GitHub Actions: Remove older non-LTS Julia versions

Bump JuMP version to 1.0
Remove Gurobi from test dependencies; remove large tests
2025-12-06 00:08:52 -06:00 · 2022-04-16 11:53:12 -05:00 · 2022-04-16 11:52:21 -05:00 · 2022-04-16 11:43:09 -05:00 · 2022-04-16 11:34:25 -05:00 · 2022-04-16 11:34:14 -05:00
314 changed files with 6001 additions and 3658 deletions
--- a/.JuliaFormatter.toml
+++ b/.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
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.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]
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.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
--- a/.github/workflows/benchmark.yml
+++ b/.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/*
-
--- a/.github/workflows/lint.yml
+++ b/.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)
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -1,12 +1,16 @@
 name: Tests
-on: [push, pull_request]
+on:
+  push:
+  pull_request:
+  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: ['1.6', '1.7']
+        julia-arch: [x64]
        os: [ubuntu-latest, windows-latest, macOS-latest]
        exclude:
          - os: macOS-latest
--- a/.gitignore
+++ b/.gitignore
@@ -1,16 +1,38 @@
 *.bak
 *.gz
-*.lastrun
-*.so
-*.mps
 *.ipynb
+*.lastrun
+*.mps
+*.so
+*/Manifest.toml
+.AppleDB
+.AppleDesktop
+.AppleDouble
+.DS_Store
+.DocumentRevisions-V100
+.LSOverride
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+._*
+.apdisk
+.com.apple.timemachine.donotpresent
+.fseventsd
 .ipy*
+.vscode
+Icon
+Manifest.toml
+Network Trash Folder
+TODO.md
+Temporary Items
 benchmark/results
 benchmark/runs
 benchmark/tables
+benchmark/tmp.json
 build
+docs/_build
 instances/**/*.json
 instances/_source
 local
 notebooks
-TODO.md
--- a/CHANGELOG.md
+++ b/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.
--- a/30
+++ b/30
@@ -2,30 +2,22 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.

-JULIA := julia --color=yes --project=@.
-MKDOCS := ~/.local/bin/mkdocs
-SRC_FILES := $(wildcard src/*.jl) $(wildcard test/*.jl)
-VERSION := 0.1
-
-build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
-	mkdir -p build
-	$(JULIA) src/sysimage.jl
+VERSION := 0.2

 clean:
-	rm -rf build/*
+	rm -rfv build Manifest.toml test/Manifest.toml deps/formatter/build deps/formatter/Manifest.toml

 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)/
 	
-docs-push:
-	rsync -avP docs/ isoron@axavier.org:/www/axavier.org/projects/UnitCommitment.jl/
+format:
+	cd deps/formatter; ../../juliaw format.jl

-install-deps-docs:
-	pip install --user mkdocs mkdocs-cinder python-markdown-math
+test: test/Manifest.toml
+	./juliaw test/runtests.jl

-test: build/sysimage.so
-	@echo Running tests...
-	cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl | tee ../build/test.log
+test/Manifest.toml: test/Project.toml
+	julia --project=test -e "using Pkg; Pkg.instantiate()"

-.PHONY: docs docs-push build test
+.PHONY: docs test format install-deps
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,367 +0,0 @@
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-
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-deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "19c33675cdeb572c1b17f96c492459d4f4958036"
-uuid = "656ef2d0-ae68-5445-9ca0-591084a874a2"
-version = "0.3.10+0"
-
-[[OpenSpecFun_jll]]
-deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "9db77584158d0ab52307f8c04f8e7c08ca76b5b3"
-uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
-version = "0.5.3+4"
-
-[[OrderedCollections]]
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-uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
-version = "1.3.2"
-
-[[Osi_jll]]
-deps = ["CoinUtils_jll", "CompilerSupportLibraries_jll", "Libdl", "OpenBLAS32_jll", "Pkg"]
-git-tree-sha1 = "bd436a97280df40938e66ae8d18e57aceb072856"
-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"
--- a/Project.toml
+++ b/Project.toml
@@ -2,10 +2,12 @@ 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"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
@@ -14,21 +16,15 @@ 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"
-MathOptInterface = "0.9"
+JuMP = "1"
+MathOptInterface = "1"
 PackageCompiler = "1"
 julia = "1"
-
-[extras]
-Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Cbc", "Test"]
--- a/README.md
+++ b/README.md
@@ -1,45 +1,145 @@
-<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"><img src="https://github.com/iSoron/UnitCommitment.jl/workflows/Benchmark/badge.svg"></img></a>
-<a href="https://zenodo.org/badge/latestdoi/263667075"><img src="https://zenodo.org/badge/263667075.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.

+## Package Components

-# UnitCommitment.jl
-
-**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)
+* **Ogün Yurdakul** (Technische Universität Berlin)
 * **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.
+```

--- a/benchmark/Makefile
+++ b/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
--- a/benchmark/Manifest.toml
+++ b/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"
-
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-
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-path = ".."
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-
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--- a/benchmark/Project.toml
+++ b/benchmark/Project.toml
@@ -1,4 +1,5 @@
 [deps]
+DocOpt = "968ba79b-81e4-546f-ab3a-2eecfa62a9db"
 Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
--- a/benchmark/run.jl
+++ b/benchmark/run.jl
@@ -2,60 +2,208 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.

