Update CHANGELOG.md

Remove benchmark/Manifest.toml
2025-12-06 08:18:51 -06:00 · 2021-05-28 11:05:31 -05:00 · 2021-05-28 10:56:45 -05:00 · 2021-05-28 10:48:54 -05:00 · 2021-05-27 22:27:19 -05:00 · 2021-05-27 22:26:38 -05:00
51 changed files with 2147 additions and 2238 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
@@ -1,19 +1,15 @@
 name: Tests
 on:
  push:
-    paths:
-      - '**.jl'
-      - '**.toml'
  pull_request:
-    paths:
-      - '**.jl'
-      - '**.toml'
+  schedule:
+    - cron: '45 10 * * *'
 jobs:
  test:
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
-        julia-version: ['1.3', '1.4', '1']
+        julia-version: ['1.3', '1.4', '1.5', '1.6']
        julia-arch: [x64, x86]
        os: [ubuntu-latest, windows-latest, macOS-latest]
        exclude:
--- a/.gitignore
+++ b/.gitignore
@@ -14,3 +14,6 @@ instances/_source
 local
 notebooks
 TODO.md
+docs/_build
+.vscode
+Manifest.toml
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,11 +1,49 @@
-# 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
+## [0.2.0] - 2021-05-28
+### Added
+- Add sub-hourly unit commitment.
+- Add `UnitCommitment.write(filename, solution)`.
+- Add 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/19
+++ b/19
@@ -3,8 +3,7 @@
 # Released under the modified BSD license. See COPYING.md for more details.

 JULIA := julia --color=yes --project=@.
-MKDOCS := ~/.local/bin/mkdocs
-VERSION := 0.1
+VERSION := 0.2

 build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
 	mkdir -p build
@@ -16,14 +15,18 @@ clean:
 	rm -rf build/*

 docs:
-	$(MKDOCS) build -d ../docs/$(VERSION)/
-	rm ../docs/$(VERSION)/*.ipynb
-	
-install-deps-docs:
-	pip install --user mkdocs mkdocs-cinder python-markdown-math
+	cd docs; make clean; make dirhtml
+	rsync -avP --delete-after docs/_build/dirhtml/ ../docs/$(VERSION)/
 	
 test: build/sysimage.so
 	@echo Running tests...
 	$(JULIA) --sysimage build/sysimage.so -e 'using Pkg; Pkg.test("UnitCommitment")' | tee build/test.log

-.PHONY: docs test
+
+format:
+	julia -e 'using JuliaFormatter; format("src"); format("test"); format("benchmark")'
+
+install-deps:
+	julia -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter", version="0.14.4"))'
+
+.PHONY: docs test format install-deps
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,367 +0,0 @@
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-
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-version = "1.3.0"
-
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-
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--- a/Project.toml
+++ b/Project.toml
@@ -2,7 +2,7 @@ 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 = "0.2.0"

 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
--- a/README.md
+++ b/README.md
@@ -1,9 +1,18 @@
-<a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/actions?query=workflow%3ATest+branch%3Adev"><img src="https://github.com/iSoron/UnitCommitment.jl/workflows/Tests/badge.svg"></img></a>
-<a href="https://github.com/ANL-CEEESA/UnitCommitment.jl/actions?query=workflow%3ABenchmark+branch%3Adev+is%3Asuccess"><img src="https://github.com/iSoron/UnitCommitment.jl/workflows/Benchmark/badge.svg"></img></a>
-<a href="https://doi.org/10.5281/zenodo.4269874"><img src="https://zenodo.org/badge/doi/10.5281/zenodo.4269874.svg" alt="DOI"></a>
-
-
-# UnitCommitment.jl
+<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 JuMP implementations of state-of-the-art mixed-integer programming formulations.

@@ -28,7 +37,7 @@

 * We would like to thank **Aleksandr M. Kazachkov** (University of Florida), **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.

-* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
+* 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, and the U.S. Department of Energy **Advanced Grid Modeling Program** under Grant DE-OE0000875

 ### Citing

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-[[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 = "98aa9c653e1dc3473bb5050caf8501293db9eee1"
-uuid = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
-version = "1.2.1"
-
-[[Parsers]]
-deps = ["Dates", "Test"]
-git-tree-sha1 = "10134f2ee0b1978ae7752c41306e131a684e1f06"
-uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "1.0.7"
-
-[[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"
-
-[[Requires]]
-deps = ["UUIDs"]
-git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
-uuid = "ae029012-a4dd-5104-9daa-d747884805df"
-version = "1.0.1"
-
-[[Revise]]
-deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Pkg", "REPL", "UUIDs", "Unicode"]
-git-tree-sha1 = "0992d4643e27b2deb9f2e4ec7a56b7033813a027"
-uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
-version = "2.7.3"
-
-[[SHA]]
-uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
-
-[[Serialization]]
-uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
-
-[[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 = "016d1e1a00fabc556473b07161da3d39726ded35"
-uuid = "90137ffa-7385-5640-81b9-e52037218182"
-version = "0.12.4"
-
-[[Statistics]]
-deps = ["LinearAlgebra", "SparseArrays"]
-uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-
-[[Test]]
-deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
-uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[[TimerOutputs]]
-deps = ["Printf"]
-git-tree-sha1 = "f458ca23ff80e46a630922c555d838303e4b9603"
-uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
-version = "0.5.6"
-
-[[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"
-
-[[UnitCommitment]]
-deps = ["Cbc", "DataStructures", "Documenter", "GLPK", "GZip", "JSON", "JuMP", "LinearAlgebra", "Logging", "MathOptFormat", "MathOptInterface", "PackageCompiler", "Printf", "Requires", "Revise", "SparseArrays", "Test", "TimerOutputs"]
-path = ".."
-uuid = "64606440-39ea-11e9-0f29-3303a1d3d877"
-version = "2.1.0"
-
-[[ZipFile]]
-deps = ["Libdl", "Printf", "Zlib_jll"]
-git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.9.2"
-
-[[Zlib_jll]]
-deps = ["Libdl", "Pkg"]
-git-tree-sha1 = "622d8b6dc0c7e8029f17127703de9819134d1b71"
-uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.11+14"
--- a/benchmark/run.jl
+++ b/benchmark/run.jl
@@ -10,6 +10,8 @@ using Logging
 using Printf
 using LinearAlgebra

+UnitCommitment._setup_logger()
+
 function main()
    basename, suffix = split(ARGS[1], ".")
    solution_filename = "results/$basename.$suffix.sol.json"
@@ -18,7 +20,6 @@ function main()
    time_limit = 60 * 20

    BLAS.set_num_threads(4)
-    global_logger(TimeLogger(initial_time = time()))

    total_time = @elapsed begin
        @info "Reading: $basename"
@@ -28,34 +29,38 @@ function main()
        @info @sprintf("Read problem in %.2f seconds", time_read)

        time_model = @elapsed begin
-            model = build_model(instance=instance,
-                                optimizer=optimizer_with_attributes(Gurobi.Optimizer,
+            model = build_model(
+                instance = instance,
+                optimizer = optimizer_with_attributes(
+                    Gurobi.Optimizer,
                    "Threads" => 4,
                    "Seed" => rand(1:1000),
-                                                                    ))
+                ),
+                variable_names = true,
+            )
        end

        @info "Optimizing..."
        BLAS.set_num_threads(1)
-        UnitCommitment.optimize!(model, time_limit=time_limit, gap_limit=1e-3)
-
+        UnitCommitment.optimize!(
+            model,
+            time_limit = time_limit,
+            gap_limit = 1e-3,
+        )
    end
    @info @sprintf("Total time was %.2f seconds", total_time)

    @info "Writing: $solution_filename"
-    solution = UnitCommitment.get_solution(model)
+    solution = UnitCommitment.solution(model)
    open(solution_filename, "w") do file
-        JSON.print(file, solution, 2)
+        return JSON.print(file, solution, 2)
    end

    @info "Verifying solution..."
    UnitCommitment.validate(instance, solution)

-    @info "Setting variable names..."
-    UnitCommitment.set_variable_names!(model)
-
    @info "Exporting model..."
-    JuMP.write_to_file(model.mip, model_filename)
+    return JuMP.write_to_file(model, model_filename)
 end

 main()
--- a/benchmark/scripts/compare.py
+++ b/benchmark/scripts/compare.py
@@ -8,41 +8,49 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 import sys

-#easy_cutoff = 120
+# easy_cutoff = 120

 b1 = pd.read_csv(sys.argv[1], index_col=0)
 b2 = pd.read_csv(sys.argv[2], index_col=0)

-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 = 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"]

 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["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 = 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)
+# 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
+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,
+sns.barplot(
+    data=merged,
    x="Speedup",
    y="Name",
    color="tab:red",
    capsize=0.15,
    errcolor="k",
-            errwidth=1.25)
+    errwidth=1.25,
+)
 plt.axvline(1.0, linestyle="--", color="k")
 plt.tight_layout()

@@ -50,7 +58,10 @@ print("Writing tables/compare.png")
 plt.savefig("tables/compare.png", dpi=150)

 print("Writing tables/compare.csv")
-merged.loc[:, ["Group",
+merged.loc[
+    :,
+    [
+        "Group",
        "Instance",
        "Sample",
        "A Time (s)",
@@ -60,5 +71,5 @@ merged.loc[:, ["Group",
        "A Value",
        "B Value",
        "Value diff (%)",
-              ]
-          ].to_csv("tables/compare.csv", index_label="Index")
+    ],
+].to_csv("tables/compare.csv", index_label="Index")
--- a/benchmark/scripts/table.py
+++ b/benchmark/scripts/table.py
@@ -9,8 +9,8 @@ from tabulate import tabulate


 def process_all_log_files():
-    pathlist = list(Path(".").glob('results/*/*/*.log'))
-    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):
@@ -52,37 +52,55 @@ def process(filename):

    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 +121,10 @@ 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))
@@ -150,6 +171,7 @@ def process(filename):
        "Transmission screening calls": transmission_calls,
    }

+
 def generate_chart():
    import pandas as pd
    import matplotlib.pyplot as plt
@@ -159,7 +181,9 @@ def generate_chart():
    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[:, "Instance"] = (
+            table.loc[:, "Group"] + "/" + table.loc[:, "Instance"]
+        )
        table.loc[:, "Filename"] = f
        tables += [table]
    benchmark = pd.concat(tables, sort=True)
@@ -168,16 +192,18 @@ def generate_chart():
    plt.figure(figsize=(12, 0.50 * k))
    sns.set_style("whitegrid")
    sns.set_palette("Set1")
-    sns.barplot(y="Instance",
+    sns.barplot(
+        y="Instance",
        x="Total time (s)",
        color="tab:red",
        capsize=0.15,
        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/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
@@ -1,7 +1,18 @@
+```{sectnum}
+---
+start: 2
+depth: 2
+suffix: .
+---
+```
+
+
 Data Format
 ===========

-## 1. Input Data Format
+
+Input Data Format
+-----------------

 Instances are specified by JSON files containing the following main sections:

@@ -15,27 +26,28 @@ Instances are specified by JSON files containing the following main sections:

 Each section is described in detail below. For a complete example, see [case14](https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances/matpower/case14).

