed. "Learning to solve
+    large-scale security-constrained unit commitment problems." INFORMS
+    Journal on Computing 33.2 (2021): 739-756. DOI: 10.1287/ijoc.2020.0976
+"""
+module XavQiuAhm2021
+
+using Distributions
+import ..UnitCommitmentInstance
+
+"""
+    struct Randomization
+        cost = Uniform(0.95, 1.05)
+        load_profile_mu = [...]
+        load_profile_sigma = [...]
+        load_share = Uniform(0.90, 1.10)
+        peak_load = Uniform(0.6 * 0.925, 0.6 * 1.075)
+        randomize_costs = true
+        randomize_load_profile = true
+        randomize_load_share = true
+    end
+
+Randomization method that changes: (1) production and startup costs, (2)
+share of load coming from each bus, (3) peak system load, and (4) temporal
+load profile, as follows:
+
+1. **Production and startup costs:**
+    For each unit `u`, the vectors `u.min_power_cost` and `u.cost_segments`
+    are multiplied by a constant `α[u]` sampled from the provided `cost`
+    distribution. If `randomize_costs` is false, skips this step.
+
+2. **Load share:**
+   For each bus `b` and time `t`, the value `b.load[t]` is multiplied by
+   `(β[b] * b.load[t]) / sum(β[b2] * b2.load[t] for b2 in buses)`, where
+   `β[b]` is sampled from the provided `load_share` distribution. If
+   `randomize_load_share` is false, skips this step.
+
+3. **Peak system load and temporal load profile:**
+    Sets the peak load to `ρ * C`, where `ρ` is sampled from `peak_load` and `C`
+    is the maximum system capacity, at any time. Also scales the loads of all
+    buses, so that `system_load[t+1]` becomes equal to `system_load[t] * γ[t]`,
+    where `γ[t]` is sampled from `Normal(load_profile_mu[t], load_profile_sigma[t])`.
+    
+    The system load for the first time period is set so that the peak load
+    matches `ρ * C`. If `load_profile_sigma` and `load_profile_mu` have fewer
+    elements than `instance.time`, wraps around. If `randomize_load_profile`
+    is false, skips this step.
+
+The default parameters were obtained based on an analysis of publicly available
+bid and hourly data from PJM, corresponding to the month of January, 2017. For
+more details, see Section 4.2 of the paper.
+"""
+Base.@kwdef struct Randomization
+    cost = Uniform(0.95, 1.05)
+    load_profile_mu::Vector{Float64} = [
+        1.0,
+        0.978,
+        0.98,
+        1.004,
+        1.02,
+        1.078,
+        1.132,
+        1.018,
+        0.999,
+        1.006,
+        0.999,
+        0.987,
+        0.975,
+        0.984,
+        0.995,
+        1.005,
+        1.045,
+        1.106,
+        0.981,
+        0.981,
+        0.978,
+        0.948,
+        0.928,
+        0.953,
+    ]
+    load_profile_sigma::Vector{Float64} = [
+        0.0,
+        0.011,
+        0.015,
+        0.01,
+        0.012,
+        0.029,
+        0.055,
+        0.027,
+        0.026,
+        0.023,
+        0.013,
+        0.012,
+        0.014,
+        0.011,
+        0.008,
+        0.008,
+        0.02,
+        0.02,
+        0.016,
+        0.012,
+        0.014,
+        0.015,
+        0.017,
+        0.024,
+    ]
+    load_share = Uniform(0.90, 1.10)
+    peak_load = Uniform(0.6 * 0.925, 0.6 * 1.075)
+    randomize_load_profile::Bool = true
+    randomize_costs::Bool = true
+    randomize_load_share::Bool = true
+end
+
+function _randomize_costs(
+    instance::UnitCommitmentInstance,
+    distribution,
+)::Nothing
+    for unit in instance.units
+        α = rand(distribution)
+        unit.min_power_cost *= α
+        for k in unit.cost_segments
+            k.cost *= α
+        end
+        for s in unit.startup_categories
+            s.cost *= α
+        end
+    end
+    return
+end
+
+function _randomize_load_share(
+    instance::UnitCommitmentInstance,
+    distribution,
+)::Nothing
+    α = rand(distribution, length(instance.buses))
+    for t in 1:instance.time
+        total = sum(bus.load[t] for bus in instance.buses)
+        den = sum(
+            bus.load[t] / total * α[i] for
+            (i, bus) in enumerate(instance.buses)
+        )
+        for (i, bus) in enumerate(instance.buses)
+            bus.load[t] *= α[i] / den
+        end
+    end
+    return
+end
+
+function _randomize_load_profile(
+    instance::UnitCommitmentInstance,
+    params::Randomization,
+)::Nothing
+    # Generate new system load
+    system_load = [1.0]
+    for t in 2:instance.time
+        idx = (t - 1) % length(params.load_profile_mu) + 1
+        gamma = rand(
+            Normal(params.load_profile_mu[idx], params.load_profile_sigma[idx]),
+        )
+        push!(system_load, system_load[t-1] * gamma)
+    end
+    capacity = sum(maximum(u.max_power) for u in instance.units)
+    peak_load = rand(params.peak_load) * capacity
+    system_load = system_load ./ maximum(system_load) .* peak_load
+
+    # Scale bus loads to match the new system load
+    prev_system_load = sum(b.load for b in instance.buses)
+    for b in instance.buses
+        for t in 1:instance.time
+            b.load[t] *= system_load[t] / prev_system_load[t]
+        end
+    end
+
+    return
+end
+
+end
+
+"""
+    function randomize!(
+        instance::UnitCommitment.UnitCommitmentInstance,
+        method::XavQiuAhm2021.Randomization,
+    )::Nothing
+
+Randomize costs and loads based on the method described in XavQiuAhm2021.
+"""
+function randomize!(
+    instance::UnitCommitment.UnitCommitmentInstance,
+    method::XavQiuAhm2021.Randomization,
+)::Nothing
+    if method.randomize_costs
+        XavQiuAhm2021._randomize_costs(instance, method.cost)
+    end
+    if method.randomize_load_share
+        XavQiuAhm2021._randomize_load_share(instance, method.load_share)
+    end
+    if method.randomize_load_profile
+        XavQiuAhm2021._randomize_load_profile(instance, method)
+    end
+    return
+end
+
+export randomize!

src/transform/slice.jl

@@ -0,0 +1,52 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+    slice(instance, range)
+
+Creates a new instance, with only a subset of the time periods.
+This function does not modify the provided instance. The initial
+conditions are also not modified.
+
+Example
+-------
+
+    # Build a 2-hour UC instance
+    instance = UnitCommitment.read_benchmark("test/case14")
+    modified = UnitCommitment.slice(instance, 1:2)
+
+"""
+function slice(
+    instance::UnitCommitmentInstance,
+    range::UnitRange{Int},
+)::UnitCommitmentInstance
+    modified = deepcopy(instance)
+    modified.time = length(range)
+    modified.power_balance_penalty = modified.power_balance_penalty[range]
+    modified.reserves.spinning = modified.reserves.spinning[range]
+    for u in modified.units
+        u.max_power = u.max_power[range]
+        u.min_power = u.min_power[range]
+        u.must_run = u.must_run[range]
+        u.min_power_cost = u.min_power_cost[range]
+        u.provides_spinning_reserves = u.provides_spinning_reserves[range]
+        for s in u.cost_segments
+            s.mw = s.mw[range]
+            s.cost = s.cost[range]
+        end
+    end
+    for b in modified.buses
+        b.load = b.load[range]
+    end
+    for l in modified.lines
+        l.normal_flow_limit = l.normal_flow_limit[range]
+        l.emergency_flow_limit = l.emergency_flow_limit[range]
+        l.flow_limit_penalty = l.flow_limit_penalty[range]
+    end
+    for ps in modified.price_sensitive_loads
+        ps.demand = ps.demand[range]
+        ps.revenue = ps.revenue[range]
+    end
+    return modified
+end

src/utils/log.jl

@@ -7,37 +7,51 @@ using Base.CoreLogging, Logging, Printf
 
 struct TimeLogger <: AbstractLogger
     initial_time::Float64
-    file::Union{Nothing, IOStream}
-    screen_log_level
-    io_log_level
+    file::Union{Nothing,IOStream}
+    screen_log_level::Any
+    io_log_level::Any
 end
 
 function TimeLogger(;
-                    initial_time::Float64,
-                    file::Union{Nothing, IOStream} = nothing,
-                    screen_log_level = CoreLogging.Info,
-                    io_log_level = CoreLogging.Info,
-                   ) :: TimeLogger
+    initial_time::Float64,
+    file::Union{Nothing,IOStream} = nothing,
+    screen_log_level = CoreLogging.Info,
+    io_log_level = CoreLogging.Info,
+)::TimeLogger
     return TimeLogger(initial_time, file, screen_log_level, io_log_level)
 end
 
 min_enabled_level(logger::TimeLogger) = logger.io_log_level
 shouldlog(logger::TimeLogger, level, _module, group, id) = true
 