+doc = """UnitCommitment.jl Benchmark Runner
+
+Usage:
+  run.jl [-s ARG]... [-m ARG]... [-c ARG]... [-f ARG]... [options]
+
+Examples:
+
+    1. Benchmark all solvers, methods and formulations:
+
+        julia run.jl
+
+    2. Benchmark formulations "default" and "ArrCon200" using Gurobi:
+
+        julia run.jl -s gurobi -f default -f ArrCon2000
+
+    3. Benchmark a few test cases, using all solvers, methods and formulations:
+
+        julia run.jl -c or-lib/20_0_1_w -c matpower/case1888rte/2017-02-01
+
+    4. Solve 4 test cases in parallel, with 2 threads available per worker:
+
+        JULIA_NUM_THREADS=2 julia --procs 4 run.jl
+
+Options:
+  -h --help             Show this screen.
+  -s --solver=ARG       Mixed-integer linear solver (e.g. gurobi)
+  -c --case=ARG         Unit commitment test case (e.g. or-lib/20_0_1_w)
+  -m --method=ARG       Solution method (e.g. default)
+  -f --formulation=ARG  Formulation (e.g. ArrCon2000)
+  --time-limit=ARG      Time limit in seconds [default: 3600]
+  --gap=ARG             Relative MIP gap tolerance [default: 0.001]
+  --trials=ARG          Number of trials [default: 5]
+"""
+
+using Distributed
+using Pkg
+Pkg.activate(".")
+@everywhere using Pkg
+@everywhere Pkg.activate(".")
+
+using DocOpt
+args = docopt(doc)
+
+@everywhere using UnitCommitment
+@everywhere UnitCommitment._setup_logger()
+
 using UnitCommitment
-using JuMP
 using Gurobi
-using JSON
 using Logging
-using Printf
-using LinearAlgebra
+using JuMP

-function main()
-    basename, suffix = split(ARGS[1], ".")
-    solution_filename = "results/$basename.$suffix.sol.json"
-    model_filename = "results/$basename.$suffix.mps.gz"
+import UnitCommitment:
+    ArrCon2000,
+    CarArr2006,
+    DamKucRajAta2016,
+    Formulation,
+    Gar1962,
+    KnuOstWat2018,
+    MorLatRam2013,
+    PanGua2016,
+    XavQiuWanThi2019

-    time_limit = 60 * 20
+# Benchmark test cases
+# -----------------------------------------------------------------------------
+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",
+    "matpower/case1888rte/2017-02-01",
+    "matpower/case1951rte/2017-02-01",
+    "matpower/case2848rte/2017-02-01",
+    "matpower/case3012wp/2017-02-01",
+    "matpower/case3375wp/2017-02-01",
+    "matpower/case6468rte/2017-02-01",
+    "matpower/case6515rte/2017-02-01",
+]

-    BLAS.set_num_threads(4)
-    global_logger(TimeLogger(initial_time = time()))
+# Formulations
+# -----------------------------------------------------------------------------
+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()),
+)

-    total_time = @elapsed begin
-        @info "Reading: $basename"
-        time_read = @elapsed begin
-            instance = UnitCommitment.read_benchmark(basename)
+# Solution methods
+# -----------------------------------------------------------------------------
+const gap_limit = parse(Float64, args["--gap"])
+const time_limit = parse(Float64, args["--time-limit"])
+methods = Dict(
+    "default" => XavQiuWanThi2019.Method(
+        time_limit = time_limit,
+        gap_limit = gap_limit,
+    ),
+)
+
+# MIP solvers
+# -----------------------------------------------------------------------------
+optimizers = Dict(
+    "gurobi" => optimizer_with_attributes(
+        Gurobi.Optimizer,
+        "Threads" => Threads.nthreads(),
+    ),
+)
+
+# Parse command line arguments
+# -----------------------------------------------------------------------------
+if !isempty(args["--case"])
+    cases = args["--case"]
 end
-        @info @sprintf("Read problem in %.2f seconds", time_read)
+if !isempty(args["--formulation"])
+    formulations = filter(p -> p.first in args["--formulation"], formulations)
+end
+if !isempty(args["--method"])
+    methods = filter(p -> p.first in args["--method"], methods)
+end
+if !isempty(args["--solver"])
+    optimizers = filter(p -> p.first in args["--solver"], optimizers)
+end
+const ntrials = parse(Int, args["--trials"])

-        time_model = @elapsed begin
-            model = build_model(instance=instance,
-                                optimizer=optimizer_with_attributes(Gurobi.Optimizer,
-                                                                    "Threads" => 4,
-                                                                    "Seed" => rand(1:1000),
-                                                                    ))
+# Print benchmark settings
+# -----------------------------------------------------------------------------
+function printlist(d::Dict)
+    for key in keys(d)
+        @info "  - $key"
+    end
 end