-### 1.1 Parameters
+### Parameters

-This section describes system-wide parameters, such as power balance penalties, and optimization parameters, such as the length of the planning horizon.
+This section describes system-wide parameters, such as power balance penalties,  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


 #### Example
 ```json
 {
    "Parameters": {
-        "Time (h)": 4,
+        "Time horizon (h)": 4,
        "Power balance penalty ($/MW)": 1000.0
    }
 }
 ```

-### 1.2 Buses
+### Buses

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

@@ -64,40 +76,40 @@ This section describes the characteristics of each bus in the system.
 ```


-### 1.3 Generators
+### Generators

 This section describes all generators in the system, including thermal units, renewable units and virtual units.

 | Key                       | Description                                      | Default | Time series?
 | :------------------------ | :------------------------------------------------| ------- | :-----------:
-| `Bus`                     | Identifier of the bus where this generator is located (string) | Required | N
+| `Bus`                     | Identifier of the bus where this generator is located (string). | Required | N
 | `Production cost curve (MW)` and `Production cost curve ($)` | Parameters describing the piecewise-linear production costs. See below for more details. | Required | Y
-| `Startup costs ($)` and `Startup delays (h)` | Parameters describing how much it costs to start the generator after it has been shut down for a certain amount of time. If `Startup costs ($)` and `Startup delays (h)` are set to `[300.0, 400.0]` and `[1, 4]`, for example, and the generator is shut down at time `t`, then it costs 300 to start up the generator at times `t+1`, `t+2` or `t+3`, and 400 to start the generator at time `t+4` or any time after that.  The number of startup cost points is unlimited, and may be different for each generator. Startup delays must be strictly increasing. | `[0.0]` and `[1]` | N
-| `Minimum uptime (h)`      | Minimum amount of time the generator must stay operational after starting up (in hours). For example, if the generator starts up at time 1 and `Minimum uptime (h)` is set to 4, then the generator can only shut down at time 5. | `1` | N
-| `Minimum downtime (h)`    | Minimum amount of time the generator must stay offline after shutting down (in hours). For example, if the generator shuts down at time 1 and `Minimum downtime (h)` is set to 4, then the generator can only start producing power again at time 5. | `1` | N
-| `Ramp up limit (MW)`      | Maximum increase in production from one time period to the next (in MW). For example, if the generator is producing 100 MW at time 1 and if this parameter is set to 40 MW, then the generator will produce at most 140 MW at time 2. | `+inf` | N
-| `Ramp down limit (MW)`    | Maximum decrease in production from one time period to the next (in MW). For example, if the generator is producing 100 MW at time 1 and this parameter is set to 40 MW, then the generator will produce at least 60 MW at time 2. | `+inf` | N
-| `Startup limit (MW)`   | Maximum amount of power a generator can produce immediately after starting up (in MW). | `+inf` | N
-| `Shutdown limit (MW)`     | Maximum amount of power a generator can produce immediately before shutting down (in MW). Specifically, the generator can only shut down at time `t+1` if its production at time `t` is below this limit.  | `+inf` | N
-| `Initial status (h)`  | If set to a positive number, indicates the amount of time the generator has been on at the beginning of the simulation, and if set to a negative number, the amount of time the generator has been off. For example, if `Initial status (h)` is `-2`, this means that the generator was off at simulation time `-2` and `-1`. The simulation starts at time `0`. | Required | N
-| `Initial power (MW)`  | Amount of power the generator at time period `-1`, immediately before the planning horizon starts. | Required | N
-| `Must run?`               | If `true`, the generator should be committed, even that is not economical (Boolean). | `false` | Y
+| `Startup costs ($)` and `Startup delays (h)` | Parameters describing how much it costs to start the generator after it has been shut down for a certain amount of time. If `Startup costs ($)` and `Startup delays (h)` are set to `[300.0, 400.0]` and `[1, 4]`, for example, and the generator is shut down at time `00:00` (h:min), then it costs \$300 to start up the generator at any time between `01:00` and `03:59`, and \$400 to start the generator at time `04:00` or any time after that.  The number of startup cost points is unlimited, and may be different for each generator. Startup delays must be strictly increasing and the first entry must equal `Minimum downtime (h)`. | `[0.0]` and `[1]` | N
+| `Minimum uptime (h)`      | Minimum amount of time the generator must stay operational after starting up (in hours). For example, if the generator starts up at time `00:00` (h:min) and `Minimum uptime (h)` is set to 4, then the generator can only shut down at time `04:00`. | `1` | N
+| `Minimum downtime (h)`    | Minimum amount of time the generator must stay offline after shutting down (in hours). For example, if the generator shuts down at time `00:00` (h:min) and `Minimum downtime (h)` is set to 4, then the generator can only start producing power again at time `04:00`. | `1` | N
+| `Ramp up limit (MW)`      | Maximum increase in production from one time step to the next (in MW). For example, if the generator is producing 100 MW at time step 1 and if this parameter is set to 40 MW, then the generator will produce at most 140 MW at time step 2. | `+inf` | N
+| `Ramp down limit (MW)`    | Maximum decrease in production from one time step to the next (in MW). For example, if the generator is producing 100 MW at time step 1 and this parameter is set to 40 MW, then the generator will produce at least 60 MW at time step 2. | `+inf` | N
+| `Startup limit (MW)`   | Maximum amount of power a generator can produce immediately after starting up (in MW). For example, if `Startup limit (MW)` is set to 100 MW and the unit is off at time step 1, then it may produce at most 100 MW at time step 2.| `+inf` | N
+| `Shutdown limit (MW)`     | Maximum amount of power a generator can produce immediately before shutting down (in MW). Specifically, the generator can only shut down at time step `t+1` if its production at time step `t` is below this limit.  | `+inf` | N
+| `Initial status (h)`  | If set to a positive number, indicates the amount of time (in hours) the generator has been on at the beginning of the simulation, and if set to a negative number, the amount of time the generator has been off. For example, if `Initial status (h)` is `-2`, this means that the generator was off since `-02:00` (h:min). The simulation starts at time `00:00`. If `Initial status (h)` is `3`, this means that the generator was on since `-03:00`. A value of zero is not acceptable. | Required | N
+| `Initial power (MW)`  | Amount of power the generator at time step `-1`, immediately before the planning horizon starts. | Required | N
+| `Must run?`               | If `true`, the generator should be committed, even if that is not economical (Boolean). | `false` | Y
 | `Provides spinning reserves?`    | If `true`, this generator may provide spinning reserves (Boolean). | `true` | Y

 #### Production costs and limits

-Production costs are represented as piecewise-linear curves. Figure 1 shows an example cost curve with three segments, where it costs 1400, 1600, 2200 and 2400 dollars to generate, respectively, 100, 110, 130 and 135 MW of power. To model this generator, `Production cost curve (MW)` should be set to `[100, 110, 130, 135]`, and `Production cost curve ($)`  should be set to `[1400, 1600, 2200, 2400]`.
+Production costs are represented as piecewise-linear curves. Figure 1 shows an example cost curve with three segments, where it costs \$1400, \$1600, \$2200 and \$2400 to generate, respectively, 100, 110, 130 and 135 MW of power. To model this generator, `Production cost curve (MW)` should be set to `[100, 110, 130, 135]`, and `Production cost curve ($)`  should be set to `[1400, 1600, 2200, 2400]`.
 Note that this curve also specifies the production limits. Specifically, the first point identifies the minimum power output when the unit is operational, while the last point identifies the maximum power output.

 <center>
-    <img src="../images/cost_curve.png" style="max-width: 500px"/>
+    <img src="../_static/cost_curve.png" style="max-width: 500px"/>
    <div><b>Figure 1.</b> Piecewise-linear production cost curve.</div>
    <br/>
 </center>

 #### Additional remarks:

-* For time-dependent production limits or time-dependent production costs, the usage of nested arrays is allowed. For example,  if `Production cost curve (MW)` is set to `[5.0, [10.0, 12.0, 15.0, 20.0]]`, then the unit may generate at most 10, 12, 15 and 20 MW of power during time periods 1, 2, 3 and 4, respectively. The minimum output for all time periods is fixed to at 5 MW.
+* For time-dependent production limits or time-dependent production costs, the usage of nested arrays is allowed. For example,  if `Production cost curve (MW)` is set to `[5.0, [10.0, 12.0, 15.0, 20.0]]`, then the unit may generate at most 10, 12, 15 and 20 MW of power during time steps 1, 2, 3 and 4, respectively. The minimum output for all time periods is fixed to at 5 MW.
 * There is no limit to the number of piecewise-linear segments, and different generators may have a different number of segments.
 * If `Production cost curve (MW)` and `Production cost curve ($)` both contain a single element, then the generator must produce exactly that amount of power when operational. To specify that the generator may produce any amount of power up to a certain limit `P`, the parameter `Production cost curve (MW)` should be set to `[0, P]`. 
 * Production cost curves must be convex.
@@ -133,7 +145,7 @@ 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 +169,7 @@ 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 +179,9 @@ This section describes the characteristics of transmission system, such as its t
 | `Target bus`           | Identifier of the bus where the transmission line reaches. | Required | N
 | `Reactance (ohms)`     | Reactance of the transmission line (in ohms). | Required | N
 | `Susceptance (S)`      | Susceptance  of the transmission line (in siemens). | Required | N
-| `Normal flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in its regular, fully-operational state. May be `null` is there is no limit. | `+inf` | Y
+| `Normal flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in its regular, fully-operational state. | `+inf` | Y
 | `Emergency flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in degraded state (for example, after the failure of another transmission line). | `+inf` | Y
-| `Flow limit penalty ($/MW)` | Penalty for violating the flow limits of the transmission line (in $/MW). This is charged per time period. For example, if there is a thermal violation of 1 MW for three time periods, three times this amount will be charged. | `5000.0` | Y
+| `Flow limit penalty ($/MW)` | Penalty for violating the flow limits of the transmission line (in $/MW). This is charged per time step. For example, if there is a thermal violation of 1 MW for three time steps, then three times this amount will be charged. | `5000.0` | Y

 #### Example

@@ -190,7 +202,7 @@ This section describes the characteristics of transmission system, such as its t
 ```


-### 1.6 Reserves
+### Reserves

 This section describes the hourly amount of operating reserves required.

@@ -214,7 +226,7 @@ This section describes the hourly amount of operating reserves required.
 }
 ```

-### 1.7 Contingencies
+### Contingencies

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

@@ -239,11 +251,11 @@ 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 +264,29 @@ Many numerical properties in the JSON file can be specified either as a single f
 }
 ```

-#### Current limitations
+The value `T` depends on both `Time horizon (h)` and `Time step (min)`, as the table below illustrates.

-* All reserves are system-wide (no zonal reserves)
-* 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.
+* 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 +1,72 @@
+# 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 and extended to make them more challenging and realistic.
+* **Model Implementation**: The package provides a Julia/JuMP implementation of state-of-the-art formulations and solution methods for SCUC. Our goal is to keep this implementation up-to-date, as new methods are proposed in the literature.
+* **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
+
+### Authors
+* **Alinson Santos Xavier** (Argonne National Laboratory)
+* **Feng Qiu** (Argonne National Laboratory)
+
+### Acknowledgments
+
+* We would like to thank **Aleksandr M. Kazachkov** (University of Florida), **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
+
+* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357
+
+* 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, 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
@@ -1,4 +1,13 @@
-# Instances
+```{sectnum}
+---
+start: 3
+depth: 2
+suffix: .
+---
+```
+
+Instances
+=========

 UnitCommitment.jl provides a large collection of benchmark instances collected
 from the literature and converted to a [common data format](format.md). In some cases, as indicated below, the original instances have been extended, with realistic parameters, using data-driven methods. 
@@ -7,7 +16,9 @@ If you use these instances in your research, we request that you cite UnitCommit
 Raw instances files are [available at our GitHub repository](https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances). Benchmark instances can also be loaded with
 `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md).

-## 1. MATPOWER
+
+MATPOWER
+--------

 [MATPOWER](https://github.com/MATPOWER/matpower) is an open-source package for solving power flow problems in MATLAB and Octave. It contains a number of power flow test cases, which have been widely used in the power systems literature.

@@ -25,7 +36,7 @@ Because most MATPOWER test cases were originally designed for power flow studies

 For each MATPOWER test case, UC.jl provides two variations (`2017-02-01` and `2017-08-01`) corresponding respectively to a winter and to a summer test case.

-### 1.1 MATPOWER/UW-PSTCA
+### MATPOWER/UW-PSTCA

 A variety of smaller IEEE test cases, [compiled by University of Washington](http://labs.ece.uw.edu/pstca/), corresponding mostly to small portions of the American Electric Power System in the 1960s.

@@ -43,7 +54,7 @@ A variety of smaller IEEE test cases, [compiled by University of Washington](htt
 | `matpower/case300/2017-08-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]


-### 1.2 MATPOWER/Polish
+### MATPOWER/Polish

 Test cases based on the Polish 400, 220 and 110 kV networks, originally provided by **Roman Korab** (Politechnika Śląska) and corrected by the MATPOWER team.

@@ -66,7 +77,7 @@ Test cases based on the Polish 400, 220 and 110 kV networks, originally provided
 | `matpower/case3375wp/2017-02-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
 | `matpower/case3375wp/2017-08-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]

-### 1.3 MATPOWER/PEGASE
+### MATPOWER/PEGASE

 Test cases from the [Pan European Grid Advanced Simulation and State Estimation (PEGASE) project](https://cordis.europa.eu/project/id/211407), describing part of the European high voltage transmission network.

@@ -83,7 +94,7 @@ Test cases from the [Pan European Grid Advanced Simulation and State Estimation
 | `matpower/case13659pegase/2017-02-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
 | `matpower/case13659pegase/2017-08-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]

-### 1.4 MATPOWER/RTE
+### MATPOWER/RTE

 Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://www.rte-france.com) representing the size and complexity of the French very high voltage transmission network.

@@ -107,11 +118,12 @@ Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://
 | `matpower/case6515rte/2017-08-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]


-## 2. PGLIB-UC Instances
+PGLIB-UC Instances
+------------------

 [PGLIB-UC](https://github.com/power-grid-lib/pglib-uc) is a benchmark library curated and maintained by the [IEEE PES Task Force on Benchmarks for Validation of Emerging Power System Algorithms](https://power-grid-lib.github.io/). These test cases have been used in [KnOsWa20].

-### 2.1 PGLIB-UC/California
+### PGLIB-UC/California

 Test cases based on publicly available data from the California ISO. For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).