-function handle_message(logger::TimeLogger,
-                        level,
-                        message,
-                        _module,
-                        group,
-                        id,
-                        filepath,
-                        line;
-                        kwargs...)
+function handle_message(
+    logger::TimeLogger,
+    level,
+    message,
+    _module,
+    group,
+    id,
+    filepath,
+    line;
+    kwargs...,
+)
     elapsed_time = time() - logger.initial_time
     time_string = @sprintf("[%12.3f] ", elapsed_time)
+
+    if level >= Logging.Error
+        color = :light_red
+    elseif level >= Logging.Warn
+        color = :light_yellow
+    else
+        color = :light_green
+    end
+
     if level >= logger.screen_log_level
-        print(time_string)
+        printstyled(time_string, color = color)
         println(message)
+        flush(stdout)
+        flush(stderr)
+        Base.Libc.flush_cstdio()
     end
     if logger.file !== nothing && level >= logger.io_log_level
         write(logger.file, time_string)
@@ -47,4 +61,7 @@ function handle_message(logger::TimeLogger,
     end
 end
 
-export TimeLogger
+function _setup_logger()
+    initial_time = time()
+    return global_logger(TimeLogger(initial_time = initial_time))
+end

src/utils/sysimage.jl

@@ -5,19 +5,24 @@
 using PackageCompiler
 
 using DataStructures
+using Distributions
 using JSON
 using JuMP
 using MathOptInterface
 using SparseArrays
 
-pkg = [:DataStructures,
-       :JSON,
-       :JuMP,
-       :MathOptInterface,
-       :SparseArrays,
-       ]
+pkg = [
+    :DataStructures,
+    :Distributions,
+    :JSON,
+    :JuMP,
+    :MathOptInterface,
+    :SparseArrays,
+]
 
 @info "Building system image..."
-create_sysimage(pkg,
-                precompile_statements_file="build/precompile.jl",
-                sysimage_path="build/sysimage.so")
+create_sysimage(
+    pkg,
+    precompile_statements_file = "build/precompile.jl",
+    sysimage_path = "build/sysimage.so",
+)

src/validate.jl

@@ -1,334 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using Printf
-
-bin(x) = [xi > 0.5 for xi in x]
-
-"""
-    fix!(instance)
-
-Verifies that the given unit commitment instance is valid and automatically fixes
-some validation errors if possible, issuing a warning for each error found.
-If a validation error cannot be automatically fixed, issues an exception.
-
-Returns the number of validation errors found.
-"""
-function fix!(instance::UnitCommitmentInstance)::Int
-    n_errors = 0
-    
-    for g in instance.units
-        
-        # Startup costs and delays must be increasing
-        for s in 2:length(g.startup_categories)
-            if g.startup_categories[s].delay <= g.startup_categories[s-1].delay
-                prev_value = g.startup_categories[s].delay
-                new_value = g.startup_categories[s-1].delay + 1
-                @warn "Generator $(g.name) has non-increasing startup delays (category $s). " *
-                      "Changing delay: $prev_value → $new_value"
-                g.startup_categories[s].delay = new_value
-                n_errors += 1
-            end
-            
-            if g.startup_categories[s].cost < g.startup_categories[s-1].cost
-                prev_value = g.startup_categories[s].cost
-                new_value = g.startup_categories[s-1].cost
-                @warn "Generator $(g.name) has decreasing startup cost (category $s). " *
-                      "Changing cost: $prev_value → $new_value"
-                g.startup_categories[s].cost = new_value
-                n_errors += 1
-            end
-
-        end
-        
-        for t in 1:instance.time
-            # Production cost curve should be convex
-            for k in 2:length(g.cost_segments)
-                cost = g.cost_segments[k].cost[t]
-                min_cost = g.cost_segments[k-1].cost[t]
-                if cost < min_cost - 1e-5
-                    @warn "Generator $(g.name) has non-convex production cost curve " *
-                          "(segment $k, time $t). Changing cost: $cost → $min_cost"
-                    g.cost_segments[k].cost[t] = min_cost
-                    n_errors += 1
-                end
-            end
-
-            # Startup limit must be greater than min_power
-            if g.startup_limit < g.min_power[t]
-                new_limit = g.min_power[t]
-                prev_limit = g.startup_limit
-                @warn "Generator $(g.name) has startup limit lower than minimum power. " *
-                      "Changing startup limit: $prev_limit → $new_limit"
-                g.startup_limit = new_limit
-                n_errors += 1
-            end
-        end
-    end
-    
-    
-    return n_errors
-end
-
-
-function validate(instance_filename::String, solution_filename::String)
-    instance = UnitCommitment.read(instance_filename)
-    solution = JSON.parse(open(solution_filename))
-    return validate(instance, solution)
-end
-
-
-"""
-    validate(instance, solution)::Bool
-
-Verifies that the given solution is feasible for the problem. If feasible,
-silently returns true. In infeasible, returns false and prints the validation
-errors to the screen.
-
-This function is implemented independently from the optimization model in `model.jl`, and
-therefore can be used to verify that the model is indeed producing valid solutions. It
-can also be used to verify the solutions produced by other optimization packages.
-"""
-function validate(instance::UnitCommitmentInstance,
-                  solution::Union{Dict,OrderedDict};
-                 )::Bool
-    err_count = 0
-    err_count += validate_units(instance, solution)
-    err_count += validate_reserve_and_demand(instance, solution)
-    
-    if err_count > 0
-        @error "Found $err_count validation errors"
-        return false
-    end
-    
-    return true
-end
-
-
-function validate_units(instance, solution; tol=0.01)
-    err_count = 0
-    
-    for unit in instance.units
-        production = solution["Production (MW)"][unit.name]
-        reserve = solution["Reserve (MW)"][unit.name]
-        actual_production_cost = solution["Production cost (\$)"][unit.name]
-        actual_startup_cost = solution["Startup cost (\$)"][unit.name]
-        is_on = bin(solution["Is on"][unit.name])
-        
-        for t in 1:instance.time
-            # Auxiliary variables
-            if t == 1
-                is_starting_up = (unit.initial_status < 0) && is_on[t]
-                is_shutting_down = (unit.initial_status > 0) && !is_on[t]
-                ramp_up = max(0, production[t] + reserve[t] - unit.initial_power)
-                ramp_down = max(0, unit.initial_power - production[t])
-            else
-                is_starting_up = !is_on[t-1] && is_on[t]
-                is_shutting_down = is_on[t-1] && !is_on[t]
-                ramp_up = max(0, production[t] + reserve[t] - production[t-1])
-                ramp_down = max(0, production[t-1] - production[t])
-            end
-            
-            # Compute production costs
-            production_cost, startup_cost = 0, 0
-            if is_on[t]
-                production_cost += unit.min_power_cost[t]
-                residual = max(0, production[t] - unit.min_power[t])
-                for s in unit.cost_segments
-                    cleared = min(residual, s.mw[t])
-                    production_cost += cleared * s.cost[t]
-                    residual = max(0, residual - s.mw[t])
-                end
-            end
-            
-            # Production should be non-negative
-            if production[t] < -tol
-                @error @sprintf("Unit %s produces negative amount of power at time %d (%.2f)",
-                                unit.name, t, production[t])
-                err_count += 1
-            end
-            
-            # Verify must-run
-            if !is_on[t] && unit.must_run[t]
-                @error @sprintf("Must-run unit %s is offline at time %d",
-                                unit.name, t)
-                err_count += 1
-            end
-            
-            # Verify reserve eligibility
-            if !unit.provides_spinning_reserves[t] && reserve[t] > tol
-                @error @sprintf("Unit %s is not eligible to provide spinning reserves at time %d",
-                                unit.name, t)
-                err_count += 1
-            end
-                
-            # If unit is on, must produce at least its minimum power
-            if is_on[t] && (production[t] < unit.min_power[t] - tol)
-                @error @sprintf("Unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
-                                unit.name, t, production[t], unit.min_power[t])
-                err_count += 1
-            end
-            
-            # If unit is on, must produce at most its maximum power
-            if is_on[t] && (production[t] + reserve[t] > unit.max_power[t] + tol)
-                @error @sprintf("Unit %s produces above its maximum limit at time %d (%.2f + %.2f> %.2f)",
-                                unit.name, t, production[t], reserve[t], unit.max_power[t])
-                err_count += 1
-            end
-            
-            # If unit is off, must produce zero
-            if !is_on[t] && production[t] + reserve[t] > tol
-                @error @sprintf("Unit %s produces power at time %d while off",
-                                unit.name, t)
-                err_count += 1
-            end
-            
-            # Startup limit
-            if is_starting_up && (ramp_up > unit.startup_limit + tol)
-                @error @sprintf("Unit %s exceeds startup limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_up, unit.startup_limit)
-                err_count += 1
-            end
-
-            # Shutdown limit
-            if is_shutting_down && (ramp_down > unit.shutdown_limit + tol)
-                @error @sprintf("Unit %s exceeds shutdown limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_down, unit.shutdown_limit)
-                err_count += 1
-            end
-
-            # Ramp-up limit
-            if !is_starting_up && !is_shutting_down && (ramp_up > unit.ramp_up_limit + tol)
-                @error @sprintf("Unit %s exceeds ramp up limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_up, unit.ramp_up_limit)
-                err_count += 1
-            end
-
-            # Ramp-down limit
-            if !is_starting_up && !is_shutting_down && (ramp_down > unit.ramp_down_limit + tol)
-                @error @sprintf("Unit %s exceeds ramp down limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_down, unit.ramp_down_limit)
-                err_count += 1
-            end
-            
-            # Verify startup costs & minimum downtime
-            if is_starting_up
-                
-                # Calculate how much time the unit has been offline
-                time_down = 0
-                for k in 1:(t-1)
-                    if !is_on[t - k]
-                        time_down += 1
-                    else
-                        break
-                    end
-                end
-                if t == time_down + 1
-                    initial_down = unit.min_downtime
-                    if unit.initial_status < 0
-                        initial_down = -unit.initial_status
-                    end
-                    time_down += initial_down
-                end
-                
-                # Calculate startup costs
-                for c in unit.startup_categories
-                    if time_down >= c.delay
-                        startup_cost = c.cost
-                    end
-                end
-                
-                # Check minimum downtime
-                if time_down < unit.min_downtime
-                    @error @sprintf("Unit %s violates minimum downtime at time %d",
-                                    unit.name, t)
-                    err_count += 1
-                end
-            end
-            
-            # Verify minimum uptime
-            if is_shutting_down
-                
-                # Calculate how much time the unit has been online
-                time_up = 0
-                for k in 1:(t-1)
-                    if is_on[t - k]
-                        time_up += 1
-                    else
-                        break
-                    end
-                end
-                if t == time_up + 1
-                    initial_up = unit.min_uptime
-                    if unit.initial_status > 0
-                        initial_up = unit.initial_status
-                    end
-                    time_up += initial_up
-                end
-                
-                if (t == time_up + 1) && (unit.initial_status > 0)
-                    time_up += unit.initial_status
-                end
-                
-                # Check minimum uptime
-                if time_up < unit.min_uptime
-                    @error @sprintf("Unit %s violates minimum uptime at time %d",
-                                    unit.name, t)
-                    err_count += 1
-                end
-            end
-            
-            # Verify production costs
-            if abs(actual_production_cost[t] - production_cost) > 1.00
-                @error @sprintf("Unit %s has unexpected production cost at time %d (%.2f should be %.2f)",
-                                unit.name, t, actual_production_cost[t], production_cost)
-                err_count += 1
-            end
-
-            # Verify startup costs
-            if abs(actual_startup_cost[t] - startup_cost) > 1.00
-                @error @sprintf("Unit %s has unexpected startup cost at time %d (%.2f should be %.2f)",
-                                unit.name, t, actual_startup_cost[t], startup_cost)
-                err_count += 1
-            end
-        
-        end
-    end
-    
-    return err_count
-end
-
-
-function validate_reserve_and_demand(instance, solution, tol=0.01)
-    err_count = 0
-    for t in 1:instance.time
-        load_curtail = 0
-        fixed_load = sum(b.load[t] for b in instance.buses)
-        production = sum(solution["Production (MW)"][g.name][t]
-                         for g in instance.units)
-        if "Load curtail (MW)" in keys(solution)
-            load_curtail = sum(solution["Load curtail (MW)"][b.name][t]
-                               for b in instance.buses)
-        end
-        balance = fixed_load - load_curtail - production
-        
-        # Verify that production equals demand
-        if abs(balance) > tol
-            @error @sprintf("Non-zero power balance at time %d (%.2f - %.2f - %.2f != 0)",
-                            t, fixed_load, load_curtail, production)
-            err_count += 1
-        end
-        
-        # Verify spinning reserves
-        reserve = sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
-        if reserve < instance.reserves.spinning[t] - tol
-            @error @sprintf("Insufficient spinning reserves at time %d (%.2f should be %.2f)",
-                            t, reserve, instance.reserves.spinning[t])
-            err_count += 1
-        end
-    end
-    
-    return err_count
-end
- 