-        @info "Optimizing..."
-        BLAS.set_num_threads(1)
-        UnitCommitment.optimize!(model, time_limit=time_limit, gap_limit=1e-3)
-
+function printlist(d::Vector)
+    for key in d
+        @info "  - $key"
    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 "Computational environment:"
+@info "  - CPU: $(Sys.cpu_info()[1].model)"
+@info "  - Logical CPU cores: $(length(Sys.cpu_info()))"
+@info "  - System memory: $(round(Sys.total_memory() / 2^30, digits=2)) GiB"
+@info "  - Available workers: $(nworkers())"
+@info "  - Available threads per worker: $(Threads.nthreads())"

-    @info "Setting variable names..."
-    UnitCommitment.set_variable_names!(model)
+@info "Parameters:"
+@info "  - Number of trials: $ntrials"
+@info "  - Time limit (s): $time_limit"
+@info "  - Relative MIP gap tolerance: $gap_limit"

-    @info "Exporting model..."
-    JuMP.write_to_file(model.mip, model_filename)
-end
+@info "Solvers:"
+printlist(optimizers)

-main()
+@info "Methods:"
+printlist(methods)
+
+@info "Formulations:"
+printlist(formulations)
+
+@info "Cases:"
+printlist(cases)
+
+# Run benchmarks
+# -----------------------------------------------------------------------------
+UnitCommitment._run_benchmarks(
+    cases = cases,
+    formulations = formulations,
+    methods = methods,
+    optimizers = optimizers,
+    trials = 1:ntrials,
+)
--- a/benchmark/scripts/compare.py
+++ b/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,
+# 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="Name",
-            color="tab:red",
-            capsize=0.15,
+    y="Instance",
+    color="tab:purple",
    errcolor="k",
-            errwidth=1.25)
-plt.axvline(1.0, linestyle="--", color="k")
-plt.tight_layout()
+    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")
--- a/benchmark/scripts/table.py
+++ b/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):
@@ -26,9 +28,9 @@ def process_all_log_files():

 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
@@ -49,40 +51,59 @@ def process(filename):
    # 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))
@@ -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))
@@ -117,6 +141,14 @@ def process(filename):
            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",
+    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)",
-                color="tab:red",
-                capsize=0.15,
+        hue="Group",
        errcolor="k",
        errwidth=1.25,
-                data=benchmark);
+        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__":
--- a/deps/formatter/Project.toml
+++ b/deps/formatter/Project.toml
@@ -0,0 +1,5 @@
+[deps]
+JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
+
+[compat]
+JuliaFormatter = "0.14.4"
--- a/deps/formatter/format.jl
+++ b/deps/formatter/format.jl
@@ -0,0 +1,9 @@
+using JuliaFormatter
+format(
+    [
+        "../../src", 
+        "../../test",
+        "../../benchmark/run.jl",
+    ],
+    verbose=true,
+)
--- a/docs/Makefile
+++ b/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)
--- a/src/docs/images/cost_curve.png
+++ b/src/docs/images/cost_curve.png
--- a/docs/_static/custom.css
+++ b/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;
+}
--- a/docs/conf.py
+++ b/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>"
--- a/src/docs/format.md
+++ b/src/docs/format.md
@@ -1,41 +1,57 @@
+```{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:

-* [Parameters](#parameters)
-* [Buses](#buses)
-* [Generators](#generators)
-* [Price-sensitive loads](#price-sensitive-loads)
-* [Transmission lines](#transmission-lines)
-* [Reserves](#reserves)
-* [Contingencies](#contingencies)
+* Parameters
+* Buses
+* Generators
+* Price-sensitive loads
+* Transmission lines
+* Reserves
+* Contingencies

-Each section is described in detail below. For a complete example, see [case14.json](https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/dev/instances/matpower-24h/case14.json).
+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 $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
+| `Flexiramp penalty ($/MW)` | Penalty for system-wide shortage in meeting flexible ramping product requirements (in $/MW). This is charged per time step. | `500` | 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 ($/MW)": -1.0,
+        "Flexiramp penalty ($/MW)": 100.0
    }
 }
 ```

-### 1.2 Buses
+### Buses

 This section describes the characteristics of each bus in the system. 

@@ -64,40 +80,42 @@ 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
+| `Provides flexible capacity?`    | If `true`, this generator may provide flexible ramping product (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.
@@ -122,6 +140,7 @@ Note that this curve also specifies the production limits. Specifically, the fir
            "Initial status (h)": 12,
            "Must run?": false,
            "Provides spinning reserves?": true,
+            "Provides flexible capacity?": false,
        },
        "gen2": {
            "Bus": "b5",
@@ -133,7 +152,7 @@ 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 +176,7 @@ 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 +186,9 @@ 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

@@ -190,16 +209,18 @@ 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.
+This section describes the hourly amount of reserves required.


 | Key                   | Description                                        | Default   |  Time series?
 | :-------------------- | :------------------------------------------------- | --------- |  :----:
 | `Spinning (MW)`       | Minimum amount of system-wide spinning reserves (in MW). Only generators which are online may provide this reserve. | `0.0` | Y
+| `Up-flexiramp (MW)`       | Minimum amount of system-wide upward flexible ramping product (in MW). Only generators which are online may provide this reserve. | `0.0` | Y
+| `Down-flexiramp (MW)`       | Minimum amount of system-wide downward flexible ramping product (in MW). Only generators which are online may provide this reserve. | `0.0` | Y

-#### Example
+#### Example 1