@@ -139,7 +151,7 @@ Test cases based on publicly available data from the California ISO. For more de
 | `pglib-uc/ca/Scenario400_reserves_5` | 1 | 611 | 0 | 0 | [KnOsWa20]


-### 2.2 PGLIB-UC/FERC
+### PGLIB-UC/FERC

 Test cases based on a publicly available [unit commitment test case produced by the Federal Energy Regulatory Commission](https://www.ferc.gov/industries-data/electric/power-sales-and-markets/increasing-efficiency-through-improved-software-1). For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).

@@ -171,7 +183,7 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `pglib-uc/ferc/2015-12-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]


-### 2.3 PGLIB-UC/RTS-GMLC
+### PGLIB-UC/RTS-GMLC

 [RTS-GMLC](https://github.com/GridMod/RTS-GMLC) is an updated version of the RTS-96 test system produced by the United States Department of Energy's [Grid Modernization Laboratory Consortium](https://gmlc.doe.gov/). The PGLIB-UC/RTS-GMLC instances are modified versions of the original RTS-GMLC instances, with modified ramp-rates and without a transmission network. For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).

@@ -190,7 +202,9 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `pglib-uc/rts_gmlc/2020-11-25` | 1 | 154 | 0 | 0 | [BaBlEh19]
 | `pglib-uc/rts_gmlc/2020-12-23` | 1 | 154 | 0 | 0 | [BaBlEh19]

-## 3. OR-LIB/UC
+
+OR-LIB/UC
+---------

 [OR-LIB](http://people.brunel.ac.uk/~mastjjb/jeb/info.html) is a collection of test data sets for a variety of operations research problems, including unit commitment. The UC instances in OR-LIB are synthetic instances generated by a [random problem generator](http://groups.di.unipi.it/optimize/Data/UC.html) developed by the [Operations Research Group at University of Pisa](http://groups.di.unipi.it/optimize/). These test cases have been used in [FrGe06] and many other publications.

@@ -239,7 +253,9 @@ Test cases based on a publicly available [unit commitment test case produced by
 | `or-lib/200_0_8_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
 | `or-lib/200_0_9_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]

-## 4. Tejada19
+
+Tejada19
+--------

 Test cases used in [TeLuSa19]. These instances are similar to OR-LIB/UC, in the sense that they use the same random problem generator, but are much larger.

@@ -295,7 +311,9 @@ 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
+
+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)

--- a/docs/model.md
+++ b/docs/model.md
@@ -0,0 +1,196 @@
+```{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
+```
+
+### Modifying the model
+
+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)
+```
+
+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:
@@ -18,9 +28,10 @@ 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.

@@ -33,20 +44,22 @@ using UnitCommitment
 instance = UnitCommitment.read("/path/to/input.json")

 # Construct optimization model
-model = UnitCommitment.build_model(instance=instance,
-                                   optimizer=Cbc.Optimizer)
+model = UnitCommitment.build_model(
+    instance=instance,
+    optimizer=Cbc.Optimizer,
+)

 # 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
+# Extract solution
+solution = UnitCommitment.solution(model)
+
+# Write solution to a file
+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:

@@ -55,15 +68,15 @@ using UnitCommitment
 instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
 ```

-## 3. Advanced usage
+Advanced usage
+--------------


-### 3.1 Modifying the formulation
+### Modifying the formulation

 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.

-### 3.2 Generating initial conditions
-
+### 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.

@@ -84,10 +97,11 @@ model = UnitCommitment.build_model(instance, Cbc.Optimizer)
 UnitCommitment.optimize!(model)
 ```

-!!! warning
-    The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
+```{warning}
+The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
+```
    
-### 3.3 Verifying solutions
+### Verifying solutions

 When developing new formulations, it is very easy to introduce subtle errors in the model that result in incorrect solutions. To help with this, UC.jl includes a utility function that verifies if a given solution is feasible, and, if not, prints all the validation errors it found. The implementation of this function is completely independent from the implementation of the optimization model, and therefore can be used to validate it. The function can also be used to verify solutions produced by other optimization packages, as long as they follow the [UC.jl data format](format.md).

--- a/instances/test/case14-sub-hourly.json.gz
+++ b/instances/test/case14-sub-hourly.json.gz
--- a/instances/test/case14.json.gz
+++ b/instances/test/case14.json.gz
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,26 +0,0 @@
-site_name: UnitCommitment.jl
-theme:
-  name: cinder
-  hljs_languages:
-    - julia
-copyright: "Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved."
-repo_url: https://github.com/ANL-CEEESA/unitcommitment.jl
-edit_uri: edit/dev/src/docs/
-nav:
-    - Home: index.md
-    - Usage: usage.md
-    - Format: format.md
-    - Instances: instances.md
-plugins:
-  - search
-markdown_extensions:
-  - admonition  
-  - mdx_math  
-  - fenced_code
-extra_javascript: 
-    - https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML
-    - js/mathjax.js
-docs_dir: src/docs
-site_dir: docs
-extra_css:
-    - "css/custom.css"
--- a/src/UnitCommitment.jl
+++ b/src/UnitCommitment.jl
@@ -3,13 +3,12 @@
 # Released under the modified BSD license. See COPYING.md for more details.

 module UnitCommitment
-    include("log.jl")
-    include("dotdict.jl")
-    include("instance.jl")
-    include("screening.jl")
-    include("model.jl")
-    include("sensitivity.jl")
-    include("validate.jl")
-    include("convert.jl")
-    include("initcond.jl")
+include("log.jl")
+include("instance.jl")
+include("screening.jl")
+include("model.jl")
+include("sensitivity.jl")
+include("validate.jl")
+include("convert.jl")
+include("initcond.jl")
 end
--- a/src/convert.jl
+++ b/src/convert.jl
@@ -4,17 +4,17 @@

 using DataStructures, JSON, GZip

-function read_json(path::String)::OrderedDict
+function _read_json(path::String)::OrderedDict
    if endswith(path, ".gz")
        file = GZip.gzopen(path)
    else
        file = open(path)
    end
-    return JSON.parse(file, dicttype=()->DefaultOrderedDict(nothing))
+    return JSON.parse(file, dicttype = () -> DefaultOrderedDict(nothing))
 end

-function read_egret_solution(path::String)::OrderedDict
-    egret = read_json(path)
+function _read_egret_solution(path::String)::OrderedDict
+    egret = _read_json(path)
    T = length(egret["system"]["time_keys"])

    solution = OrderedDict()
--- a/src/docs/css/custom.css
+++ b/src/docs/css/custom.css
@@ -1,28 +0,0 @@
-.navbar-default {
-  border-bottom: 0px;
-  background-color: #fff;
-  box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
-}
-
-a, .navbar-default a {
-  color: #06a !important;
-  font-weight: normal;
-}
-
-.disabled > a {
-  color: #999 !important;
-}
-
-.navbar-default a:hover,
-.navbar-default .active,
-.active > a {
-  background-color: #f0f0f0 !important;
-}
-
-.icon-bar {
-  background-color: #666 !important;
-}
-
-.navbar-collapse {
-  border-color: #fff !important;
-}
--- a/src/docs/illustrations.ipynb
+++ b/src/docs/illustrations.ipynb
--- a/src/docs/index.md
+++ b/src/docs/index.md
@@ -1,42 +0,0 @@
-# UnitCommitment.jl
-
-**UnitCommitment.jl** (UC.jl) is a Julia optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and Julia/JuMP implementations of state-of-the-art mixed-integer programming formulations.
-
-### Package Components
-
-* **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
-* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature and extended to make them more challenging and realistic.
-* **Model Implementation**: The package provides a Julia/JuMP implementation of state-of-the-art formulations and solution methods for SCUC. Our goal is to keep this implementation up-to-date, as new methods are proposed in the literature.
-* **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
-
-### Documentation
-
-* [Usage](usage.md)
-* [Data Format](format.md)
-* [Instances](instances.md)
-
-### Source code
-
-* [https://github.com/ANL-CEEESA/unitcommitment.jl](https://github.com/ANL-CEEESA/unitcommitment.jl)
-
-### Authors
-* **Alinson Santos Xavier** (Argonne National Laboratory)
-* **Feng Qiu** (Argonne National Laboratory)
-
-### Acknowledgments
-
-* We would like to thank **Aleksandr M. Kazachkov** (University of Florida), **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
-
-* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
-
-### Citing
-
-If you use UnitCommitment.jl in your research, we request that you cite the package as follows:
-
-* Alinson S. Xavier, Feng Qiu, "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
-
-If you make use of the provided instances files, we request that you additionally cite the original sources, as described in the [instances page](instances.md).
-
-### License
-
-Released under the modified BSD license. See `LICENSE.md` for more details.
--- a/src/docs/js/mathjax.js
+++ b/src/docs/js/mathjax.js
@@ -1,8 +0,0 @@
-MathJax.Hub.Config({
-  "tex2jax": { inlineMath: [ [ '$', '$' ] ] }
-});
-MathJax.Hub.Config({
-  config: ["MMLorHTML.js"],
-  jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"],
-  extensions: ["MathMenu.js", "MathZoom.js"]
-});
--- a/src/dotdict.jl
+++ b/src/dotdict.jl
@@ -1,68 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-struct DotDict
-    inner::Dict
-end
-
-DotDict() = DotDict(Dict())
-
-function Base.setproperty!(d::DotDict, key::Symbol, value)
-    setindex!(getfield(d, :inner), value, key)
-end
-
-function Base.getproperty(d::DotDict, key::Symbol)
-    (key == :inner ? getfield(d, :inner) : d.inner[key])
-end
-
-function Base.getindex(d::DotDict, key::Int64)
-    d.inner[Symbol(key)]
-end
-
-function Base.getindex(d::DotDict, key::Symbol)
-    d.inner[key]
-end
-
-function Base.keys(d::DotDict)
-    keys(d.inner)
-end
-
-function Base.values(d::DotDict)
-    values(d.inner)
-end
-
-function Base.iterate(d::DotDict)
-    iterate(values(d.inner))
-end
-
-function Base.iterate(d::DotDict, v::Int64)
-    iterate(values(d.inner), v)
-end
-
-function Base.length(d::DotDict)
-    length(values(d.inner))
-end
-
-function Base.show(io::IO, d::DotDict)
-    print(io, "DotDict with $(length(keys(d.inner))) entries:\n")
-    count = 0
-    for k in keys(d.inner)
-        count += 1
-        if count > 10
-            print(io, "  ...\n")
-            break
-        end
-        print(io, "  :$(k) => $(d.inner[k])\n")
-    end
-end
-
-function recursive_to_dot_dict(el)
-    if typeof(el) == Dict{String, Any}
-        return DotDict(Dict(Symbol(k) => recursive_to_dot_dict(el[k]) for k in keys(el)))
-    else
-        return el
-    end
-end
-
-export recursive_to_dot_dict
--- a/src/initcond.jl
+++ b/src/initcond.jl
@@ -11,8 +11,10 @@ Generates feasible initial conditions for the given instance, by constructing
 and solving a single-period mixed-integer optimization problem, using the given
 optimizer. The instance is modified in-place.
 """
-function generate_initial_conditions!(instance::UnitCommitmentInstance,
-                                      optimizer)
+function generate_initial_conditions!(
+    instance::UnitCommitmentInstance,
+    optimizer,
+)::Nothing
    G = instance.units
    B = instance.buses
    t = 1
@@ -23,19 +25,17 @@ function generate_initial_conditions!(instance::UnitCommitmentInstance,
    @variable(mip, p[G] >= 0)

    # Constraint: Minimum power
-    @constraint(mip,
-                min_power[g in G],
-                p[g] >= g.min_power[t] * x[g])
+    @constraint(mip, min_power[g in G], p[g] >= g.min_power[t] * x[g])

    # Constraint: Maximum power
-    @constraint(mip,
-                max_power[g in G],
-                p[g] <= g.max_power[t] * x[g])
+    @constraint(mip, max_power[g in G], p[g] <= g.max_power[t] * x[g])

    # Constraint: Production equals demand
-    @constraint(mip,
+    @constraint(
+        mip,
        power_balance,
-                sum(b.load[t] for b in B) == sum(p[g] for g in G))
+        sum(b.load[t] for b in B) == sum(p[g] for g in G)
+    )

    # Constraint: Must run
    for g in G
@@ -58,9 +58,7 @@ function generate_initial_conditions!(instance::UnitCommitmentInstance,
            return c / mw
        end
    end
-    @objective(mip,
-               Min,
-               sum(p[g] * cost_slope(g) for g in G))
+    @objective(mip, Min, sum(p[g] * cost_slope(g) for g in G))

    JuMP.optimize!(mip)