src/validation/repair.jl

@@ -0,0 +1,69 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+"""
+    repair!(instance)
+
+Verifies that the given unit commitment instance is valid and automatically
+fixes some validation errors if possible, issuing a warning for each error
+found. If a validation error cannot be automatically fixed, issues an
+exception.
+
+Returns the number of validation errors found.
+"""
+function repair!(instance::UnitCommitmentInstance)::Int
+    n_errors = 0
+
+    for g in instance.units
+
+        # Startup costs and delays must be increasing
+        for s in 2:length(g.startup_categories)
+            if g.startup_categories[s].delay <= g.startup_categories[s-1].delay
+                prev_value = g.startup_categories[s].delay
+                new_value = g.startup_categories[s-1].delay + 1
+                @warn "Generator $(g.name) has non-increasing startup delays (category $s). " *
+                      "Changing delay: $prev_value → $new_value"
+                g.startup_categories[s].delay = new_value
+                n_errors += 1
+            end
+
+            if g.startup_categories[s].cost < g.startup_categories[s-1].cost
+                prev_value = g.startup_categories[s].cost
+                new_value = g.startup_categories[s-1].cost
+                @warn "Generator $(g.name) has decreasing startup cost (category $s). " *
+                      "Changing cost: $prev_value → $new_value"
+                g.startup_categories[s].cost = new_value
+                n_errors += 1
+            end
+        end
+
+        for t in 1:instance.time
+            # Production cost curve should be convex
+            for k in 2:length(g.cost_segments)
+                cost = g.cost_segments[k].cost[t]
+                min_cost = g.cost_segments[k-1].cost[t]
+                if cost < min_cost - 1e-5
+                    @warn "Generator $(g.name) has non-convex production cost curve " *
+                          "(segment $k, time $t). Changing cost: $cost → $min_cost"
+                    g.cost_segments[k].cost[t] = min_cost
+                    n_errors += 1
+                end
+            end
+
+            # Startup limit must be greater than min_power
+            if g.startup_limit < g.min_power[t]
+                new_limit = g.min_power[t]
+                prev_limit = g.startup_limit
+                @warn "Generator $(g.name) has startup limit lower than minimum power. " *
+                      "Changing startup limit: $prev_limit → $new_limit"
+                g.startup_limit = new_limit
+                n_errors += 1
+            end
+        end
+    end
+
+    return n_errors
+end
+
+export repair!