 ```json
 {
@@ -214,7 +235,28 @@ This section describes the hourly amount of operating reserves required.
 }
 ```

-### 1.7 Contingencies
+#### Example 2
+
+```json
+{
+    "Reserves": {
+        "Up-flexiramp (MW)": [
+            20.31042,
+            23.65273,
+            27.41784,
+            25.34057
+        ],
+         "Down-flexiramp (MW)": [
+            19.41546,
+            21.45377,
+            23.53402,
+            24.80973
+        ]
+    }
+}
+```
+
+### Contingencies

 This section describes credible contingency scenarios in the optimization, such as the loss of a transmission line or generator.

@@ -239,11 +281,11 @@ 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 +294,30 @@ 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)
-* Network topology remains the same for all time periods
-* Only N-1 transmission contingencies are supported. Generator contingencies are not supported.
-* Time-varying minimum production amounts are not currently compatible with ramp/startup/shutdown limits.
+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

-## 2. Output Data Format
+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.
+* Upward and downward flexible ramping products can only be acquired under the WanHob2016 formulation, which does not support spinning reserves. 
+* Network topology remains the same for all time periods
+* 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.
+
+
--- a/docs/index.md
+++ b/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
+```
+
--- a/src/docs/instances.md
+++ b/src/docs/instances.md
@@ -1,13 +1,23 @@
-# Instances
+```{sectnum}
+---
+start: 3
+depth: 2
+suffix: .
+---
+```

-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. 
-If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources.
+Instances
+=========

-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).
+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. If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources, as listed below. Benchmark instances can be loaded with `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md).

-## 1. MATPOWER
+```{warning}
+The instances included in UC.jl are still under development and may change in the future. If you use these instances in your research, for reproducibility, you should specify what version of UC.jl they came from.
+```
+
+
+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.

@@ -25,93 +35,68 @@ 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.

 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
 | `matpower/case14/2017-02-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
-| `matpower/case14/2017-08-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
 | `matpower/case30/2017-02-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
-| `matpower/case30/2017-08-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
 | `matpower/case57/2017-02-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
-| `matpower/case57/2017-08-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
 | `matpower/case118/2017-02-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
-| `matpower/case118/2017-08-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
 | `matpower/case300/2017-02-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
-| `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.

 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
 | `matpower/case2383wp/2017-02-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
-| `matpower/case2383wp/2017-08-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
 | `matpower/case2736sp/2017-02-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
-| `matpower/case2736sp/2017-08-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
 | `matpower/case2737sop/2017-02-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
-| `matpower/case2737sop/2017-08-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
 | `matpower/case2746wop/2017-02-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
-| `matpower/case2746wop/2017-08-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
 | `matpower/case2746wp/2017-02-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
-| `matpower/case2746wp/2017-08-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
 | `matpower/case3012wp/2017-02-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
-| `matpower/case3012wp/2017-08-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
 | `matpower/case3120sp/2017-02-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
-| `matpower/case3120sp/2017-08-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