@@ -73,4 +71,5 @@ function generate_initial_conditions!(instance::UnitCommitmentInstance,
            g.initial_status = -24
        end
    end
+    return
 end
--- a/src/instance.jl
+++ b/src/instance.jl
@@ -5,39 +5,35 @@
 using Printf
 using JSON
 using DataStructures
+using GZip
 import Base: getindex, time
-import GZip
-

 mutable struct Bus
    name::String
    offset::Int
-    load::Array{Float64}
-    units::Array
-    price_sensitive_loads::Array
+    load::Vector{Float64}
+    units::Vector
+    price_sensitive_loads::Vector
 end

-
 mutable struct CostSegment
-    mw::Array{Float64}
-    cost::Array{Float64}
+    mw::Vector{Float64}
+    cost::Vector{Float64}
 end

-
 mutable struct StartupCategory
    delay::Int
    cost::Float64
 end

-
 mutable struct Unit
    name::String
    bus::Bus
-    max_power::Array{Float64}
-    min_power::Array{Float64}
-    must_run::Array{Bool}
-    min_power_cost::Array{Float64}
-    cost_segments::Array{CostSegment}
+    max_power::Vector{Float64}
+    min_power::Vector{Float64}
+    must_run::Vector{Bool}
+    min_power_cost::Vector{Float64}
+    cost_segments::Vector{CostSegment}
    min_uptime::Int
    min_downtime::Int
    ramp_up_limit::Float64
@@ -46,11 +42,10 @@ mutable struct Unit
    shutdown_limit::Float64
    initial_status::Union{Int,Nothing}
    initial_power::Union{Float64,Nothing}
-    provides_spinning_reserves::Array{Bool}
-    startup_categories::Array{StartupCategory}
+    provides_spinning_reserves::Vector{Bool}
+    startup_categories::Vector{StartupCategory}
 end

-
 mutable struct TransmissionLine
    name::String
    offset::Int
@@ -58,107 +53,121 @@ mutable struct TransmissionLine
    target::Bus
    reactance::Float64
    susceptance::Float64
-    normal_flow_limit::Array{Float64}
-    emergency_flow_limit::Array{Float64}
-    flow_limit_penalty::Array{Float64}
+    normal_flow_limit::Vector{Float64}
+    emergency_flow_limit::Vector{Float64}
+    flow_limit_penalty::Vector{Float64}
 end

-
 mutable struct Reserves
-    spinning::Array{Float64}
+    spinning::Vector{Float64}
 end

-
 mutable struct Contingency
    name::String
-    lines::Array{TransmissionLine}
-    units::Array{Unit}
+    lines::Vector{TransmissionLine}
+    units::Vector{Unit}
 end

-
 mutable struct PriceSensitiveLoad
    name::String
    bus::Bus
-    demand::Array{Float64}
-    revenue::Array{Float64}
+    demand::Vector{Float64}
+    revenue::Vector{Float64}
 end

-
 mutable struct UnitCommitmentInstance
    time::Int
-    power_balance_penalty::Array{Float64}
-    units::Array{Unit}
-    buses::Array{Bus}
-    lines::Array{TransmissionLine}
+    power_balance_penalty::Vector{Float64}
+    units::Vector{Unit}
+    buses::Vector{Bus}
+    lines::Vector{TransmissionLine}
    reserves::Reserves
-    contingencies::Array{Contingency}
-    price_sensitive_loads::Array{PriceSensitiveLoad}
+    contingencies::Vector{Contingency}
+    price_sensitive_loads::Vector{PriceSensitiveLoad}
 end

-
 function Base.show(io::IO, instance::UnitCommitmentInstance)
-    print(io, "UnitCommitmentInstance with ")
+    print(io, "UnitCommitmentInstance(")
    print(io, "$(length(instance.units)) units, ")
    print(io, "$(length(instance.buses)) buses, ")
    print(io, "$(length(instance.lines)) lines, ")
    print(io, "$(length(instance.contingencies)) contingencies, ")
-    print(io, "$(length(instance.price_sensitive_loads)) price sensitive loads")
+    print(
+        io,
+        "$(length(instance.price_sensitive_loads)) price sensitive loads, ",
+    )
+    print(io, "$(instance.time) time steps")
+    print(io, ")")
+    return
 end

-
-function read_benchmark(name::AbstractString) :: UnitCommitmentInstance
+function read_benchmark(name::AbstractString)::UnitCommitmentInstance
    basedir = dirname(@__FILE__)
    return UnitCommitment.read("$basedir/../instances/$name.json.gz")
 end

-
 function read(path::AbstractString)::UnitCommitmentInstance
    if endswith(path, ".gz")
-        return read(GZip.gzopen(path))
+        return _read(gzopen(path))
    else
-        return read(open(path))
+        return _read(open(path))
    end
 end

-
-function read(file::IO)::UnitCommitmentInstance
-    return from_json(JSON.parse(file, dicttype=()->DefaultOrderedDict(nothing)))
+function _read(file::IO)::UnitCommitmentInstance
+    return _from_json(
+        JSON.parse(file, dicttype = () -> DefaultOrderedDict(nothing)),
+    )
 end

-function from_json(json; fix=true)
+function _from_json(json; repair = true)
    units = Unit[]
    buses = Bus[]
    contingencies = Contingency[]
    lines = TransmissionLine[]
    loads = PriceSensitiveLoad[]
-    T = json["Parameters"]["Time (h)"]

-    name_to_bus = Dict{String, Bus}()
-    name_to_line = Dict{String, TransmissionLine}()
-    name_to_unit = Dict{String, Unit}()
+    function scalar(x; default = nothing)
+        x !== nothing || return default
+        return x
+    end

-    function timeseries(x; default=nothing)
+    time_horizon = json["Parameters"]["Time (h)"]
+    if time_horizon === nothing
+        time_horizon = json["Parameters"]["Time horizon (h)"]
+    end
+    time_horizon !== nothing || error("Missing parameter: Time horizon (h)")
+    time_step = scalar(json["Parameters"]["Time step (min)"], default = 60)
+    (60 % time_step == 0) ||
+        error("Time step $time_step is not a divisor of 60")
+    time_multiplier = 60 ÷ time_step
+    T = time_horizon * time_multiplier
+
+    name_to_bus = Dict{String,Bus}()
+    name_to_line = Dict{String,TransmissionLine}()
+    name_to_unit = Dict{String,Unit}()
+
+    function timeseries(x; default = nothing)
        x !== nothing || return default
        x isa Array || return [x for t in 1:T]
        return x
    end

-    function scalar(x; default=nothing)
-        x !== nothing || return default
-        x
-    end
-    
    # Read parameters
-    power_balance_penalty = timeseries(json["Parameters"]["Power balance penalty (\$/MW)"],
-                                       default=[1000.0 for t in 1:T])
+    power_balance_penalty = timeseries(
+        json["Parameters"]["Power balance penalty (\$/MW)"],
+        default = [1000.0 for t in 1:T],
+    )

    # Read buses
    for (bus_name, dict) in json["Buses"]
-        bus = Bus(bus_name,
+        bus = Bus(
+            bus_name,
            length(buses),
            timeseries(dict["Load (MW)"]),
            Unit[],
-                  PriceSensitiveLoad[])
+            PriceSensitiveLoad[],
+        )
        name_to_bus[bus_name] = bus
        push!(buses, bus)
    end
@@ -169,8 +178,12 @@ function from_json(json; fix=true)

        # Read production cost curve
        K = length(dict["Production cost curve (MW)"])
-        curve_mw   = hcat([timeseries(dict["Production cost curve (MW)"][k]) for k in 1:K]...)
-        curve_cost = hcat([timeseries(dict["Production cost curve (\$)"][k]) for k in 1:K]...)
+        curve_mw = hcat(
+            [timeseries(dict["Production cost curve (MW)"][k]) for k in 1:K]...,
+        )
+        curve_cost = hcat(
+            [timeseries(dict["Production cost curve (\$)"][k]) for k in 1:K]...,
+        )
        min_power = curve_mw[:, 1]
        max_power = curve_mw[:, K]
        min_power_cost = curve_cost[:, 1]
@@ -178,50 +191,63 @@ function from_json(json; fix=true)
        for k in 2:K
            amount = curve_mw[:, k] - curve_mw[:, k-1]
            cost = (curve_cost[:, k] - curve_cost[:, k-1]) ./ amount
-            replace!(cost, NaN=>0.0)
+            replace!(cost, NaN => 0.0)
            push!(segments, CostSegment(amount, cost))
        end

        # Read startup costs
-        startup_delays = scalar(dict["Startup delays (h)"], default=[1])
-        startup_costs  = scalar(dict["Startup costs (\$)"], default=[0.])
+        startup_delays = scalar(dict["Startup delays (h)"], default = [1])
+        startup_costs = scalar(dict["Startup costs (\$)"], default = [0.0])
        startup_categories = StartupCategory[]
        for k in 1:length(startup_delays)
-            push!(startup_categories, StartupCategory(startup_delays[k],
-                                                      startup_costs[k]))
+            push!(
+                startup_categories,
+                StartupCategory(
+                    startup_delays[k] .* time_multiplier,
+                    startup_costs[k],
+                ),
+            )
        end

        # Read and validate initial conditions
-        initial_power = scalar(dict["Initial power (MW)"], default=nothing)
-        initial_status = scalar(dict["Initial status (h)"], default=nothing)
+        initial_power = scalar(dict["Initial power (MW)"], default = nothing)
+        initial_status = scalar(dict["Initial status (h)"], default = nothing)
        if initial_power === nothing
-            initial_status === nothing || error("unit $unit_name has initial status but no initial power")
+            initial_status === nothing ||
+                error("unit $unit_name has initial status but no initial power")
        else
-            initial_status !== nothing || error("unit $unit_name has initial power but no initial status")
-            initial_status != 0 || error("unit $unit_name has invalid initial status")
+            initial_status !== nothing ||
+                error("unit $unit_name has initial power but no initial status")
+            initial_status != 0 ||
+                error("unit $unit_name has invalid initial status")
            if initial_status < 0 && initial_power > 1e-3
                error("unit $unit_name has invalid initial power")
            end
+            initial_status *= time_multiplier
        end

-        unit = Unit(unit_name,
+        unit = Unit(
+            unit_name,
            bus,
            max_power,
            min_power,
-                    timeseries(dict["Must run?"], default=[false for t in 1:T]),
+            timeseries(dict["Must run?"], default = [false for t in 1:T]),
            min_power_cost,
            segments,
-                    scalar(dict["Minimum uptime (h)"], default=1),
-                    scalar(dict["Minimum downtime (h)"], default=1),
-                    scalar(dict["Ramp up limit (MW)"], default=1e6),
-                    scalar(dict["Ramp down limit (MW)"], default=1e6),
-                    scalar(dict["Startup limit (MW)"], default=1e6),
-                    scalar(dict["Shutdown limit (MW)"], default=1e6),
+            scalar(dict["Minimum uptime (h)"], default = 1) * time_multiplier,
+            scalar(dict["Minimum downtime (h)"], default = 1) * time_multiplier,
+            scalar(dict["Ramp up limit (MW)"], default = 1e6),
+            scalar(dict["Ramp down limit (MW)"], default = 1e6),
+            scalar(dict["Startup limit (MW)"], default = 1e6),
+            scalar(dict["Shutdown limit (MW)"], default = 1e6),
            initial_status,
            initial_power,
-                    timeseries(dict["Provides spinning reserves?"],
-                               default=[true for t in 1:T]),
-                    startup_categories)
+            timeseries(
+                dict["Provides spinning reserves?"],
+                default = [true for t in 1:T],
+            ),
+            startup_categories,
+        )
        push!(bus.units, unit)
        name_to_unit[unit_name] = unit
        push!(units, unit)
@@ -230,25 +256,33 @@ function from_json(json; fix=true)
    # Read reserves
    reserves = Reserves(zeros(T))
    if "Reserves" in keys(json)
-        reserves.spinning = timeseries(json["Reserves"]["Spinning (MW)"],
-                                       default=zeros(T))
+        reserves.spinning =
+            timeseries(json["Reserves"]["Spinning (MW)"], default = zeros(T))
    end