src/validation/validate.jl

@@ -0,0 +1,344 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using Printf
+
+bin(x) = [xi > 0.5 for xi in x]
+
+function validate(instance_filename::String, solution_filename::String)
+    instance = UnitCommitment.read(instance_filename)
+    solution = JSON.parse(open(solution_filename))
+    return validate(instance, solution)
+end
+
+"""
+    validate(instance, solution)::Bool
+
+Verifies that the given solution is feasible for the problem. If feasible,
+silently returns true. In infeasible, returns false and prints the validation
+errors to the screen.
+
+This function is implemented independently from the optimization model in
+`model.jl`, and therefore can be used to verify that the model is indeed
+producing valid solutions. It can also be used to verify the solutions produced
+by other optimization packages.
+"""
+function validate(
+    instance::UnitCommitmentInstance,
+    solution::Union{Dict,OrderedDict},
+)::Bool
+    err_count = 0
+    err_count += _validate_units(instance, solution)
+    err_count += _validate_reserve_and_demand(instance, solution)
+
+    if err_count > 0
+        @error "Found $err_count validation errors"
+        return false
+    end
+
+    return true
+end
+
+function _validate_units(instance, solution; tol = 0.01)
+    err_count = 0
+
+    for unit in instance.units
+        production = solution["Production (MW)"][unit.name]
+        reserve = solution["Reserve (MW)"][unit.name]
+        actual_production_cost = solution["Production cost (\$)"][unit.name]
+        actual_startup_cost = solution["Startup cost (\$)"][unit.name]
+        is_on = bin(solution["Is on"][unit.name])
+
+        for t in 1:instance.time
+            # Auxiliary variables
+            if t == 1
+                is_starting_up = (unit.initial_status < 0) && is_on[t]
+                is_shutting_down = (unit.initial_status > 0) && !is_on[t]
+                ramp_up =
+                    max(0, production[t] + reserve[t] - unit.initial_power)
+                ramp_down = max(0, unit.initial_power - production[t])
+            else
+                is_starting_up = !is_on[t-1] && is_on[t]
+                is_shutting_down = is_on[t-1] && !is_on[t]
+                ramp_up = max(0, production[t] + reserve[t] - production[t-1])
+                ramp_down = max(0, production[t-1] - production[t])
+            end
+
+            # Compute production costs
+            production_cost, startup_cost = 0, 0
+            if is_on[t]
+                production_cost += unit.min_power_cost[t]
+                residual = max(0, production[t] - unit.min_power[t])
+                for s in unit.cost_segments
+                    cleared = min(residual, s.mw[t])
+                    production_cost += cleared * s.cost[t]
+                    residual = max(0, residual - s.mw[t])
+                end
+            end
+
+            # Production should be non-negative
+            if production[t] < -tol
+                @error @sprintf(
+                    "Unit %s produces negative amount of power at time %d (%.2f)",
+                    unit.name,
+                    t,
+                    production[t]
+                )
+                err_count += 1
+            end
+
+            # Verify must-run
+            if !is_on[t] && unit.must_run[t]
+                @error @sprintf(
+                    "Must-run unit %s is offline at time %d",
+                    unit.name,
+                    t
+                )
+                err_count += 1
+            end
+
+            # Verify reserve eligibility
+            if !unit.provides_spinning_reserves[t] && reserve[t] > tol
+                @error @sprintf(
+                    "Unit %s is not eligible to provide spinning reserves at time %d",
+                    unit.name,
+                    t
+                )
+                err_count += 1
+            end
+
+            # If unit is on, must produce at least its minimum power
+            if is_on[t] && (production[t] < unit.min_power[t] - tol)
+                @error @sprintf(
+                    "Unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
+                    unit.name,
+                    t,
+                    production[t],
+                    unit.min_power[t]
+                )
+                err_count += 1
+            end
+
+            # If unit is on, must produce at most its maximum power
+            if is_on[t] &&
+               (production[t] + reserve[t] > unit.max_power[t] + tol)
+                @error @sprintf(
+                    "Unit %s produces above its maximum limit at time %d (%.2f + %.2f> %.2f)",
+                    unit.name,
+                    t,
+                    production[t],
+                    reserve[t],
+                    unit.max_power[t]
+                )
+                err_count += 1
+            end
+
+            # If unit is off, must produce zero
+            if !is_on[t] && production[t] + reserve[t] > tol
+                @error @sprintf(
+                    "Unit %s produces power at time %d while off",
+                    unit.name,
+                    t
+                )
+                err_count += 1
+            end
+
+            # Startup limit
+            if is_starting_up && (ramp_up > unit.startup_limit + tol)
+                @error @sprintf(
+                    "Unit %s exceeds startup limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_up,
+                    unit.startup_limit
+                )
+                err_count += 1
+            end
+
+            # Shutdown limit
+            if is_shutting_down && (ramp_down > unit.shutdown_limit + tol)
+                @error @sprintf(
+                    "Unit %s exceeds shutdown limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_down,
+                    unit.shutdown_limit
+                )
+                err_count += 1
+            end
+
+            # Ramp-up limit
+            if !is_starting_up &&
+               !is_shutting_down &&
+               (ramp_up > unit.ramp_up_limit + tol)
+                @error @sprintf(
+                    "Unit %s exceeds ramp up limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_up,
+                    unit.ramp_up_limit
+                )
+                err_count += 1
+            end
+
+            # Ramp-down limit
+            if !is_starting_up &&
+               !is_shutting_down &&
+               (ramp_down > unit.ramp_down_limit + tol)
+                @error @sprintf(
+                    "Unit %s exceeds ramp down limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_down,
+                    unit.ramp_down_limit
+                )
+                err_count += 1
+            end
+
+            # Verify startup costs & minimum downtime
+            if is_starting_up
+
+                # Calculate how much time the unit has been offline
+                time_down = 0
+                for k in 1:(t-1)
+                    if !is_on[t-k]
+                        time_down += 1
+                    else
+                        break
+                    end
+                end
+                if (t == time_down + 1) && (unit.initial_status < 0)
+                    time_down -= unit.initial_status
+                end
+
+                # Calculate startup costs
+                for c in unit.startup_categories
+                    if time_down >= c.delay
+                        startup_cost = c.cost
+                    end
+                end
+
+                # Check minimum downtime
+                if time_down < unit.min_downtime
+                    @error @sprintf(
+                        "Unit %s violates minimum downtime at time %d",
+                        unit.name,
+                        t
+                    )
+                    err_count += 1
+                end
+            end
+
+            # Verify minimum uptime
+            if is_shutting_down
+
+                # Calculate how much time the unit has been online
+                time_up = 0
+                for k in 1:(t-1)
+                    if is_on[t-k]
+                        time_up += 1
+                    else
+                        break
+                    end
+                end
+                if (t == time_up + 1) && (unit.initial_status > 0)
+                    time_up += unit.initial_status
+                end
+
+                # Check minimum uptime
+                if time_up < unit.min_uptime
+                    @error @sprintf(
+                        "Unit %s violates minimum uptime at time %d",
+                        unit.name,
+                        t
+                    )
+                    err_count += 1
+                end
+            end
+
+            # Verify production costs
+            if abs(actual_production_cost[t] - production_cost) > 1.00
+                @error @sprintf(
+                    "Unit %s has unexpected production cost at time %d (%.2f should be %.2f)",
+                    unit.name,
+                    t,
+                    actual_production_cost[t],
+                    production_cost
+                )
+                err_count += 1
+            end
+
+            # Verify startup costs
+            if abs(actual_startup_cost[t] - startup_cost) > 1.00
+                @error @sprintf(
+                    "Unit %s has unexpected startup cost at time %d (%.2f should be %.2f)",
+                    unit.name,
+                    t,
+                    actual_startup_cost[t],
+                    startup_cost
+                )
+                err_count += 1
+            end
+        end
+    end
+
+    return err_count
+end
+
+function _validate_reserve_and_demand(instance, solution, tol = 0.01)
+    err_count = 0
+    for t in 1:instance.time
+        load_curtail = 0
+        fixed_load = sum(b.load[t] for b in instance.buses)
+        ps_load = 0
+        if length(instance.price_sensitive_loads) > 0
+            ps_load = sum(
+                solution["Price-sensitive loads (MW)"][ps.name][t] for
+                ps in instance.price_sensitive_loads
+            )
+        end
+        production =
+            sum(solution["Production (MW)"][g.name][t] for g in instance.units)
+        if "Load curtail (MW)" in keys(solution)
+            load_curtail = sum(
+                solution["Load curtail (MW)"][b.name][t] for
+                b in instance.buses
+            )
+        end
+        balance = fixed_load - load_curtail - production + ps_load
+
+        # Verify that production equals demand
+        if abs(balance) > tol
+            @error @sprintf(
+                "Non-zero power balance at time %d (%.2f + %.2f - %.2f - %.2f != 0)",
+                t,
+                fixed_load,
+                ps_load,
+                load_curtail,
+                production,
+            )
+            err_count += 1
+        end
+
+        # Verify spinning reserves
+        reserve =
+            sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
+        reserve_shortfall =
+            (instance.shortfall_penalty[t] >= 0) ?
+            solution["Reserve shortfall (MW)"][t] : 0
+
+        if reserve + reserve_shortfall < instance.reserves.spinning[t] - tol
+            @error @sprintf(
+                "Insufficient spinning reserves at time %d (%.2f + %.2f should be %.2f)",
+                t,
+                reserve,
+                reserve_shortfall,
+                instance.reserves.spinning[t],
+            )
+            err_count += 1
+        end
+    end
+
+    return err_count
+end