 | `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.

 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
 | `matpower/case89pegase/2017-02-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case89pegase/2017-08-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
 | `matpower/case1354pegase/2017-02-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case1354pegase/2017-08-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
 | `matpower/case2869pegase/2017-02-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case2869pegase/2017-08-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
 | `matpower/case9241pegase/2017-02-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case9241pegase/2017-08-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
 | `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.

 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
 | `matpower/case1888rte/2017-02-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
-| `matpower/case1888rte/2017-08-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
 | `matpower/case1951rte/2017-02-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
-| `matpower/case1951rte/2017-08-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
 | `matpower/case2848rte/2017-02-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
-| `matpower/case2848rte/2017-08-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
 | `matpower/case2868rte/2017-02-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
-| `matpower/case2868rte/2017-08-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
 | `matpower/case6468rte/2017-02-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
-| `matpower/case6468rte/2017-08-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
 | `matpower/case6470rte/2017-02-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
-| `matpower/case6470rte/2017-08-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
 | `matpower/case6495rte/2017-02-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
-| `matpower/case6495rte/2017-08-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
 | `matpower/case6515rte/2017-02-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
-| `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 +124,7 @@ 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 +156,7 @@ 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).

@@ -190,9 +175,11 @@ 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](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 [Antonio Frangioni's random UC generator](http://groups.di.unipi.it/optimize/Data/UC.html). These test cases have been used in [FrGe06] and many other publications.
+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.

 | Name | Hours | Buses | Generators | Lines | Contingencies | References |
 |------|-------|-------|------------|-------|---------------|------------|
@@ -239,9 +226,11 @@ 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

- Test cases used in [TeLuSa19]. These instances are similar to OR-LIB/UC, in the sense that they have also been generated using [Antonio Frangioni's random instance generator](http://groups.di.unipi.it/optimize/Data/UC.html), but are much larger.
+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.

 | Name | Hours | Buses | Generators | Lines | Contingencies | References |
 |------|-------|-------|------------|-------|---------------|------------|
@@ -295,9 +284,11 @@ 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)

--- a/docs/model.md
+++ b/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)
+
--- a/src/docs/usage.md
+++ b/src/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:
@@ -18,50 +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, 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.

@@ -78,14 +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
+```{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).

--- a/instances/README.md
+++ b/instances/README.md
@@ -1,53 +0,0 @@
-Instances
-=========
-
-UnitCommitment.jl provides a large collection of benchmark instances collected
-from the literature and converted to a common data format. If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources, as listed below. [See documentation for more details](https://anl-ceeesa.github.io/UnitCommitment.jl/).
-
-References
----------
-
-### UnitCommitment.jl
-
-* [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)
-
-
-### MATPOWER
-
-* [MTPWR] **D. Zimmerman, C. E. Murillo-Sandnchez and R. J. Thomas.** "Matpower:  Steady-state  operations,  planning,  and  analysis  tools  forpower systems research and education", IEEE Transactions on PowerSystems, vol. 26, no. 1, pp. 12 –19, Feb. 2011. [DOI: 10.1109/TPWRS.2010.2051168](https://doi.org/10.1109/TPWRS.2010.2051168)