    # Read transmission lines
    if "Transmission lines" in keys(json)
        for (line_name, dict) in json["Transmission lines"]
-            line = TransmissionLine(line_name,
+            line = TransmissionLine(
+                line_name,
                length(lines) + 1,
                name_to_bus[dict["Source bus"]],
                name_to_bus[dict["Target bus"]],
                scalar(dict["Reactance (ohms)"]),
                scalar(dict["Susceptance (S)"]),
-                                    timeseries(dict["Normal flow limit (MW)"],
-                                               default=[1e8 for t in 1:T]),
-                                    timeseries(dict["Emergency flow limit (MW)"],
-                                               default=[1e8 for t in 1:T]),
-                                    timeseries(dict["Flow limit penalty (\$/MW)"],
-                                               default=[5000.0 for t in 1:T]))
+                timeseries(
+                    dict["Normal flow limit (MW)"],
+                    default = [1e8 for t in 1:T],
+                ),
+                timeseries(
+                    dict["Emergency flow limit (MW)"],
+                    default = [1e8 for t in 1:T],
+                ),
+                timeseries(
+                    dict["Flow limit penalty (\$/MW)"],
+                    default = [5000.0 for t in 1:T],
+                ),
+            )
            name_to_line[line_name] = line
            push!(lines, line)
        end
@@ -260,10 +294,12 @@ function from_json(json; fix=true)
            affected_units = Unit[]
            affected_lines = TransmissionLine[]
            if "Affected lines" in keys(dict)
-                affected_lines = [name_to_line[l] for l in dict["Affected lines"]]
+                affected_lines =
+                    [name_to_line[l] for l in dict["Affected lines"]]
            end
            if "Affected units" in keys(dict)
-                affected_units = [name_to_unit[u] for u in dict["Affected units"]]
+                affected_units =
+                    [name_to_unit[u] for u in dict["Affected units"]]
            end
            cont = Contingency(cont_name, affected_lines, affected_units)
            push!(contingencies, cont)
@@ -274,7 +310,8 @@ function from_json(json; fix=true)
    if "Price-sensitive loads" in keys(json)
        for (load_name, dict) in json["Price-sensitive loads"]
            bus = name_to_bus[dict["Bus"]]
-            load = PriceSensitiveLoad(load_name,
+            load = PriceSensitiveLoad(
+                load_name,
                bus,
                timeseries(dict["Demand (MW)"]),
                timeseries(dict["Revenue (\$/MW)"]),
@@ -284,21 +321,22 @@ function from_json(json; fix=true)
        end
    end

-    instance = UnitCommitmentInstance(T,
+    instance = UnitCommitmentInstance(
+        T,
        power_balance_penalty,
        units,
        buses,
        lines,
        reserves,
        contingencies,
-                                      loads)
-    if fix
-        UnitCommitment.fix!(instance)
+        loads,
+    )
+    if repair
+        UnitCommitment.repair!(instance)
    end
    return instance
 end

-
 """
    slice(instance, range)

@@ -314,7 +352,10 @@ Example
    modified = UnitCommitment.slice(instance, 1:2)

 """
-function slice(instance::UnitCommitmentInstance, range::UnitRange{Int})::UnitCommitmentInstance
+function slice(
+    instance::UnitCommitmentInstance,
+    range::UnitRange{Int},
+)::UnitCommitmentInstance
    modified = deepcopy(instance)
    modified.time = length(range)
    modified.power_balance_penalty = modified.power_balance_penalty[range]
@@ -345,5 +386,4 @@ function slice(instance::UnitCommitmentInstance, range::UnitRange{Int})::UnitCom
    return modified
 end

-
 export UnitCommitmentInstance
--- a/src/log.jl
+++ b/src/log.jl
@@ -7,24 +7,25 @@ using Base.CoreLogging, Logging, Printf

 struct TimeLogger <: AbstractLogger
    initial_time::Float64
-    file::Union{Nothing, IOStream}
-    screen_log_level
-    io_log_level
+    file::Union{Nothing,IOStream}
+    screen_log_level::Any
+    io_log_level::Any
 end

 function TimeLogger(;
    initial_time::Float64,
-                    file::Union{Nothing, IOStream} = nothing,
+    file::Union{Nothing,IOStream} = nothing,
    screen_log_level = CoreLogging.Info,
    io_log_level = CoreLogging.Info,
-                   ) :: TimeLogger
+)::TimeLogger
    return TimeLogger(initial_time, file, screen_log_level, io_log_level)
 end

 min_enabled_level(logger::TimeLogger) = logger.io_log_level
 shouldlog(logger::TimeLogger, level, _module, group, id) = true

-function handle_message(logger::TimeLogger,
+function handle_message(
+    logger::TimeLogger,
    level,
    message,
    _module,
@@ -32,11 +33,21 @@ function handle_message(logger::TimeLogger,
    id,
    filepath,
    line;
-                        kwargs...)
+    kwargs...,
+)
    elapsed_time = time() - logger.initial_time
    time_string = @sprintf("[%12.3f] ", elapsed_time)
+
+    if level >= Logging.Error
+        color = :light_red
+    elseif level >= Logging.Warn
+        color = :light_yellow
+    else
+        color = :light_green
+    end
+
    if level >= logger.screen_log_level
-        print(time_string)
+        printstyled(time_string, color = color)
        println(message)
    end
    if logger.file !== nothing && level >= logger.io_log_level
@@ -47,4 +58,7 @@ function handle_message(logger::TimeLogger,
    end
 end

-export TimeLogger
+function _setup_logger()
+    initial_time = time()
+    return global_logger(TimeLogger(initial_time = initial_time))
+end
--- a/src/model.jl
+++ b/src/model.jl
--- a/src/screening.jl
+++ b/src/screening.jl
@@ -4,49 +4,44 @@
 # Copyright (C) 2019 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>

-
 using DataStructures
 using Base.Threads

-
 struct Violation
    time::Int
    monitored_line::TransmissionLine
-    outage_line::Union{TransmissionLine, Nothing}
+    outage_line::Union{TransmissionLine,Nothing}
    amount::Float64  # Violation amount (in MW)
 end

-
 function Violation(;
    time::Int,
    monitored_line::TransmissionLine,
-                   outage_line::Union{TransmissionLine, Nothing},
+    outage_line::Union{TransmissionLine,Nothing},
    amount::Float64,
-                  ) :: Violation
+)::Violation
    return Violation(time, monitored_line, outage_line, amount)
 end

-
 mutable struct ViolationFilter
    max_per_line::Int
    max_total::Int
-    queues::Dict{Int, PriorityQueue{Violation, Float64}}
+    queues::Dict{Int,PriorityQueue{Violation,Float64}}
 end

-
 function ViolationFilter(;
-                         max_per_line::Int=1,
-                         max_total::Int=5,
-                        )::ViolationFilter
+    max_per_line::Int = 1,
+    max_total::Int = 5,
+)::ViolationFilter
    return ViolationFilter(max_per_line, max_total, Dict())
 end

-
-function offer(filter::ViolationFilter, v::Violation)::Nothing
+function _offer(filter::ViolationFilter, v::Violation)::Nothing
    if v.monitored_line.offset ∉ keys(filter.queues)
-        filter.queues[v.monitored_line.offset] = PriorityQueue{Violation, Float64}()
+        filter.queues[v.monitored_line.offset] =
+            PriorityQueue{Violation,Float64}()
    end
-    q::PriorityQueue{Violation, Float64} = filter.queues[v.monitored_line.offset]
+    q::PriorityQueue{Violation,Float64} = filter.queues[v.monitored_line.offset]
    if length(q) < filter.max_per_line
        enqueue!(q, v => v.amount)
    else
@@ -55,13 +50,12 @@ function offer(filter::ViolationFilter, v::Violation)::Nothing
            enqueue!(q, v => v.amount)
        end
    end
-    nothing
+    return nothing
 end

-
-function query(filter::ViolationFilter)::Array{Violation, 1}
+function _query(filter::ViolationFilter)::Array{Violation,1}
    violations = Array{Violation,1}()
-    time_queue = PriorityQueue{Violation, Float64}()
+    time_queue = PriorityQueue{Violation,Float64}()
    for l in keys(filter.queues)
        line_queue = filter.queues[l]
        while length(line_queue) > 0
@@ -82,35 +76,36 @@ function query(filter::ViolationFilter)::Array{Violation, 1}
    return violations
 end

-
 """

-    function find_violations(instance::UnitCommitmentInstance,
-                             net_injections::Array{Float64, 2};
-                             isf::Array{Float64,2},
-                             lodf::Array{Float64,2},
-                             max_per_line::Int = 1,
-                             max_per_period::Int = 5,
-                            ) :: Array{Violation, 1}
-
-Find transmission constraint violations (both pre-contingency, as well as post-contingency).
-
-The argument `net_injection` should be a (B-1) x T matrix, where B is the number of buses
-and T is the number of time periods. The arguments `isf` and `lodf` can be computed using
-UnitCommitment.injection_shift_factors and UnitCommitment.line_outage_factors.
-The argument `overflow` specifies how much flow above the transmission limits (in MW) is allowed.
-It should be an L x T matrix, where L is the number of transmission lines.
-"""
-function find_violations(;
+    function _find_violations(
        instance::UnitCommitmentInstance,
-                         net_injections::Array{Float64, 2},
-                         overflow::Array{Float64, 2},
+        net_injections::Array{Float64, 2};
        isf::Array{Float64,2},
        lodf::Array{Float64,2},
        max_per_line::Int = 1,
        max_per_period::Int = 5,
    )::Array{Violation, 1}

+Find transmission constraint violations (both pre-contingency, as well as
+post-contingency).
+
+The argument `net_injection` should be a (B-1) x T matrix, where B is the
+number of buses and T is the number of time periods. The arguments `isf` and
+`lodf` can be computed using UnitCommitment.injection_shift_factors and
+UnitCommitment.line_outage_factors. The argument `overflow` specifies how much
+flow above the transmission limits (in MW) is allowed. It should be an L x T
+matrix, where L is the number of transmission lines.
+"""
+function _find_violations(;
+    instance::UnitCommitmentInstance,
+    net_injections::Array{Float64,2},
+    overflow::Array{Float64,2},
+    isf::Array{Float64,2},
+    lodf::Array{Float64,2},
+    max_per_line::Int = 1,
+    max_per_period::Int = 5,
+)::Array{Violation,1}
    B = length(instance.buses) - 1
    L = length(instance.lines)
    T = instance.time
@@ -120,20 +115,27 @@ function find_violations(;
    size(isf) == (L, B) || error("isf has incorrect size")
    size(lodf) == (L, L) || error("lodf has incorrect size")

-    filters = Dict(t => ViolationFilter(max_total=max_per_period,
-                                        max_per_line=max_per_line)
-                   for t in 1:T)
+    filters = Dict(
+        t => ViolationFilter(
+            max_total = max_per_period,
+            max_per_line = max_per_line,
+        ) for t in 1:T
+    )

    pre_flow::Array{Float64} = zeros(L, K)           # pre_flow[lm, thread]
    post_flow::Array{Float64} = zeros(L, L, K)       # post_flow[lm, lc, thread]
    pre_v::Array{Float64} = zeros(L, K)              # pre_v[lm, thread]
    post_v::Array{Float64} = zeros(L, L, K)          # post_v[lm, lc, thread]

-    normal_limits::Array{Float64,2} = [l.normal_flow_limit[t] + overflow[l.offset, t]
-                                       for l in instance.lines, t in 1:T]
+    normal_limits::Array{Float64,2} = [
+        l.normal_flow_limit[t] + overflow[l.offset, t] for
+        l in instance.lines, t in 1:T
+    ]

-    emergency_limits::Array{Float64,2} = [l.emergency_flow_limit[t] + overflow[l.offset, t]
-                                          for l in instance.lines, t in 1:T]
+    emergency_limits::Array{Float64,2} = [
+        l.emergency_flow_limit[t] + overflow[l.offset, t] for
+        l in instance.lines, t in 1:T
+    ]

    is_vulnerable::Array{Bool} = zeros(Bool, L)
    for c in instance.contingencies
@@ -148,51 +150,63 @@ function find_violations(;

        # Post-contingency flows
        for lc in 1:L, lm in 1:L
-            post_flow[lm, lc, k] = pre_flow[lm, k] + pre_flow[lc, k] * lodf[lm, lc]
+            post_flow[lm, lc, k] =
+                pre_flow[lm, k] + pre_flow[lc, k] * lodf[lm, lc]
        end

        # Pre-contingency violations
        for lm in 1:L
-            pre_v[lm, k] = max(0.0,
+            pre_v[lm, k] = max(
+                0.0,
                pre_flow[lm, k] - normal_limits[lm, t],
-                               - pre_flow[lm, k] - normal_limits[lm, t])
+                -pre_flow[lm, k] - normal_limits[lm, t],
+            )
        end

        # Post-contingency violations
        for lc in 1:L, lm in 1:L
-            post_v[lm, lc, k] = max(0.0,
+            post_v[lm, lc, k] = max(
+                0.0,
                post_flow[lm, lc, k] - emergency_limits[lm, t],
-                                    - post_flow[lm, lc, k] - emergency_limits[lm, t])
+                -post_flow[lm, lc, k] - emergency_limits[lm, t],
+            )
        end

        # Offer pre-contingency violations
        for lm in 1:L
            if pre_v[lm, k] > 1e-5
-                offer(filters[t], Violation(time=t,
-                                            monitored_line=instance.lines[lm],
-                                            outage_line=nothing,
-                                            amount=pre_v[lm, k]))
+                _offer(
+                    filters[t],
+                    Violation(
+                        time = t,
+                        monitored_line = instance.lines[lm],
+                        outage_line = nothing,
+                        amount = pre_v[lm, k],
+                    ),
+                )
            end
        end