test/convert_test.jl

@@ -1,19 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment
-
-@testset "convert" begin
-    @testset "EGRET solution" begin
-        solution = UnitCommitment.read_egret_solution("fixtures/egret_output.json.gz")
-        for attr in ["Is on", "Production (MW)", "Production cost (\$)"]
-            @test attr in keys(solution)
-            @test "115_STEAM_1" in keys(solution[attr])
-            @test length(solution[attr]["115_STEAM_1"]) == 48
-        end
-        @test solution["Production cost (\$)"]["315_CT_6"][15:20] == [0., 0., 884.44, 1470.71, 1470.71, 884.44]
-        @test solution["Startup cost (\$)"]["315_CT_6"][15:20] == [0., 0., 5665.23, 0., 0., 0.]
-        @test length(keys(solution["Is on"])) == 154
-    end
-end

test/import/egret_test.jl

@@ -0,0 +1,20 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using UnitCommitment
+
+@testset "read_egret_solution" begin
+    solution =
+        UnitCommitment.read_egret_solution("fixtures/egret_output.json.gz")
+    for attr in ["Is on", "Production (MW)", "Production cost (\$)"]
+        @test attr in keys(solution)
+        @test "115_STEAM_1" in keys(solution[attr])
+        @test length(solution[attr]["115_STEAM_1"]) == 48
+    end
+    @test solution["Production cost (\$)"]["315_CT_6"][15:20] ==
+          [0.0, 0.0, 884.44, 1470.71, 1470.71, 884.44]
+    @test solution["Startup cost (\$)"]["315_CT_6"][15:20] ==
+          [0.0, 0.0, 5665.23, 0.0, 0.0, 0.0]
+    @test length(keys(solution["Is on"])) == 154
+end

test/instance/read_test.jl

@@ -0,0 +1,115 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
+
+@testset "read_benchmark" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+
+    @test length(instance.lines) == 20
+    @test length(instance.buses) == 14
+    @test length(instance.units) == 6
+    @test length(instance.contingencies) == 19
+    @test length(instance.price_sensitive_loads) == 1
+    @test instance.time == 4
+
+    @test instance.lines[5].name == "l5"
+    @test instance.lines[5].source.name == "b2"
+    @test instance.lines[5].target.name == "b5"
+    @test instance.lines[5].reactance ≈ 0.17388
+    @test instance.lines[5].susceptance ≈ 10.037550333
+    @test instance.lines[5].normal_flow_limit == [1e8 for t in 1:4]
+    @test instance.lines[5].emergency_flow_limit == [1e8 for t in 1:4]
+    @test instance.lines[5].flow_limit_penalty == [5e3 for t in 1:4]
+
+    @test instance.lines[1].name == "l1"
+    @test instance.lines[1].source.name == "b1"
+    @test instance.lines[1].target.name == "b2"
+    @test instance.lines[1].reactance ≈ 0.059170
+    @test instance.lines[1].susceptance ≈ 29.496860773945
+    @test instance.lines[1].normal_flow_limit == [300.0 for t in 1:4]
+    @test instance.lines[1].emergency_flow_limit == [400.0 for t in 1:4]
+    @test instance.lines[1].flow_limit_penalty == [1e3 for t in 1:4]
+
+    @test instance.buses[9].name == "b9"
+    @test instance.buses[9].load == [35.36638, 33.25495, 31.67138, 31.14353]
+
+    unit = instance.units[1]
+    @test unit.name == "g1"
+    @test unit.bus.name == "b1"
+    @test unit.ramp_up_limit == 1e6
+    @test unit.ramp_down_limit == 1e6
+    @test unit.startup_limit == 1e6
+    @test unit.shutdown_limit == 1e6
+    @test unit.must_run == [false for t in 1:4]
+    @test unit.min_power_cost == [1400.0 for t in 1:4]
+    @test unit.min_uptime == 1
+    @test unit.min_downtime == 1
+    @test unit.provides_spinning_reserves == [true for t in 1:4]
+    for t in 1:1
+        @test unit.cost_segments[1].mw[t] == 10.0
+        @test unit.cost_segments[2].mw[t] == 20.0
+        @test unit.cost_segments[3].mw[t] == 5.0
+        @test unit.cost_segments[1].cost[t] ≈ 20.0
+        @test unit.cost_segments[2].cost[t] ≈ 30.0
+        @test unit.cost_segments[3].cost[t] ≈ 40.0
+    end
+    @test length(unit.startup_categories) == 3
+    @test unit.startup_categories[1].delay == 1
+    @test unit.startup_categories[2].delay == 2
+    @test unit.startup_categories[3].delay == 3
+    @test unit.startup_categories[1].cost == 1000.0
+    @test unit.startup_categories[2].cost == 1500.0
+    @test unit.startup_categories[3].cost == 2000.0
+
+    unit = instance.units[2]
+    @test unit.name == "g2"
+    @test unit.must_run == [false for t in 1:4]
+
+    unit = instance.units[3]
+    @test unit.name == "g3"
+    @test unit.bus.name == "b3"
+    @test unit.ramp_up_limit == 70.0
+    @test unit.ramp_down_limit == 70.0
+    @test unit.startup_limit == 70.0
+    @test unit.shutdown_limit == 70.0
+    @test unit.must_run == [true for t in 1:4]
+    @test unit.min_power_cost == [0.0 for t in 1:4]
+    @test unit.min_uptime == 1
+    @test unit.min_downtime == 1
+    @test unit.provides_spinning_reserves == [true for t in 1:4]
+    for t in 1:4
+        @test unit.cost_segments[1].mw[t] ≈ 33
+        @test unit.cost_segments[2].mw[t] ≈ 33
+        @test unit.cost_segments[3].mw[t] ≈ 34
+        @test unit.cost_segments[1].cost[t] ≈ 33.75
+        @test unit.cost_segments[2].cost[t] ≈ 38.04
+        @test unit.cost_segments[3].cost[t] ≈ 44.77853
+    end
+
+    @test instance.reserves.spinning == zeros(4)
+
+    @test instance.contingencies[1].lines == [instance.lines[1]]
+    @test instance.contingencies[1].units == []
+
+    load = instance.price_sensitive_loads[1]
+    @test load.name == "ps1"
+    @test load.bus.name == "b3"
+    @test load.revenue == [100.0 for t in 1:4]
+    @test load.demand == [50.0 for t in 1:4]
+end
+
+@testset "read_benchmark sub-hourly" begin
+    instance = UnitCommitment.read_benchmark("test/case14-sub-hourly")
+    @test instance.time == 4
+    unit = instance.units[1]
+    @test unit.name == "g1"
+    @test unit.min_uptime == 2
+    @test unit.min_downtime == 2
+    @test length(unit.startup_categories) == 3
+    @test unit.startup_categories[1].delay == 2
+    @test unit.startup_categories[2].delay == 4
+    @test unit.startup_categories[3].delay == 6
+    @test unit.initial_status == -200
+end