-
-* [PSTCA] **University of Washington, Dept. of Electrical Engineering.** "Power Systems Test Case Archive". Available at: <http://www.ee.washington.edu/research/pstca/> (Accessed: Nov 14, 2020)
-
-* [JoFlMa16] **C. Josz, S. Fliscounakis, J. Maeght, and P. Panciatici.** "AC Power Flow
-Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE". [ArXiv (2016)](https://arxiv.org/abs/1603.01533).
-
-* [FlPaCa13] **S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel.**
-"Contingency ranking with respect to overloads in very large power
-systems taking into account uncertainty, preventive and corrective
-actions", Power Systems, IEEE Trans. on, (28)4:4909-4917, 2013.
-[DOI: 10.1109/TPWRS.2013.2251015](https://doi.org/10.1109/TPWRS.2013.2251015)
-
-
-### PGLIB-UC
-
-* [PGLIB] **Carleton Coffrin and Bernard Knueven.** "Power Grid Lib - Unit Commitment". Available at: <https://github.com/power-grid-lib/pglib-uc> (Accessed: Nov 14, 2020)
-
-* [KrHiOn12] **Eric Krall, Michael Higgins and Richard P. O’Neill.** "RTO unit commitment test system." Federal Energy Regulatory Commission. Available at: <https://www.ferc.gov/industries-data/electric/power-sales-and-markets/increasing-efficiency-through-improved-software-1> (Accessed: Nov 14, 2020)
-
-* [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)
-
-### RTS-GMLC
-
-* https://github.com/GridMod/RTS-GMLC
-
-* [BaBlEh19] **Clayton Barrows, Aaron Bloom, Ali Ehlen, Jussi Ikaheimo, Jennie Jorgenson, Dheepak Krishnamurthy, Jessica Lau et al.** "The IEEE Reliability Test System: A Proposed 2019 Update." IEEE Transactions on Power Systems (2019). [DOI: 10.1109/TPWRS.2019.2925557](https://doi.org/10.1109/TPWRS.2019.2925557)
-
-### OR-LIB
-
-* [ORLIB] **J.E.Beasley.** "OR-Library: distributing test problems by electronic mail", Journal of the Operational Research Society 41(11) (1990). [DOI: 10.2307/2582903](https://doi.org/10.2307/2582903)
-
-* [FrGe06] **A. Frangioni, C. Gentile.** "Solving nonlinear single-unit commitment problems with ramping constraints" Operations Research 54(4), p. 767 - 775, 2006. [DOI: 10.1287/opre.1060.0309](https://doi.org/10.1287/opre.1060.0309)
-
-### Tejada19
-
-* [TeLuSa19] **D. A. Tejada-Arango, S. Lumbreras, P. Sanchez-Martin and A. Ramos.** "Which Unit-Commitment Formulation is Best? A Systematic Comparison," in IEEE Transactions on Power Systems. [DOI: 10.1109/TPWRS.2019.2962024](https://ieeexplore.ieee.org/document/8941313/).
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--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Alinson S. Xavier	5c3c8f0d63	GitHub Actions: Remove older non-LTS Julia versions	2022-04-16 11:53:12 -05:00
Alinson S. Xavier	cce6a874b9	Bump JuMP version to 1.0	2022-04-16 11:52:21 -05:00
Alinson S. Xavier	1ce1cddaf3	Remove Gurobi from test dependencies; remove large tests	2022-04-16 11:43:09 -05:00
Alinson S. Xavier	46d754dbcf	GitHub Actions: Add Julia 1.7	2022-04-16 11:34:25 -05:00
Alinson S. Xavier	b7d9083335	Makefile: Update clean target	2022-04-16 11:34:14 -05:00
Alinson S. Xavier	86ae1d0429	juliaw: Make it compatible with Julia 1.7	2022-04-16 11:33:57 -05:00
Alinson S. Xavier	58a7567c16	Randomization: Explicitly use MersenneTwister; allow other RNGs	2022-04-16 11:14:06 -05:00
Alinson S. Xavier	2367e5a348	Fix formatting	2022-04-16 10:27:46 -05:00
Alinson S. Xavier	74b8a8ae2c	Fix formatting	2022-04-16 10:23:58 -05:00
Alinson S. Xavier	3260fa29ad	Remove temporary files	2022-04-16 10:16:53 -05:00
Alinson S. Xavier	3b1d2d1845	Add author: Ogün Yurdakul	2022-04-16 10:15:32 -05:00
Alinson S. Xavier	db106f1a38	Make juliaw executable	2022-04-16 10:12:09 -05:00
Alinson S. Xavier	16b0fec6cd	Make tests completely silent; remove set_gap warnings on Cbc	2022-04-16 10:11:33 -05:00
Alinson S. Xavier	cda1e368fe	Remove some redundant comments	2022-04-16 09:55:28 -05:00
Alinson S. Xavier	099fb4e3cb	Add case14-flex test case	2022-04-16 09:52:08 -05:00
oyurdakul	b4bc50c865	new formatting	2022-04-01 15:22:42 +02:00
oyurdakul	febb4f1aad	new formatting	2022-04-01 15:17:14 +02:00
oyurdakul	8988b00b07	modified validation, error scripts	2022-03-23 02:39:24 +01:00
oyurdakul	0046c4ca2a	change the validation of reserves	2022-03-22 19:01:20 +01:00
Alinson S Xavier	72f659b9ff	Merge branch 'dev' into add-flexiramp	2022-03-01 16:32:52 -06:00
Alinson S Xavier	861284875b	Reformat source code	2022-03-01 16:32:33 -06:00
Alinson S Xavier	360308ef4a	Reformat source code	2022-03-01 16:26:51 -06:00