        # Offer post-contingency violations
        for lm in 1:L, lc in 1:L
            if post_v[lm, lc, k] > 1e-5 && is_vulnerable[lc]
-                offer(filters[t], Violation(time=t,
-                                            monitored_line=instance.lines[lm],
-                                            outage_line=instance.lines[lc],
-                                            amount=post_v[lm, lc, k]))
+                _offer(
+                    filters[t],
+                    Violation(
+                        time = t,
+                        monitored_line = instance.lines[lm],
+                        outage_line = instance.lines[lc],
+                        amount = post_v[lm, lc, k],
+                    ),
+                )
            end
        end
    end

    violations = Violation[]
    for t in 1:instance.time
-        append!(violations, query(filters[t]))
+        append!(violations, _query(filters[t]))
    end

    return violations
 end
-
-
-export Violation, ViolationFilter, offer, query, find_violations
--- a/src/sensitivity.jl
+++ b/src/sensitivity.jl
@@ -5,31 +5,38 @@
 using SparseArrays, Base.Threads, LinearAlgebra, JuMP

 """
-    injection_shift_factors(; buses, lines)
+    _injection_shift_factors(; buses, lines)

-Returns a (B-1)xL matrix M, where B is the number of buses and L is the number of transmission
-lines. For a given bus b and transmission line l, the entry M[l.offset, b.offset] indicates
-the amount of power (in MW) that flows through transmission line l when 1 MW of power is
-injected at the slack bus (the bus that has offset zero) and withdrawn from b.
+Returns a (B-1)xL matrix M, where B is the number of buses and L is the number
+of transmission lines. For a given bus b and transmission line l, the entry
+M[l.offset, b.offset] indicates the amount of power (in MW) that flows through
+transmission line l when 1 MW of power is injected at the slack bus (the bus
+that has offset zero) and withdrawn from b.
 """
-function injection_shift_factors(; buses, lines)
-    susceptance = susceptance_matrix(lines)
-    incidence = reduced_incidence_matrix(lines = lines, buses = buses)
+function _injection_shift_factors(;
+    buses::Array{Bus},
+    lines::Array{TransmissionLine},
+)
+    susceptance = _susceptance_matrix(lines)
+    incidence = _reduced_incidence_matrix(lines = lines, buses = buses)
    laplacian = transpose(incidence) * susceptance * incidence
    isf = susceptance * incidence * inv(Array(laplacian))
    return isf
 end

-
 """
-    reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{TransmissionLine})
+    _reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{TransmissionLine})

-Returns the incidence matrix for the network, with the column corresponding to the slack
-bus is removed. More precisely, returns a (B-1) x L matrix, where B is the number of buses
-and L is the number of lines. For each row, there is a 1 element and a -1 element, indicating
-the source and target buses, respectively, for that line.
+Returns the incidence matrix for the network, with the column corresponding to
+the slack bus is removed. More precisely, returns a (B-1) x L matrix, where B
+is the number of buses and L is the number of lines. For each row, there is a 1
+element and a -1 element, indicating the source and target buses, respectively,
+for that line.
 """
-function reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{TransmissionLine})
+function _reduced_incidence_matrix(;
+    buses::Array{Bus},
+    lines::Array{TransmissionLine},
+)
    matrix = spzeros(Float64, length(lines), length(buses) - 1)
    for line in lines
        if line.source.offset > 0
@@ -39,37 +46,34 @@ function reduced_incidence_matrix(; buses::Array{Bus}, lines::Array{Transmission
            matrix[line.offset, line.target.offset] = -1
        end
    end
-    matrix
+    return matrix
 end

 """
-    susceptance_matrix(lines::Array{TransmissionLine})
+    _susceptance_matrix(lines::Array{TransmissionLine})

-Returns a LxL diagonal matrix, where each diagonal entry is the susceptance of the
-corresponding transmission line.
+Returns a LxL diagonal matrix, where each diagonal entry is the susceptance of
+the corresponding transmission line.
 """
-function susceptance_matrix(lines::Array{TransmissionLine})
+function _susceptance_matrix(lines::Array{TransmissionLine})
    return Diagonal([l.susceptance for l in lines])
 end

-
 """

-    line_outage_factors(; buses, lines, isf)
+    _line_outage_factors(; buses, lines, isf)

-Returns a LxL matrix containing the Line Outage Distribution Factors (LODFs) for the
-given network. This matrix how does the pre-contingency flow change when each individual
-transmission line is removed.
+Returns a LxL matrix containing the Line Outage Distribution Factors (LODFs)
+for the given network. This matrix how does the pre-contingency flow change
+when each individual transmission line is removed.
 """
-function line_outage_factors(;
-                             buses::Array{Bus, 1},
-                             lines::Array{TransmissionLine, 1},
+function _line_outage_factors(;
+    buses::Array{Bus,1},
+    lines::Array{TransmissionLine,1},
    isf::Array{Float64,2},
-                            ) :: Array{Float64,2}
-
+)::Array{Float64,2}
    n_lines, n_buses = size(isf)
-    incidence = Array(reduced_incidence_matrix(lines=lines,
-                                               buses=buses))
+    incidence = Array(_reduced_incidence_matrix(lines = lines, buses = buses))
    lodf::Array{Float64,2} = isf * transpose(incidence)
    m, n = size(lodf)
    for i in 1:n
--- a/src/sysimage.jl
+++ b/src/sysimage.jl
@@ -10,14 +10,11 @@ using JuMP
 using MathOptInterface
 using SparseArrays

-pkg = [:DataStructures,
-       :JSON,
-       :JuMP,
-       :MathOptInterface,
-       :SparseArrays,
-       ]
+pkg = [:DataStructures, :JSON, :JuMP, :MathOptInterface, :SparseArrays]

@info "Building system image..."
-create_sysimage(pkg,
-                precompile_statements_file="build/precompile.jl",
-                sysimage_path="build/sysimage.so")
+create_sysimage(
+    pkg,
+    precompile_statements_file = "build/precompile.jl",
+    sysimage_path = "build/sysimage.so",
+)
--- a/src/validate.jl
+++ b/src/validate.jl
@@ -7,15 +7,16 @@ using Printf
 bin(x) = [xi > 0.5 for xi in x]

 """
-    fix!(instance)
+    repair!(instance)

-Verifies that the given unit commitment instance is valid and automatically fixes
-some validation errors if possible, issuing a warning for each error found.
-If a validation error cannot be automatically fixed, issues an exception.
+Verifies that the given unit commitment instance is valid and automatically
+fixes some validation errors if possible, issuing a warning for each error
+found. If a validation error cannot be automatically fixed, issues an
+exception.

 Returns the number of validation errors found.
 """
-function fix!(instance::UnitCommitmentInstance)::Int
+function repair!(instance::UnitCommitmentInstance)::Int
    n_errors = 0

    for g in instance.units
@@ -39,7 +40,6 @@ function fix!(instance::UnitCommitmentInstance)::Int
                g.startup_categories[s].cost = new_value
                n_errors += 1
            end
-
        end

        for t in 1:instance.time
@@ -67,18 +67,15 @@ function fix!(instance::UnitCommitmentInstance)::Int
        end
    end

-    
    return n_errors
 end

-
 function validate(instance_filename::String, solution_filename::String)
    instance = UnitCommitment.read(instance_filename)
    solution = JSON.parse(open(solution_filename))
    return validate(instance, solution)
 end

-
 """
    validate(instance, solution)::Bool

@@ -86,16 +83,18 @@ Verifies that the given solution is feasible for the problem. If feasible,
 silently returns true. In infeasible, returns false and prints the validation
 errors to the screen.

-This function is implemented independently from the optimization model in `model.jl`, and
-therefore can be used to verify that the model is indeed producing valid solutions. It
-can also be used to verify the solutions produced by other optimization packages.
+This function is implemented independently from the optimization model in
+`model.jl`, and therefore can be used to verify that the model is indeed
+producing valid solutions. It can also be used to verify the solutions produced
+by other optimization packages.
 """
-function validate(instance::UnitCommitmentInstance,
-                  solution::Union{Dict,OrderedDict};
-                 )::Bool
+function validate(
+    instance::UnitCommitmentInstance,
+    solution::Union{Dict,OrderedDict},
+)::Bool
    err_count = 0
-    err_count += validate_units(instance, solution)
-    err_count += validate_reserve_and_demand(instance, solution)
+    err_count += _validate_units(instance, solution)
+    err_count += _validate_reserve_and_demand(instance, solution)

    if err_count > 0
        @error "Found $err_count validation errors"
@@ -105,8 +104,7 @@ function validate(instance::UnitCommitmentInstance,
    return true
 end

-
-function validate_units(instance, solution; tol=0.01)
+function _validate_units(instance, solution; tol = 0.01)
    err_count = 0

    for unit in instance.units
@@ -121,7 +119,8 @@ function validate_units(instance, solution; tol=0.01)
            if t == 1
                is_starting_up = (unit.initial_status < 0) && is_on[t]
                is_shutting_down = (unit.initial_status > 0) && !is_on[t]
-                ramp_up = max(0, production[t] + reserve[t] - unit.initial_power)
+                ramp_up =
+                    max(0, production[t] + reserve[t] - unit.initial_power)
                ramp_down = max(0, unit.initial_power - production[t])
            else
                is_starting_up = !is_on[t-1] && is_on[t]
@@ -144,71 +143,120 @@ function validate_units(instance, solution; tol=0.01)

            # Production should be non-negative
            if production[t] < -tol
-                @error @sprintf("Unit %s produces negative amount of power at time %d (%.2f)",
-                                unit.name, t, production[t])
+                @error @sprintf(
+                    "Unit %s produces negative amount of power at time %d (%.2f)",
+                    unit.name,
+                    t,
+                    production[t]
+                )
                err_count += 1
            end

            # Verify must-run
            if !is_on[t] && unit.must_run[t]
-                @error @sprintf("Must-run unit %s is offline at time %d",
-                                unit.name, t)
+                @error @sprintf(
+                    "Must-run unit %s is offline at time %d",
+                    unit.name,
+                    t
+                )
                err_count += 1
            end

            # Verify reserve eligibility
            if !unit.provides_spinning_reserves[t] && reserve[t] > tol
-                @error @sprintf("Unit %s is not eligible to provide spinning reserves at time %d",
-                                unit.name, t)
+                @error @sprintf(
+                    "Unit %s is not eligible to provide spinning reserves at time %d",
+                    unit.name,
+                    t
+                )
                err_count += 1
            end

            # If unit is on, must produce at least its minimum power
            if is_on[t] && (production[t] < unit.min_power[t] - tol)
-                @error @sprintf("Unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
-                                unit.name, t, production[t], unit.min_power[t])
+                @error @sprintf(
+                    "Unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
+                    unit.name,
+                    t,
+                    production[t],
+                    unit.min_power[t]
+                )
                err_count += 1
            end

            # If unit is on, must produce at most its maximum power
-            if is_on[t] && (production[t] + reserve[t] > unit.max_power[t] + tol)
-                @error @sprintf("Unit %s produces above its maximum limit at time %d (%.2f + %.2f> %.2f)",
-                                unit.name, t, production[t], reserve[t], unit.max_power[t])
+            if is_on[t] &&
+               (production[t] + reserve[t] > unit.max_power[t] + tol)
+                @error @sprintf(
+                    "Unit %s produces above its maximum limit at time %d (%.2f + %.2f> %.2f)",
+                    unit.name,
+                    t,
+                    production[t],
+                    reserve[t],
+                    unit.max_power[t]
+                )
                err_count += 1
            end

            # If unit is off, must produce zero
            if !is_on[t] && production[t] + reserve[t] > tol
-                @error @sprintf("Unit %s produces power at time %d while off",
-                                unit.name, t)
+                @error @sprintf(
+                    "Unit %s produces power at time %d while off",
+                    unit.name,
+                    t
+                )
                err_count += 1
            end

            # Startup limit
            if is_starting_up && (ramp_up > unit.startup_limit + tol)
-                @error @sprintf("Unit %s exceeds startup limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_up, unit.startup_limit)
+                @error @sprintf(
+                    "Unit %s exceeds startup limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_up,
+                    unit.startup_limit
+                )
                err_count += 1
            end

            # Shutdown limit
            if is_shutting_down && (ramp_down > unit.shutdown_limit + tol)
-                @error @sprintf("Unit %s exceeds shutdown limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_down, unit.shutdown_limit)
+                @error @sprintf(
+                    "Unit %s exceeds shutdown limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_down,
+                    unit.shutdown_limit
+                )
                err_count += 1
            end