test/instance_test.jl

@@ -1,142 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
-
-@testset "Instance" begin
-    @testset "read" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-
-        @test length(instance.lines) == 20
-        @test length(instance.buses) == 14
-        @test length(instance.units) == 6
-        @test length(instance.contingencies) == 19
-        @test length(instance.price_sensitive_loads) == 1
-        @test instance.time == 4
-
-        @test instance.lines[5].name                 == "l5"
-        @test instance.lines[5].source.name          == "b2"
-        @test instance.lines[5].target.name          == "b5"
-        @test instance.lines[5].reactance            ≈  0.17388
-        @test instance.lines[5].susceptance          ≈  10.037550333
-        @test instance.lines[5].normal_flow_limit    == [1e8 for t in 1:4]
-        @test instance.lines[5].emergency_flow_limit == [1e8 for t in 1:4]
-        @test instance.lines[5].flow_limit_penalty   == [5e3 for t in 1:4]
-
-        @test instance.lines[1].name                 == "l1"
-        @test instance.lines[1].source.name          == "b1"
-        @test instance.lines[1].target.name          == "b2"
-        @test instance.lines[1].reactance            ≈  0.059170
-        @test instance.lines[1].susceptance          ≈  29.496860773945
-        @test instance.lines[1].normal_flow_limit    == [300.0 for t in 1:4]
-        @test instance.lines[1].emergency_flow_limit == [400.0 for t in 1:4]
-        @test instance.lines[1].flow_limit_penalty   == [1e3   for t in 1:4]
-
-        @test instance.buses[9].name == "b9"
-        @test instance.buses[9].load == [35.36638, 33.25495, 31.67138, 31.14353]
-
-        unit = instance.units[1]
-        @test unit.name                          == "g1"
-        @test unit.bus.name                      == "b1"
-        @test unit.ramp_up_limit                 == 1e6
-        @test unit.ramp_down_limit               == 1e6
-        @test unit.startup_limit                 == 1e6
-        @test unit.shutdown_limit                == 1e6
-        @test unit.must_run                      == [false for t in 1:4]
-        @test unit.min_power_cost                == [1400. for t in 1:4]
-        @test unit.min_uptime                    == 1
-        @test unit.min_downtime                  == 1
-        @test unit.provides_spinning_reserves    == [true for t in 1:4]
-        for t in 1:1
-            @test unit.cost_segments[1].mw[t]    == 10.0
-            @test unit.cost_segments[2].mw[t]    == 20.0
-            @test unit.cost_segments[3].mw[t]    == 5.0
-            @test unit.cost_segments[1].cost[t] ≈  20.0
-            @test unit.cost_segments[2].cost[t] ≈  30.0
-            @test unit.cost_segments[3].cost[t] ≈  40.0
-        end
-        @test length(unit.startup_categories) == 3
-        @test unit.startup_categories[1].delay == 1
-        @test unit.startup_categories[2].delay == 2
-        @test unit.startup_categories[3].delay == 3
-        @test unit.startup_categories[1].cost == 1000.0
-        @test unit.startup_categories[2].cost == 1500.0
-        @test unit.startup_categories[3].cost == 2000.0
-        
-        unit = instance.units[2]
-        @test unit.name     == "g2"
-        @test unit.must_run == [false for t in 1:4]
-
-        unit = instance.units[3]
-        @test unit.name                          == "g3"
-        @test unit.bus.name                      == "b3"
-        @test unit.ramp_up_limit                 == 70.0
-        @test unit.ramp_down_limit               == 70.0
-        @test unit.startup_limit                 == 70.0
-        @test unit.shutdown_limit                == 70.0
-        @test unit.must_run                      == [true for t in 1:4]
-        @test unit.min_power_cost                == [0. for t in 1:4]
-        @test unit.min_uptime                    == 1
-        @test unit.min_downtime                  == 1
-        @test unit.provides_spinning_reserves    == [true for t in 1:4]
-        for t in 1:4
-            @test unit.cost_segments[1].mw[t]    ≈ 33
-            @test unit.cost_segments[2].mw[t]    ≈ 33
-            @test unit.cost_segments[3].mw[t]    ≈ 34
-            @test unit.cost_segments[1].cost[t] ≈ 33.75
-            @test unit.cost_segments[2].cost[t] ≈ 38.04
-            @test unit.cost_segments[3].cost[t] ≈ 44.77853
-        end
-        
-        @test instance.reserves.spinning == zeros(4)
-        
-        @test instance.contingencies[1].lines == [instance.lines[1]]
-        @test instance.contingencies[1].units == []
-        
-        load = instance.price_sensitive_loads[1]
-        @test load.name     == "ps1"
-        @test load.bus.name == "b3"
-        @test load.revenue  == [100. for t in 1:4]
-        @test load.demand   == [50.  for t in 1:4]
-    end
-    
-    @testset "slice" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        modified = UnitCommitment.slice(instance, 1:2)
-        
-        # Should update all time-dependent fields
-        @test modified.time == 2
-        @test length(modified.power_balance_penalty) == 2
-        @test length(modified.reserves.spinning) == 2
-        for u in modified.units
-            @test length(u.max_power) == 2
-            @test length(u.min_power) == 2
-            @test length(u.must_run) == 2
-            @test length(u.min_power_cost) == 2
-            @test length(u.provides_spinning_reserves) == 2
-            for s in u.cost_segments
-                @test length(s.mw) == 2
-                @test length(s.cost) == 2
-            end
-        end
-        for b in modified.buses
-            @test length(b.load) == 2
-        end
-        for l in modified.lines
-            @test length(l.normal_flow_limit) == 2
-            @test length(l.emergency_flow_limit) == 2
-            @test length(l.flow_limit_penalty) == 2
-        end
-        for ps in modified.price_sensitive_loads
-            @test length(ps.demand) == 2
-            @test length(ps.revenue) == 2
-        end
-        
-        # Should be able to build model without errors
-        optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
-        model = build_model(instance=modified,
-                            optimizer=optimizer,
-                            variable_names=true)
-    end
-end

test/model/formulations_test.jl

@@ -0,0 +1,74 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using UnitCommitment
+using JuMP
+import UnitCommitment:
+    ArrCon2000,
+    CarArr2006,
+    DamKucRajAta2016,
+    Formulation,
+    Gar1962,
+    KnuOstWat2018,
+    MorLatRam2013,
+    PanGua2016,
+    XavQiuWanThi2019
+
+if ENABLE_LARGE_TESTS
+    using Gurobi
+end
+
+function _small_test(formulation::Formulation)::Nothing
+    instances = ["matpower/case118/2017-02-01", "test/case14"]
+    for instance in instances
+        # Should not crash
+        UnitCommitment.build_model(
+            instance = UnitCommitment.read_benchmark(instance),
+            formulation = formulation,
+        )
+    end
+    return
+end
+
+function _large_test(formulation::Formulation)::Nothing
+    instances = ["pglib-uc/ca/Scenario400_reserves_1"]
+    for instance in instances
+        instance = UnitCommitment.read_benchmark(instance)
+        model = UnitCommitment.build_model(
+            instance = instance,
+            formulation = formulation,
+            optimizer = Gurobi.Optimizer,
+        )
+        UnitCommitment.optimize!(
+            model,
+            XavQiuWanThi2019.Method(two_phase_gap = false, gap_limit = 0.1),
+        )
+        solution = UnitCommitment.solution(model)
+        @test UnitCommitment.validate(instance, solution)
+    end
+    return
+end
+
+function _test(formulation::Formulation)::Nothing
+    _small_test(formulation)
+    if ENABLE_LARGE_TESTS
+        _large_test(formulation)
+    end
+end
+
+@testset "formulations" begin
+    _test(Formulation())
+    _test(Formulation(ramping = ArrCon2000.Ramping()))
+    # _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
+    _test(
+        Formulation(
+            ramping = MorLatRam2013.Ramping(),
+            startup_costs = MorLatRam2013.StartupCosts(),
+        ),
+    )
+    _test(Formulation(ramping = PanGua2016.Ramping()))
+    _test(Formulation(pwl_costs = Gar1962.PwlCosts()))
+    _test(Formulation(pwl_costs = CarArr2006.PwlCosts()))
+    _test(Formulation(pwl_costs = KnuOstWat2018.PwlCosts()))
+end

test/model_test.jl

@@ -1,32 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment, LinearAlgebra, Cbc, JuMP
-
-@testset "Model" begin
-    @testset "Run" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        for line in instance.lines, t in 1:4
-            line.normal_flow_limit[t] = 10.0
-        end
-        optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
-        model = build_model(instance=instance,
-                            optimizer=optimizer,
-                            variable_names=true)
-
-        JuMP.write_to_file(model.mip, "test.mps")
-
-        # Optimize and retrieve solution
-        UnitCommitment.optimize!(model)
-        solution = get_solution(model)
-        
-        # Verify solution
-        @test UnitCommitment.validate(instance, solution)
-
-        # Reoptimize with fixed solution
-        UnitCommitment.fix!(model, solution)
-        UnitCommitment.optimize!(model)
-        @test UnitCommitment.validate(instance, solution)
-    end
-end

test/runtests.jl

@@ -3,13 +3,36 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 
 using Test
+using UnitCommitment
+
+UnitCommitment._setup_logger()
+
+const ENABLE_LARGE_TESTS = ("UCJL_LARGE_TESTS" in keys(ENV))
 