Alinson S Xavier	03268dd3df	Merge branch 'dev' into add-flexiramp	2022-03-01 16:26:42 -06:00
Alinson S Xavier	ec0f9dcfcd	Temporarily revert changes to instances.md; download v0.2 instances	2022-03-01 16:24:47 -06:00
oyurdakul	a3a71ff5a9	add flexiramp	2022-02-03 09:45:06 +01:00
Alinson S Xavier	5beff627d3	Cite sources in read_benchmark; update docs	2022-01-24 10:42:55 -06:00
Alinson S Xavier	5e2cdb9e0c	Update docs	2022-01-20 16:20:02 -06:00
Alinson S Xavier	e41f4d11c2	Remove instances from repository; download on the fly	2022-01-20 16:17:48 -06:00
Alinson S Xavier	fbc4b004cd	benchmarks: use provided gap and time limit	2021-08-31 10:25:58 -05:00
Alinson S Xavier	93d3e5987d	Replace sysimage.jl by juliaw; add deps/formatter	2021-08-31 09:51:36 -05:00
Alinson S Xavier	f235333551	Improve benchmark scripts	2021-08-31 08:03:21 -05:00
Alinson S Xavier	6c566e0e79	Improve sysimage.jl	2021-08-20 04:51:09 -05:00
Alinson S Xavier	5c3f7b15d3	UnitCommitmentInstance: add _by_name fields	2021-08-19 07:07:25 -05:00
Alinson S Xavier	7c907a6eb5	Implement randomization method from XavQiuAhm2021	2021-08-05 17:04:37 -05:00
Alinson S. Xavier	b1498c50b3	GitHub Actions: Test fewer combinations	2021-07-26 07:57:17 -05:00
Aleksandr Kazachkov	000215e991	Add reserve shortfall penalty	2021-07-26 07:54:45 -05:00
Alinson S Xavier	7a1b6f0f55	Update CHANGELOG.md	2021-07-21 11:18:22 -05:00
Alinson S Xavier	719143ea40	Flip coefficients in eq_net_injection; add example to the docs	2021-07-21 11:04:11 -05:00
Alinson S Xavier	07d7e04728	Fix bug in validation script; create large tests	2021-07-21 09:49:20 -05:00
Alinson S. Xavier	4daf38906d	Merge pull request #12 from mtanneau/mt/FixDuplicateStartup Fix duplicated startup constraint	2021-07-19 17:14:39 -05:00
mtanneau	b2eaa0e48b	Fix duplicated startup constraint	2021-07-17 15:57:03 -04:00
Alinson S Xavier	821d48bdc6	Implement instance randomization	2021-06-17 10:17:50 -05:00
Alinson S. Xavier	cee86168ce	Update README.md	2021-06-03 16:25:10 -05:00
Alinson S Xavier	a7f9e84c31	Add Gar1962.ProdVars	2021-06-03 08:13:05 -05:00
Alinson S Xavier	063b602d1a	Create file for status vars; add Gar1962.StatusVars	2021-06-02 20:56:31 -05:00
Alinson S Xavier	2f90c48d60	table.py: Print validation errors	2021-06-02 11:38:07 -05:00
Alinson S Xavier	98ae4d3ad4	Update docs	2021-06-02 09:36:32 -05:00
Alinson S Xavier	30c21b0a06	Update version to 0.2.1	2021-06-02 09:21:09 -05:00
Alinson S Xavier	f642c4dbe9	Update docs	2021-06-02 09:16:41 -05:00
Alinson S. Xavier	a59bc2c25e	Update README.md	2021-06-02 08:46:41 -05:00
Alinson S Xavier	cdb58a8113	Update docs	2021-06-02 08:42:04 -05:00
Alinson S Xavier	34dd6bd86f	Docs: Add DOIs	2021-06-02 08:35:26 -05:00
Alinson S. Xavier	ca592be056	Update README.md	2021-06-02 08:16:47 -05:00
Alinson S Xavier	107337f621	Remove _build_model; update docs	2021-06-02 08:15:03 -05:00
Alinson S Xavier	0c1b508e85	Minor changes to benchmark plots	2021-06-02 08:12:41 -05:00
Alinson S Xavier	c5728cb575	Switch to KnuOstWat2018.PwlCosts by default	2021-06-02 08:12:14 -05:00
Alinson S Xavier	98e483bb3d	Update CHANGELOG.md	2021-06-01 14:38:57 -05:00
Alinson S Xavier	0a96565f47	Reformat code	2021-06-01 14:34:07 -05:00
Alinson S Xavier	8cdd88d6de	Make papers into modules, instead of structs; add StartupCostsFormulation	2021-06-01 14:21:50 -05:00
Alinson S Xavier	ecb13dba7c	Use 4-digit years	2021-06-01 13:08:07 -05:00
Alinson S Xavier	fc8995eff1	Add KnuOstWat18	2021-06-01 12:48:34 -05:00
Alinson S Xavier	f69d378d47	Add CarArr06	2021-06-01 11:42:08 -05:00
Alinson S Xavier	a3d0f2c65c	Split Gar62 into separate formulation; add PiecewiseLinearCostsFormulation	2021-06-01 11:29:08 -05:00
Alinson S Xavier	2a9881ddfc	Split _add_production_eqs; remove unused arguments	2021-06-01 11:13:41 -05:00
Alinson S Xavier	df3d21ad96	Fix formatting	2021-06-01 09:58:26 -05:00
Alinson S Xavier	8fdee6a968	Fix missing import	2021-06-01 09:55:54 -05:00
Alinson S Xavier	05441b7492	Add ramping formulaton: PanGua16	2021-06-01 09:40:12 -05:00
Alinson S Xavier	b4cb4d8252	Add basic formulation tests	2021-06-01 09:03:35 -05:00
Alinson S Xavier	38259428e4	Reorganize test folder	2021-06-01 08:21:47 -05:00
Alinson S Xavier	572fce48f1	Merge branch 'dev' into feature/reorganize	2021-06-01 07:10:55 -05:00
Alinson S Xavier	180de30246	Merge branch 'dev' of github.com:ANL-CEEESA/UnitCommitment.jl into dev	2021-06-01 07:09:04 -05:00
Alinson S Xavier	92bfc01e8f	Small fixes to ArrCon00	2021-06-01 07:07:56 -05:00
Alinson S Xavier	67cef8b5cd	Rename formulation structs	2021-05-30 21:45:54 -05:00
Alinson S Xavier	7db8d723f7	Update benchmark scripts	2021-05-30 21:45:49 -05:00