            # Ramp-up limit
-            if !is_starting_up && !is_shutting_down && (ramp_up > unit.ramp_up_limit + tol)
-                @error @sprintf("Unit %s exceeds ramp up limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_up, unit.ramp_up_limit)
+            if !is_starting_up &&
+               !is_shutting_down &&
+               (ramp_up > unit.ramp_up_limit + tol)
+                @error @sprintf(
+                    "Unit %s exceeds ramp up limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_up,
+                    unit.ramp_up_limit
+                )
                err_count += 1
            end

            # Ramp-down limit
-            if !is_starting_up && !is_shutting_down && (ramp_down > unit.ramp_down_limit + tol)
-                @error @sprintf("Unit %s exceeds ramp down limit at time %d (%.2f > %.2f)",
-                                unit.name, t, ramp_down, unit.ramp_down_limit)
+            if !is_starting_up &&
+               !is_shutting_down &&
+               (ramp_down > unit.ramp_down_limit + tol)
+                @error @sprintf(
+                    "Unit %s exceeds ramp down limit at time %d (%.2f > %.2f)",
+                    unit.name,
+                    t,
+                    ramp_down,
+                    unit.ramp_down_limit
+                )
                err_count += 1
            end

@@ -218,7 +266,7 @@ function validate_units(instance, solution; tol=0.01)
                # Calculate how much time the unit has been offline
                time_down = 0
                for k in 1:(t-1)
-                    if !is_on[t - k]
+                    if !is_on[t-k]
                        time_down += 1
                    else
                        break
@@ -241,8 +289,11 @@ function validate_units(instance, solution; tol=0.01)

                # Check minimum downtime
                if time_down < unit.min_downtime
-                    @error @sprintf("Unit %s violates minimum downtime at time %d",
-                                    unit.name, t)
+                    @error @sprintf(
+                        "Unit %s violates minimum downtime at time %d",
+                        unit.name,
+                        t
+                    )
                    err_count += 1
                end
            end
@@ -253,7 +304,7 @@ function validate_units(instance, solution; tol=0.01)
                # Calculate how much time the unit has been online
                time_up = 0
                for k in 1:(t-1)
-                    if is_on[t - k]
+                    if is_on[t-k]
                        time_up += 1
                    else
                        break
@@ -273,62 +324,92 @@ function validate_units(instance, solution; tol=0.01)

                # Check minimum uptime
                if time_up < unit.min_uptime
-                    @error @sprintf("Unit %s violates minimum uptime at time %d",
-                                    unit.name, t)
+                    @error @sprintf(
+                        "Unit %s violates minimum uptime at time %d",
+                        unit.name,
+                        t
+                    )
                    err_count += 1
                end
            end

            # Verify production costs
            if abs(actual_production_cost[t] - production_cost) > 1.00
-                @error @sprintf("Unit %s has unexpected production cost at time %d (%.2f should be %.2f)",
-                                unit.name, t, actual_production_cost[t], production_cost)
+                @error @sprintf(
+                    "Unit %s has unexpected production cost at time %d (%.2f should be %.2f)",
+                    unit.name,
+                    t,
+                    actual_production_cost[t],
+                    production_cost
+                )
                err_count += 1
            end

            # Verify startup costs
            if abs(actual_startup_cost[t] - startup_cost) > 1.00
-                @error @sprintf("Unit %s has unexpected startup cost at time %d (%.2f should be %.2f)",
-                                unit.name, t, actual_startup_cost[t], startup_cost)
+                @error @sprintf(
+                    "Unit %s has unexpected startup cost at time %d (%.2f should be %.2f)",
+                    unit.name,
+                    t,
+                    actual_startup_cost[t],
+                    startup_cost
+                )
                err_count += 1
            end
-        
        end
    end

    return err_count
 end

-
-function validate_reserve_and_demand(instance, solution, tol=0.01)
+function _validate_reserve_and_demand(instance, solution, tol = 0.01)
    err_count = 0
    for t in 1:instance.time
        load_curtail = 0
        fixed_load = sum(b.load[t] for b in instance.buses)
-        production = sum(solution["Production (MW)"][g.name][t]
-                         for g in instance.units)
-        if "Load curtail (MW)" in keys(solution)
-            load_curtail = sum(solution["Load curtail (MW)"][b.name][t]
-                               for b in instance.buses)
+        ps_load = 0
+        if length(instance.price_sensitive_loads) > 0
+            ps_load = sum(
+                solution["Price-sensitive loads (MW)"][ps.name][t] for
+                ps in instance.price_sensitive_loads
+            )
        end
-        balance = fixed_load - load_curtail - production
+        production =
+            sum(solution["Production (MW)"][g.name][t] for g in instance.units)
+        if "Load curtail (MW)" in keys(solution)
+            load_curtail = sum(
+                solution["Load curtail (MW)"][b.name][t] for
+                b in instance.buses
+            )
+        end
+        balance = fixed_load - load_curtail - production + ps_load

        # Verify that production equals demand
        if abs(balance) > tol
-            @error @sprintf("Non-zero power balance at time %d (%.2f - %.2f - %.2f != 0)",
-                            t, fixed_load, load_curtail, production)
+            @error @sprintf(
+                "Non-zero power balance at time %d (%.2f + %.2f - %.2f - %.2f != 0)",
+                t,
+                fixed_load,
+                ps_load,
+                load_curtail,
+                production,
+            )
            err_count += 1
        end

        # Verify spinning reserves
-        reserve = sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
+        reserve =
+            sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
        if reserve < instance.reserves.spinning[t] - tol
-            @error @sprintf("Insufficient spinning reserves at time %d (%.2f should be %.2f)",
-                            t, reserve, instance.reserves.spinning[t])
+            @error @sprintf(
+                "Insufficient spinning reserves at time %d (%.2f should be %.2f)",
+                t,
+                reserve,
+                instance.reserves.spinning[t],
+            )
            err_count += 1
        end
    end

    return err_count
 end
- 
--- a/test/convert_test.jl
+++ b/test/convert_test.jl
@@ -6,14 +6,17 @@ using UnitCommitment

@testset "convert" begin
    @testset "EGRET solution" begin
-        solution = UnitCommitment.read_egret_solution("fixtures/egret_output.json.gz")
+        solution =
+            UnitCommitment._read_egret_solution("fixtures/egret_output.json.gz")
        for attr in ["Is on", "Production (MW)", "Production cost (\$)"]
            @test attr in keys(solution)
            @test "115_STEAM_1" in keys(solution[attr])
            @test length(solution[attr]["115_STEAM_1"]) == 48
        end
-        @test solution["Production cost (\$)"]["315_CT_6"][15:20] == [0., 0., 884.44, 1470.71, 1470.71, 884.44]
-        @test solution["Startup cost (\$)"]["315_CT_6"][15:20] == [0., 0., 5665.23, 0., 0., 0.]
+        @test solution["Production cost (\$)"]["315_CT_6"][15:20] ==
+              [0.0, 0.0, 884.44, 1470.71, 1470.71, 884.44]
+        @test solution["Startup cost (\$)"]["315_CT_6"][15:20] ==
+              [0.0, 0.0, 5665.23, 0.0, 0.0, 0.0]
        @test length(keys(solution["Is on"])) == 154
    end
 end
--- a/test/instance_test.jl
+++ b/test/instance_test.jl
@@ -44,7 +44,7 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
        @test unit.startup_limit == 1e6
        @test unit.shutdown_limit == 1e6
        @test unit.must_run == [false for t in 1:4]
-        @test unit.min_power_cost                == [1400. for t in 1:4]
+        @test unit.min_power_cost == [1400.0 for t in 1:4]
        @test unit.min_uptime == 1
        @test unit.min_downtime == 1
        @test unit.provides_spinning_reserves == [true for t in 1:4]
@@ -76,7 +76,7 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
        @test unit.startup_limit == 70.0
        @test unit.shutdown_limit == 70.0
        @test unit.must_run == [true for t in 1:4]
-        @test unit.min_power_cost                == [0. for t in 1:4]
+        @test unit.min_power_cost == [0.0 for t in 1:4]
        @test unit.min_uptime == 1
        @test unit.min_downtime == 1
        @test unit.provides_spinning_reserves == [true for t in 1:4]
@@ -97,8 +97,22 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
        load = instance.price_sensitive_loads[1]
        @test load.name == "ps1"
        @test load.bus.name == "b3"
-        @test load.revenue  == [100. for t in 1:4]
-        @test load.demand   == [50.  for t in 1:4]
+        @test load.revenue == [100.0 for t in 1:4]
+        @test load.demand == [50.0 for t in 1:4]
+    end
+
+    @testset "read sub-hourly" begin
+        instance = UnitCommitment.read_benchmark("test/case14-sub-hourly")
+        @test instance.time == 4
+        unit = instance.units[1]
+        @test unit.name == "g1"
+        @test unit.min_uptime == 2
+        @test unit.min_downtime == 2
+        @test length(unit.startup_categories) == 3
+        @test unit.startup_categories[1].delay == 2
+        @test unit.startup_categories[2].delay == 4
+        @test unit.startup_categories[3].delay == 6
+        @test unit.initial_status == -200
    end

    @testset "slice" begin
@@ -135,8 +149,10 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip

        # Should be able to build model without errors
        optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
-        model = build_model(instance=modified,
-                            optimizer=optimizer,
-                            variable_names=true)
+        model = build_model(
+            instance = modified,
+            optimizer = optimizer,
+            variable_names = true,
+        )
    end
 end
--- a/test/model_test.jl
+++ b/test/model_test.jl
@@ -11,15 +11,22 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP
            line.normal_flow_limit[t] = 10.0
        end
        optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
-        model = build_model(instance=instance,
-                            optimizer=optimizer,
-                            variable_names=true)
-
-        JuMP.write_to_file(model.mip, "test.mps")
+        model = build_model(
+            instance = instance,
+            optimizer = optimizer,
+            variable_names = true,
+        )
+        @test name(model[:is_on]["g1", 1]) == "is_on[g1,1]"

        # Optimize and retrieve solution
        UnitCommitment.optimize!(model)
-        solution = get_solution(model)
+        solution = UnitCommitment.solution(model)
+
+        # Write solution to a file
+        filename = tempname()
+        UnitCommitment.write(filename, solution)
+        loaded = JSON.parsefile(filename)
+        @test length(loaded["Is on"]) == 6

        # Verify solution
        @test UnitCommitment.validate(instance, solution)
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -3,6 +3,9 @@
 # Released under the modified BSD license. See COPYING.md for more details.

 using Test
+using UnitCommitment
+
+UnitCommitment._setup_logger()

@testset "UnitCommitment" begin
    include("instance_test.jl")
--- a/test/screening_test.jl
+++ b/test/screening_test.jl
@@ -3,51 +3,83 @@
 # Released under the modified BSD license. See COPYING.md for more details.

 using UnitCommitment, Test, LinearAlgebra
+import UnitCommitment: Violation, _offer, _query

@testset "Screening" begin
    @testset "Violation filter" begin
        instance = UnitCommitment.read_benchmark("test/case14")
-        filter = ViolationFilter(max_per_line=1, max_total=2)
+        filter = UnitCommitment.ViolationFilter(max_per_line = 1, max_total = 2)

-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=nothing,
-                                amount=100.))
+        _offer(
+            filter,
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[1],
+                outage_line = nothing,
+                amount = 100.0,
+            ),
+        )
+        _offer(
+            filter,
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[1],
+                outage_line = instance.lines[1],
+                amount = 300.0,
+            ),
+        )
+        _offer(
+            filter,
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[1],
+                outage_line = instance.lines[5],
+                amount = 500.0,
+            ),
+        )
+        _offer(
+            filter,
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[1],
+                outage_line = instance.lines[4],
+                amount = 400.0,
+            ),
+        )
+        _offer(
+            filter,
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[2],
+                outage_line = instance.lines[1],
+                amount = 200.0,
+            ),
+        )
+        _offer(
+            filter,
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[2],
+                outage_line = instance.lines[8],
+                amount = 100.0,
+            ),
+        )

-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=instance.lines[1],
-                                amount=300.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=instance.lines[5],
-                                amount=500.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[1],
-                                outage_line=instance.lines[4],
-                                amount=400.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[2],
-                                outage_line=instance.lines[1],
-                                amount=200.))
-        
-        offer(filter, Violation(time=1,
-                                monitored_line=instance.lines[2],
-                                outage_line=instance.lines[8],
-                                amount=100.))
-
-        actual = query(filter)
-        expected = [Violation(time=1,
-                              monitored_line=instance.lines[2],
-                              outage_line=instance.lines[1],
-                              amount=200.),
-                    Violation(time=1,
-                              monitored_line=instance.lines[1],
-                              outage_line=instance.lines[5],
-                              amount=500.)]
+        actual = _query(filter)
+        expected = [
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[2],
+                outage_line = instance.lines[1],
+                amount = 200.0,
+            ),
+            Violation(
+                time = 1,
+                monitored_line = instance.lines[1],
+                outage_line = instance.lines[5],
+                amount = 500.0,
+            ),
+        ]
        @test actual == expected
    end