 @testset "UnitCommitment" begin
-    include("instance_test.jl")
-    include("model_test.jl")
-    include("sensitivity_test.jl")
-    include("screening_test.jl")
-    include("convert_test.jl")
-    include("validate_test.jl")
-    include("initcond_test.jl")
+    include("usage.jl")
+    @testset "import" begin
+        include("import/egret_test.jl")
+    end
+    @testset "instance" begin
+        include("instance/read_test.jl")
+    end
+    @testset "model" begin
+        include("model/formulations_test.jl")
+    end
+    @testset "XavQiuWanThi19" begin
+        include("solution/methods/XavQiuWanThi19/filter_test.jl")
+        include("solution/methods/XavQiuWanThi19/find_test.jl")
+        include("solution/methods/XavQiuWanThi19/sensitivity_test.jl")
+    end
+    @testset "transform" begin
+        include("transform/initcond_test.jl")
+        include("transform/slice_test.jl")
+        @testset "randomize" begin
+            include("transform/randomize/XavQiuAhm2021_test.jl")
+        end
+    end
+    @testset "validation" begin
+        include("validation/repair_test.jl")
+    end
 end

test/screening_test.jl

@@ -1,75 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment, Test, LinearAlgebra
-
-@testset "Screening" begin
-    @testset "Violation filter" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        filter = ViolationFilter(max_per_line=1, max_total=2)
-
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=nothing,
-                                amount=100.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=instance.lines[1],
-                                amount=300.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=instance.lines[5],
-                                amount=500.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=instance.lines[4],
-                                amount=400.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[2],
-                                outage_line=instance.lines[1],
-                                amount=200.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[2],
-                                outage_line=instance.lines[8],
-                                amount=100.))
-
-        actual = query(filter)
-        expected = [Violation(time=1,
-                              monitored_line=instance.lines[2],
-                              outage_line=instance.lines[1],
-                              amount=200.),
-                    Violation(time=1,
-                              monitored_line=instance.lines[1],
-                              outage_line=instance.lines[5],
-                              amount=500.)]
-        @test actual == expected
-    end
-    
-    @testset "find_violations" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        for line in instance.lines, t in 1:instance.time
-            line.normal_flow_limit[t] = 1.0
-            line.emergency_flow_limit[t] = 1.0
-        end
-        isf = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                     buses=instance.buses)
-        lodf = UnitCommitment.line_outage_factors(lines=instance.lines,
-                                                  buses=instance.buses,
-                                                  isf=isf)
-        inj = [1000.0 for b in 1:13, t in 1:instance.time]
-        overflow = [0.0 for l in instance.lines, t in 1:instance.time]
-        violations = UnitCommitment.find_violations(instance=instance,
-                                                    net_injections=inj,
-                                                    overflow=overflow,
-                                                    isf=isf,
-                                                    lodf=lodf)
-        
-        @test length(violations) == 20
-    end
-end

test/sensitivity_test.jl

@@ -1,115 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment, Test, LinearAlgebra
-
-@testset "Sensitivity" begin
-    @testset "Susceptance matrix" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        actual = UnitCommitment.susceptance_matrix(instance.lines)
-        @test size(actual) == (20, 20)
-        expected = Diagonal([29.5,  7.83,  8.82, 9.9,  10.04,
-                             10.2,  41.45, 8.35, 3.14, 6.93,
-                             8.77,  6.82,  13.4, 9.91, 15.87,
-                             20.65, 6.46,  9.09, 8.73, 5.02])
-        @test round.(actual, digits=2) == expected
-    end
-    
-    @testset "Reduced incidence matrix" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        actual = UnitCommitment.reduced_incidence_matrix(lines=instance.lines,
-                                                         buses=instance.buses)
-        @test size(actual) == (20, 13)
-        @test actual[1,   1]  == -1.0
-        @test actual[3,   1]  ==  1.0
-        @test actual[4,   1]  ==  1.0
-        @test actual[5,   1]  ==  1.0
-        @test actual[3,   2]  == -1.0
-        @test actual[6,   2]  ==  1.0
-        @test actual[4,   3]  == -1.0
-        @test actual[6,   3]  == -1.0
-        @test actual[7,   3]  ==  1.0
-        @test actual[8,   3]  ==  1.0
-        @test actual[9,   3]  ==  1.0
-        @test actual[2,   4]  == -1.0
-        @test actual[5,   4]  == -1.0
-        @test actual[7,   4]  == -1.0
-        @test actual[10,  4]  ==  1.0
-        @test actual[10,  5]  == -1.0
-        @test actual[11,  5]  ==  1.0
-        @test actual[12,  5]  ==  1.0
-        @test actual[13,  5]  ==  1.0
-        @test actual[8,   6]  == -1.0
-        @test actual[14,  6]  ==  1.0
-        @test actual[15,  6]  ==  1.0
-        @test actual[14,  7]  == -1.0
-        @test actual[9,   8]  == -1.0
-        @test actual[15,  8]  == -1.0
-        @test actual[16,  8]  ==  1.0
-        @test actual[17,  8]  ==  1.0
-        @test actual[16,  9]  == -1.0
-        @test actual[18,  9]  ==  1.0
-        @test actual[11, 10]  == -1.0
-        @test actual[18, 10]  == -1.0
-        @test actual[12, 11]  == -1.0
-        @test actual[19, 11]  ==  1.0
-        @test actual[13, 12]  == -1.0
-        @test actual[19, 12]  == -1.0
-        @test actual[20, 12]  ==  1.0
-        @test actual[17, 13]  == -1.0
-        @test actual[20, 13]  == -1.0
-    end
-    
-    @testset "Injection Shift Factors (ISF)" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        actual = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                        buses=instance.buses)
-        @test size(actual) == (20, 13)
-        @test round.(actual, digits=2) == [
-            -0.84 -0.75 -0.67 -0.61 -0.63 -0.66 -0.66 -0.65 -0.65 -0.64 -0.63 -0.63 -0.64;
-            -0.16 -0.25 -0.33 -0.39 -0.37 -0.34 -0.34 -0.35 -0.35 -0.36 -0.37 -0.37 -0.36;
-             0.03 -0.53 -0.15 -0.1  -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13;
-             0.06 -0.14 -0.32 -0.22 -0.25 -0.3  -0.3  -0.29 -0.28 -0.27 -0.25 -0.26 -0.27;
-             0.08 -0.07 -0.2  -0.29 -0.26 -0.22 -0.22 -0.22 -0.23 -0.25 -0.26 -0.26 -0.24;
-             0.03  0.47 -0.15 -0.1  -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13;
-             0.08  0.31  0.5  -0.3  -0.03  0.36  0.36  0.28  0.23  0.1  -0.0   0.02  0.17;
-             0.0   0.01  0.02 -0.01 -0.22 -0.63 -0.63 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36;
-             0.0   0.01  0.01 -0.01 -0.12 -0.17 -0.17 -0.26 -0.24 -0.18 -0.14 -0.14 -0.21;
-            -0.0  -0.02 -0.03  0.02 -0.66 -0.2  -0.2  -0.29 -0.36 -0.5  -0.63 -0.61 -0.43;
-            -0.0  -0.01 -0.02  0.01  0.21 -0.12 -0.12 -0.17 -0.28 -0.53  0.18  0.15 -0.03;
-            -0.0  -0.0  -0.0   0.0   0.03 -0.02 -0.02 -0.03 -0.02  0.01 -0.52 -0.17 -0.09;
-            -0.0  -0.01 -0.01  0.01  0.11 -0.06 -0.06 -0.09 -0.05  0.02 -0.28 -0.59 -0.31;
-            -0.0  -0.0  -0.0  -0.0  -0.0  -0.0  -1.0  -0.0  -0.0  -0.0  -0.0  -0.0   0.0 ;
-             0.0   0.01  0.02 -0.01 -0.22  0.37  0.37 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36;
-             0.0   0.01  0.02 -0.01 -0.21  0.12  0.12  0.17 -0.72 -0.47 -0.18 -0.15  0.03;
-             0.0   0.01  0.01 -0.01 -0.14  0.08  0.08  0.12  0.07 -0.03 -0.2  -0.24 -0.6 ;
-             0.0   0.01  0.02 -0.01 -0.21  0.12  0.12  0.17  0.28 -0.47 -0.18 -0.15  0.03;
-            -0.0  -0.0  -0.0   0.0   0.03 -0.02 -0.02 -0.03 -0.02  0.01  0.48 -0.17 -0.09;
-            -0.0  -0.01 -0.01  0.01  0.14 -0.08 -0.08 -0.12 -0.07  0.03  0.2   0.24 -0.4  ]
-    end
-
-    @testset "Line Outage Distribution Factors (LODF)" begin
-        instance = UnitCommitment.read_benchmark("test/case14")
-        isf_before = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                            buses=instance.buses)
-        lodf = UnitCommitment.line_outage_factors(lines=instance.lines,
-                                                  buses=instance.buses,
-                                                  isf=isf_before)
-        for contingency in instance.contingencies
-            for lc in contingency.lines
-                prev_susceptance = lc.susceptance
-                lc.susceptance = 0.0
-                isf_after =  UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                                    buses=instance.buses)
-                lc.susceptance = prev_susceptance
-                for lm in instance.lines
-                    expected = isf_after[lm.offset, :]
-                    actual = isf_before[lm.offset, :] +
-                                 lodf[lm.offset, lc.offset] * isf_before[lc.offset, :]
-                    @test norm(expected - actual) < 1e-6
-                end
-            end
-        end
-    end
-end