Alinson S Xavier	f01562e37f	Update docs	2021-05-30 07:58:53 -05:00
Alinson S Xavier	7a01dd436f	Add MorLatRam13 ramping	2021-05-30 07:52:07 -05:00
Alinson S Xavier	1fdbce2ffa	Add Alex to authors	2021-05-30 07:18:27 -05:00
Alinson S Xavier	bf6d19343e	Set up multi-formulation architecture; start merging akazachk's code	2021-05-30 07:14:28 -05:00
Alinson S Xavier	483c793d49	Break down model.jl	2021-05-29 18:33:16 -05:00
Alinson S Xavier	4e8426beba	Reorganize files; document some methods	2021-05-29 07:43:53 -05:00
Alinson S. Xavier	1440b5fc82	Update README.md	2021-05-28 11:15:27 -05:00
Alinson S. Xavier	db27b6de72	Update README.md	2021-05-28 11:14:57 -05:00
Alinson S. Xavier	4f0f57c29e	Update CHANGELOG.md	2021-05-28 11:05:31 -05:00
Alinson S Xavier	e594a68492	Update CHANGELOG.md	2021-05-28 10:56:45 -05:00
Alinson S Xavier	b16c0f0133	Remove benchmark/Manifest.toml	2021-05-28 10:48:54 -05:00
Alinson S Xavier	4188c42d3d	Remove benchmark/Manifest.toml	2021-05-27 22:27:19 -05:00
Alinson S Xavier	a684419f33	Reformat Python scripts	2021-05-27 22:26:38 -05:00
Alinson S Xavier	3687d42733	Fix validation when no price-sensitive loads are included	2021-05-27 22:14:49 -05:00
Alinson S Xavier	bd0d377c95	Update Makefile	2021-05-27 21:42:54 -05:00
Alinson S Xavier	9224cd2efb	Format source code with JuliaFormatter; set up GH Actions	2021-05-27 21:37:38 -05:00
Alinson S Xavier	fb9221b8fb	Properly validate solutions with price-sensitive loads	2021-05-27 21:14:37 -05:00
Alinson S Xavier	7eb1019410	Rename internal methods to _something; reformat code	2021-05-27 20:45:15 -05:00
Alinson S Xavier	11514b5de8	Rename fix!(instance) to repair!	2021-05-27 18:05:42 -05:00
Alinson S Xavier	3bd8428322	Make logs more colorful	2021-05-27 18:01:32 -05:00
Alinson S Xavier	99975db5cd	Implement UnitCommitment.write	2021-05-27 18:01:05 -05:00
Alinson S Xavier	e2660f50f2	Update docs	2021-05-27 17:47:26 -05:00
Alinson S Xavier	d20c41704d	Update docs	2021-05-27 17:20:00 -05:00
Alinson S Xavier	24871a7f8a	Update docs	2021-05-27 17:04:03 -05:00
Alinson S Xavier	6adf12535e	Add formulation section	2021-05-27 13:59:15 -05:00
Alinson S Xavier	117c8932e9	GitHub Actions: Fix tests; remove unused workflows	2021-05-27 12:09:42 -05:00
Alinson S. Xavier	844c9377d8	Update test.yml	2021-05-27 11:47:48 -05:00
Alinson S. Xavier	14a42188dd	test.yml: Drop Julia 1.3	2021-05-27 11:46:51 -05:00
Alinson S. Xavier	e9144ef9b2	Update test.yml	2021-05-27 11:44:17 -05:00
Alinson S Xavier	607bbeb75c	Make build_model return a plain JuMP model	2021-05-27 11:30:49 -05:00
Alinson S Xavier	5c81be4660	Migrate docs from mkdocs to sphinx	2021-05-27 11:11:02 -05:00
Alinson S Xavier	3da6f7e08b	Makefile: Bump version	2021-05-27 11:11:02 -05:00
Alinson S. Xavier	c38c5be05d	Merge pull request #10 from mtanneau/ArrayType Fix Array type instability	2021-04-11 10:57:43 -05:00
mtanneau	a37e7cd9b1	Fix Array type instability	2021-04-10 11:24:59 -04:00
Alinson S Xavier	5f74992cf6	Update CHANGELOG; bump version number	2021-03-09 11:07:59 -06:00
Alinson S Xavier	4947bff460	Implement sub-hourly commitment	2021-03-09 11:07:59 -06:00
Alinson S Xavier	5f0400fd93	Update dependencies	2021-03-09 11:07:59 -06:00
Alinson S Xavier	274fd6dfa1	Docs: Add "Time step (min)", rename "Time (h)" to "Time horizon (h)"	2021-03-09 11:07:59 -06:00
Alinson S. Xavier	1cc4e312fb	Update README.md	2020-12-30 09:17:31 -06:00
Alinson S. Xavier	0282b27ed3	Create config.yml	2020-12-30 08:31:37 -06:00
Alinson S. Xavier	612fdf0f80	Update issue templates	2020-12-30 08:27:57 -06:00
Alinson S. Xavier	9b8bf9e9b2	Update README.md	2020-12-05 15:34:55 -06:00
Feng	98a19747ce	Update README.md updated acknowledgements	2020-11-28 09:13:01 -06:00
Alinson S Xavier	cd41d782ed	Makefile: Remove obsolete targets	2020-11-18 14:19:34 -06:00
Alinson S Xavier	4146aa9f95	Small fixes to Makefile [benchmark]	2020-11-18 14:10:07 -06:00
mtanneau	9957894bdc	Precompile statements for sysimage	2020-11-18 14:04:08 -06:00
Alinson S. Xavier	853e0c7b70	Merge pull request #5 from mtanneau/mt-test Fix make test pipeline	2020-11-18 13:54:18 -06:00
mtanneau	c637784cb9	Fix make test pipeline	2020-11-18 14:20:35 -05:00
Alinson S Xavier	332ace7613	Instance.md: Small corrections	2020-11-16 11:25:24 -06:00
Álinson S. Xavier	c1cf0b900c	README.md: Filter benchmark runs	2020-11-16 07:55:11 -06:00
Alinson S Xavier	dac1e49735	Only run tests when .jl/.toml files are changed	2020-11-16 07:42:09 -06:00
Alinson S Xavier	eaacdcc3fa	Small corrections to usage.md	2020-11-16 07:42:09 -06:00
Álinson S. Xavier	32ea06fa39	README.md: Use fixed DOI	2020-11-16 07:13:38 -06:00
Alinson S Xavier	7bfe671eee	Format.md: Small fixes	2020-11-16 07:02:43 -06:00