@@ -57,19 +89,24 @@ using UnitCommitment, Test, LinearAlgebra
            line.normal_flow_limit[t] = 1.0
            line.emergency_flow_limit[t] = 1.0
        end
-        isf = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                     buses=instance.buses)
-        lodf = UnitCommitment.line_outage_factors(lines=instance.lines,
-                                                  buses=instance.buses,
-                                                  isf=isf)
+        isf = UnitCommitment._injection_shift_factors(
+            lines = instance.lines,
+            buses = instance.buses,
+        )
+        lodf = UnitCommitment._line_outage_factors(
+            lines = instance.lines,
+            buses = instance.buses,
+            isf = isf,
+        )
        inj = [1000.0 for b in 1:13, t in 1:instance.time]
        overflow = [0.0 for l in instance.lines, t in 1:instance.time]
-        violations = UnitCommitment.find_violations(instance=instance,
-                                                    net_injections=inj,
-                                                    overflow=overflow,
-                                                    isf=isf,
-                                                    lodf=lodf)
-        
+        violations = UnitCommitment._find_violations(
+            instance = instance,
+            net_injections = inj,
+            overflow = overflow,
+            isf = isf,
+            lodf = lodf,
+        )
        @test length(violations) == 20
    end
 end
--- a/test/sensitivity_test.jl
+++ b/test/sensitivity_test.jl
@@ -7,19 +7,39 @@ using UnitCommitment, Test, LinearAlgebra
@testset "Sensitivity" begin
    @testset "Susceptance matrix" begin
        instance = UnitCommitment.read_benchmark("test/case14")
-        actual = UnitCommitment.susceptance_matrix(instance.lines)
+        actual = UnitCommitment._susceptance_matrix(instance.lines)
        @test size(actual) == (20, 20)
-        expected = Diagonal([29.5,  7.83,  8.82, 9.9,  10.04,
-                             10.2,  41.45, 8.35, 3.14, 6.93,
-                             8.77,  6.82,  13.4, 9.91, 15.87,
-                             20.65, 6.46,  9.09, 8.73, 5.02])
-        @test round.(actual, digits=2) == expected
+        expected = Diagonal([
+            29.5,
+            7.83,
+            8.82,
+            9.9,
+            10.04,
+            10.2,
+            41.45,
+            8.35,
+            3.14,
+            6.93,
+            8.77,
+            6.82,
+            13.4,
+            9.91,
+            15.87,
+            20.65,
+            6.46,
+            9.09,
+            8.73,
+            5.02,
+        ])
+        @test round.(actual, digits = 2) == expected
    end

    @testset "Reduced incidence matrix" begin
        instance = UnitCommitment.read_benchmark("test/case14")
-        actual = UnitCommitment.reduced_incidence_matrix(lines=instance.lines,
-                                                         buses=instance.buses)
+        actual = UnitCommitment._reduced_incidence_matrix(
+            lines = instance.lines,
+            buses = instance.buses,
+        )
        @test size(actual) == (20, 13)
        @test actual[1, 1] == -1.0
        @test actual[3, 1] == 1.0
@@ -63,49 +83,59 @@ using UnitCommitment, Test, LinearAlgebra

    @testset "Injection Shift Factors (ISF)" begin
        instance = UnitCommitment.read_benchmark("test/case14")
-        actual = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                        buses=instance.buses)
+        actual = UnitCommitment._injection_shift_factors(
+            lines = instance.lines,
+            buses = instance.buses,
+        )
        @test size(actual) == (20, 13)
-        @test round.(actual, digits=2) == [
-            -0.84 -0.75 -0.67 -0.61 -0.63 -0.66 -0.66 -0.65 -0.65 -0.64 -0.63 -0.63 -0.64;
-            -0.16 -0.25 -0.33 -0.39 -0.37 -0.34 -0.34 -0.35 -0.35 -0.36 -0.37 -0.37 -0.36;
-             0.03 -0.53 -0.15 -0.1  -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13;
-             0.06 -0.14 -0.32 -0.22 -0.25 -0.3  -0.3  -0.29 -0.28 -0.27 -0.25 -0.26 -0.27;
-             0.08 -0.07 -0.2  -0.29 -0.26 -0.22 -0.22 -0.22 -0.23 -0.25 -0.26 -0.26 -0.24;
-             0.03  0.47 -0.15 -0.1  -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13;
-             0.08  0.31  0.5  -0.3  -0.03  0.36  0.36  0.28  0.23  0.1  -0.0   0.02  0.17;
-             0.0   0.01  0.02 -0.01 -0.22 -0.63 -0.63 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36;
-             0.0   0.01  0.01 -0.01 -0.12 -0.17 -0.17 -0.26 -0.24 -0.18 -0.14 -0.14 -0.21;
-            -0.0  -0.02 -0.03  0.02 -0.66 -0.2  -0.2  -0.29 -0.36 -0.5  -0.63 -0.61 -0.43;
-            -0.0  -0.01 -0.02  0.01  0.21 -0.12 -0.12 -0.17 -0.28 -0.53  0.18  0.15 -0.03;
-            -0.0  -0.0  -0.0   0.0   0.03 -0.02 -0.02 -0.03 -0.02  0.01 -0.52 -0.17 -0.09;
-            -0.0  -0.01 -0.01  0.01  0.11 -0.06 -0.06 -0.09 -0.05  0.02 -0.28 -0.59 -0.31;
-            -0.0  -0.0  -0.0  -0.0  -0.0  -0.0  -1.0  -0.0  -0.0  -0.0  -0.0  -0.0   0.0 ;
-             0.0   0.01  0.02 -0.01 -0.22  0.37  0.37 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36;
-             0.0   0.01  0.02 -0.01 -0.21  0.12  0.12  0.17 -0.72 -0.47 -0.18 -0.15  0.03;
-             0.0   0.01  0.01 -0.01 -0.14  0.08  0.08  0.12  0.07 -0.03 -0.2  -0.24 -0.6 ;
-             0.0   0.01  0.02 -0.01 -0.21  0.12  0.12  0.17  0.28 -0.47 -0.18 -0.15  0.03;
-            -0.0  -0.0  -0.0   0.0   0.03 -0.02 -0.02 -0.03 -0.02  0.01  0.48 -0.17 -0.09;
-            -0.0  -0.01 -0.01  0.01  0.14 -0.08 -0.08 -0.12 -0.07  0.03  0.2   0.24 -0.4  ]
+        @test round.(actual, digits = 2) == [
+            -0.84 -0.75 -0.67 -0.61 -0.63 -0.66 -0.66 -0.65 -0.65 -0.64 -0.63 -0.63 -0.64
+            -0.16 -0.25 -0.33 -0.39 -0.37 -0.34 -0.34 -0.35 -0.35 -0.36 -0.37 -0.37 -0.36
+            0.03 -0.53 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
+            0.06 -0.14 -0.32 -0.22 -0.25 -0.3 -0.3 -0.29 -0.28 -0.27 -0.25 -0.26 -0.27
+            0.08 -0.07 -0.2 -0.29 -0.26 -0.22 -0.22 -0.22 -0.23 -0.25 -0.26 -0.26 -0.24
+            0.03 0.47 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
+            0.08 0.31 0.5 -0.3 -0.03 0.36 0.36 0.28 0.23 0.1 -0.0 0.02 0.17
+            0.0 0.01 0.02 -0.01 -0.22 -0.63 -0.63 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
+            0.0 0.01 0.01 -0.01 -0.12 -0.17 -0.17 -0.26 -0.24 -0.18 -0.14 -0.14 -0.21
+            -0.0 -0.02 -0.03 0.02 -0.66 -0.2 -0.2 -0.29 -0.36 -0.5 -0.63 -0.61 -0.43
+            -0.0 -0.01 -0.02 0.01 0.21 -0.12 -0.12 -0.17 -0.28 -0.53 0.18 0.15 -0.03
+            -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 -0.52 -0.17 -0.09
+            -0.0 -0.01 -0.01 0.01 0.11 -0.06 -0.06 -0.09 -0.05 0.02 -0.28 -0.59 -0.31
+            -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -1.0 -0.0 -0.0 -0.0 -0.0 -0.0 0.0
+            0.0 0.01 0.02 -0.01 -0.22 0.37 0.37 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
+            0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 -0.72 -0.47 -0.18 -0.15 0.03
+            0.0 0.01 0.01 -0.01 -0.14 0.08 0.08 0.12 0.07 -0.03 -0.2 -0.24 -0.6
+            0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 0.28 -0.47 -0.18 -0.15 0.03
+            -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 0.48 -0.17 -0.09
+            -0.0 -0.01 -0.01 0.01 0.14 -0.08 -0.08 -0.12 -0.07 0.03 0.2 0.24 -0.4
+        ]
    end

    @testset "Line Outage Distribution Factors (LODF)" begin
        instance = UnitCommitment.read_benchmark("test/case14")
-        isf_before = UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                            buses=instance.buses)
-        lodf = UnitCommitment.line_outage_factors(lines=instance.lines,
-                                                  buses=instance.buses,
-                                                  isf=isf_before)
+        isf_before = UnitCommitment._injection_shift_factors(
+            lines = instance.lines,
+            buses = instance.buses,
+        )
+        lodf = UnitCommitment._line_outage_factors(
+            lines = instance.lines,
+            buses = instance.buses,
+            isf = isf_before,
+        )
        for contingency in instance.contingencies
            for lc in contingency.lines
                prev_susceptance = lc.susceptance
                lc.susceptance = 0.0
-                isf_after =  UnitCommitment.injection_shift_factors(lines=instance.lines,
-                                                                    buses=instance.buses)
+                isf_after = UnitCommitment._injection_shift_factors(
+                    lines = instance.lines,
+                    buses = instance.buses,
+                )
                lc.susceptance = prev_susceptance
                for lm in instance.lines
                    expected = isf_after[lm.offset, :]
-                    actual = isf_before[lm.offset, :] +
+                    actual =
+                        isf_before[lm.offset, :] +
                        lodf[lm.offset, lc.offset] * isf_before[lc.offset, :]
                    @test norm(expected - actual) < 1e-6
                end
--- a/test/validate_test.jl
+++ b/test/validate_test.jl
@@ -4,18 +4,23 @@

 using UnitCommitment, JSON, GZip, DataStructures

-parse_case14() = JSON.parse(GZip.gzopen("../instances/test/case14.json.gz"),
-                            dicttype=()->DefaultOrderedDict(nothing))
+function parse_case14()
+    return JSON.parse(
+        GZip.gzopen("../instances/test/case14.json.gz"),
+        dicttype = () -> DefaultOrderedDict(nothing),
+    )
+end

@testset "Validation" begin
-    @testset "fix!" begin
-        
+    @testset "repair!" begin
        @testset "Cost curve should be convex" begin
            json = parse_case14()
-            json["Generators"]["g1"]["Production cost curve (MW)"] = [100, 150, 200]
-            json["Generators"]["g1"]["Production cost curve (\$)"] = [10, 25, 30]
-            instance = UnitCommitment.from_json(json, fix=false)
-            @test UnitCommitment.fix!(instance) == 4
+            json["Generators"]["g1"]["Production cost curve (MW)"] =
+                [100, 150, 200]
+            json["Generators"]["g1"]["Production cost curve (\$)"] =
+                [10, 25, 30]
+            instance = UnitCommitment._from_json(json, repair = false)
+            @test UnitCommitment.repair!(instance) == 4
        end

        @testset "Startup limit must be greater than Pmin" begin
@@ -23,17 +28,16 @@ parse_case14() = JSON.parse(GZip.gzopen("../instances/test/case14.json.gz"),
            json["Generators"]["g1"]["Production cost curve (MW)"] = [100, 150]
            json["Generators"]["g1"]["Production cost curve (\$)"] = [100, 150]
            json["Generators"]["g1"]["Startup limit (MW)"] = 80
-            instance = UnitCommitment.from_json(json, fix=false)
-            @test UnitCommitment.fix!(instance) == 1
+            instance = UnitCommitment._from_json(json, repair = false)
+            @test UnitCommitment.repair!(instance) == 1
        end

        @testset "Startup costs and delays must be increasing" begin
            json = parse_case14()
            json["Generators"]["g1"]["Startup costs (\$)"] = [300, 200, 100]
            json["Generators"]["g1"]["Startup delays (h)"] = [8, 4, 2]
-            instance = UnitCommitment.from_json(json, fix=false)
-            @test UnitCommitment.fix!(instance) == 4
+            instance = UnitCommitment._from_json(json, repair = false)
+            @test UnitCommitment.repair!(instance) == 4
        end
-
    end
 end
Author	SHA1	Message	Date
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