test/solution/methods/XavQiuWanThi19/filter_test.jl

@@ -0,0 +1,83 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using UnitCommitment, Test, LinearAlgebra
+import UnitCommitment: _Violation, _offer, _query
+
+@testset "_ViolationFilter" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+    filter = UnitCommitment._ViolationFilter(max_per_line = 1, max_total = 2)
+
+    _offer(
+        filter,
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[1],
+            outage_line = nothing,
+            amount = 100.0,
+        ),
+    )
+    _offer(
+        filter,
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[1],
+            outage_line = instance.lines[1],
+            amount = 300.0,
+        ),
+    )
+    _offer(
+        filter,
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[1],
+            outage_line = instance.lines[5],
+            amount = 500.0,
+        ),
+    )
+    _offer(
+        filter,
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[1],
+            outage_line = instance.lines[4],
+            amount = 400.0,
+        ),
+    )
+    _offer(
+        filter,
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[2],
+            outage_line = instance.lines[1],
+            amount = 200.0,
+        ),
+    )
+    _offer(
+        filter,
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[2],
+            outage_line = instance.lines[8],
+            amount = 100.0,
+        ),
+    )
+
+    actual = _query(filter)
+    expected = [
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[2],
+            outage_line = instance.lines[1],
+            amount = 200.0,
+        ),
+        _Violation(
+            time = 1,
+            monitored_line = instance.lines[1],
+            outage_line = instance.lines[5],
+            amount = 500.0,
+        ),
+    ]
+    @test actual == expected
+end

test/solution/methods/XavQiuWanThi19/find_test.jl

@@ -0,0 +1,35 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using UnitCommitment, Test, LinearAlgebra
+import UnitCommitment: _Violation, _offer, _query
+
+@testset "find_violations" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+    for line in instance.lines, t in 1:instance.time
+        line.normal_flow_limit[t] = 1.0
+        line.emergency_flow_limit[t] = 1.0
+    end
+    isf = UnitCommitment._injection_shift_factors(
+        lines = instance.lines,
+        buses = instance.buses,
+    )
+    lodf = UnitCommitment._line_outage_factors(
+        lines = instance.lines,
+        buses = instance.buses,
+        isf = isf,
+    )
+    inj = [1000.0 for b in 1:13, t in 1:instance.time]
+    overflow = [0.0 for l in instance.lines, t in 1:instance.time]
+    violations = UnitCommitment._find_violations(
+        instance = instance,
+        net_injections = inj,
+        overflow = overflow,
+        isf = isf,
+        lodf = lodf,
+        max_per_line = 1,
+        max_per_period = 5,
+    )
+    @test length(violations) == 20
+end

test/solution/methods/XavQiuWanThi19/sensitivity_test.jl

@@ -0,0 +1,143 @@
+# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using UnitCommitment, Test, LinearAlgebra
+
+@testset "_susceptance_matrix" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+    actual = UnitCommitment._susceptance_matrix(instance.lines)
+    @test size(actual) == (20, 20)
+    expected = Diagonal([
+        29.5,
+        7.83,
+        8.82,
+        9.9,
+        10.04,
+        10.2,
+        41.45,
+        8.35,
+        3.14,
+        6.93,
+        8.77,
+        6.82,
+        13.4,
+        9.91,
+        15.87,
+        20.65,
+        6.46,
+        9.09,
+        8.73,
+        5.02,
+    ])
+    @test round.(actual, digits = 2) == expected
+end
+
+@testset "_reduced_incidence_matrix" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+    actual = UnitCommitment._reduced_incidence_matrix(
+        lines = instance.lines,
+        buses = instance.buses,
+    )
+    @test size(actual) == (20, 13)
+    @test actual[1, 1] == -1.0
+    @test actual[3, 1] == 1.0
+    @test actual[4, 1] == 1.0
+    @test actual[5, 1] == 1.0
+    @test actual[3, 2] == -1.0
+    @test actual[6, 2] == 1.0
+    @test actual[4, 3] == -1.0
+    @test actual[6, 3] == -1.0
+    @test actual[7, 3] == 1.0
+    @test actual[8, 3] == 1.0
+    @test actual[9, 3] == 1.0
+    @test actual[2, 4] == -1.0
+    @test actual[5, 4] == -1.0
+    @test actual[7, 4] == -1.0
+    @test actual[10, 4] == 1.0
+    @test actual[10, 5] == -1.0
+    @test actual[11, 5] == 1.0
+    @test actual[12, 5] == 1.0
+    @test actual[13, 5] == 1.0
+    @test actual[8, 6] == -1.0
+    @test actual[14, 6] == 1.0
+    @test actual[15, 6] == 1.0
+    @test actual[14, 7] == -1.0
+    @test actual[9, 8] == -1.0
+    @test actual[15, 8] == -1.0
+    @test actual[16, 8] == 1.0
+    @test actual[17, 8] == 1.0
+    @test actual[16, 9] == -1.0
+    @test actual[18, 9] == 1.0
+    @test actual[11, 10] == -1.0
+    @test actual[18, 10] == -1.0
+    @test actual[12, 11] == -1.0
+    @test actual[19, 11] == 1.0
+    @test actual[13, 12] == -1.0
+    @test actual[19, 12] == -1.0
+    @test actual[20, 12] == 1.0
+    @test actual[17, 13] == -1.0
+    @test actual[20, 13] == -1.0
+end
+
+@testset "_injection_shift_factors" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+    actual = UnitCommitment._injection_shift_factors(
+        lines = instance.lines,
+        buses = instance.buses,
+    )
+    @test size(actual) == (20, 13)
+    @test round.(actual, digits = 2) == [
+        -0.84 -0.75 -0.67 -0.61 -0.63 -0.66 -0.66 -0.65 -0.65 -0.64 -0.63 -0.63 -0.64
+        -0.16 -0.25 -0.33 -0.39 -0.37 -0.34 -0.34 -0.35 -0.35 -0.36 -0.37 -0.37 -0.36
+        0.03 -0.53 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
+        0.06 -0.14 -0.32 -0.22 -0.25 -0.3 -0.3 -0.29 -0.28 -0.27 -0.25 -0.26 -0.27
+        0.08 -0.07 -0.2 -0.29 -0.26 -0.22 -0.22 -0.22 -0.23 -0.25 -0.26 -0.26 -0.24
+        0.03 0.47 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
+        0.08 0.31 0.5 -0.3 -0.03 0.36 0.36 0.28 0.23 0.1 -0.0 0.02 0.17
+        0.0 0.01 0.02 -0.01 -0.22 -0.63 -0.63 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
+        0.0 0.01 0.01 -0.01 -0.12 -0.17 -0.17 -0.26 -0.24 -0.18 -0.14 -0.14 -0.21
+        -0.0 -0.02 -0.03 0.02 -0.66 -0.2 -0.2 -0.29 -0.36 -0.5 -0.63 -0.61 -0.43
+        -0.0 -0.01 -0.02 0.01 0.21 -0.12 -0.12 -0.17 -0.28 -0.53 0.18 0.15 -0.03
+        -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 -0.52 -0.17 -0.09
+        -0.0 -0.01 -0.01 0.01 0.11 -0.06 -0.06 -0.09 -0.05 0.02 -0.28 -0.59 -0.31
+        -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -1.0 -0.0 -0.0 -0.0 -0.0 -0.0 0.0
+        0.0 0.01 0.02 -0.01 -0.22 0.37 0.37 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
+        0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 -0.72 -0.47 -0.18 -0.15 0.03
+        0.0 0.01 0.01 -0.01 -0.14 0.08 0.08 0.12 0.07 -0.03 -0.2 -0.24 -0.6
+        0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 0.28 -0.47 -0.18 -0.15 0.03
+        -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 0.48 -0.17 -0.09
+        -0.0 -0.01 -0.01 0.01 0.14 -0.08 -0.08 -0.12 -0.07 0.03 0.2 0.24 -0.4
+    ]
+end
+
+@testset "_line_outage_factors" begin
+    instance = UnitCommitment.read_benchmark("test/case14")
+    isf_before = UnitCommitment._injection_shift_factors(
+        lines = instance.lines,
+        buses = instance.buses,
+    )
+    lodf = UnitCommitment._line_outage_factors(
+        lines = instance.lines,
+        buses = instance.buses,
+        isf = isf_before,
+    )
+    for contingency in instance.contingencies
+        for lc in contingency.lines
+            prev_susceptance = lc.susceptance
+            lc.susceptance = 0.0
+            isf_after = UnitCommitment._injection_shift_factors(
+                lines = instance.lines,
+                buses = instance.buses,
+            )
+            lc.susceptance = prev_susceptance
+            for lm in instance.lines
+                expected = isf_after[lm.offset, :]
+                actual =
+                    isf_before[lm.offset, :] +
+                    lodf[lm.offset, lc.offset] * isf_before[lc.offset, :]
+                @test norm(expected - actual) < 1e-6
+            end
+        end
+    end
+end