GitHub Actions: Add Julia 1.7, remove 1.4 and 1.5

2025-12-07 08:48:51 -06:00 · 2022-04-16 09:02:59 -05:00
109 changed files with 2124 additions and 3817 deletions
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -1,4 +1,4 @@
-name: Build & Test
+name: Tests
 on:
  push:
  pull_request:
@@ -6,30 +6,19 @@ on:
    - cron: '45 10 * * *'
 jobs:
  test:
    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
-        version: ['1.6', '1.7', '1.8', '1.9']
+        julia-version: ['1.6', '1.7']
-        os:
+        julia-arch: [x64]
-          - ubuntu-latest
+        os: [ubuntu-latest, windows-latest, macOS-latest]
-        arch:
+        exclude:
-          - x64
+          - os: macOS-latest
            julia-arch: x86
    steps:
      - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@latest
        with:
-          version: ${{ matrix.version }}
+          version: ${{ matrix.julia-version }}
-          arch: ${{ matrix.arch }}
+      - uses: julia-actions/julia-buildpkg@latest
-      - name: Run tests
+      - uses: julia-actions/julia-runtest@latest
        shell: julia --color=yes --project=test {0}
        run: |
          using Pkg
          Pkg.develop(path=".")
          Pkg.update()
          using UnitCommitmentT
          try
            runtests()
          catch
            exit(1)
          end
--- a/.gitignore
+++ b/.gitignore
@@ -1,38 +1,21 @@
 *.bak
 *.gz
 *.ipynb
 *.lastrun
 *.mps
 *.so
-*/Manifest.toml
+*.mps
-.AppleDB
+*.ipynb
 .AppleDesktop
 .AppleDouble
 .DS_Store
 .DocumentRevisions-V100
 .LSOverride
 .Spotlight-V100
 .TemporaryItems
 .Trashes
 .VolumeIcon.icns
 ._*
 .apdisk
 .com.apple.timemachine.donotpresent
 .fseventsd
 .ipy*
 .vscode
 Icon
 Manifest.toml
 Network Trash Folder
 TODO.md
 Temporary Items
 benchmark/results
 benchmark/runs
 benchmark/tables
 benchmark/tmp.json
 build
 docs/_build
 instances/**/*.json
 instances/_source
 local
 notebooks
 TODO.md
 docs/_build
 .vscode
 Manifest.toml
 */Manifest.toml
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,21 +11,6 @@ All notable changes to this project will be documented in this file.
 [semver]: https://semver.org/spec/v2.0.0.html
 [pkjjl]: https://pkgdocs.julialang.org/v1/compatibility/#compat-pre-1.0
 ## [0.3.0] - 2022-07-18
 ### Added
 - Add support for multiple reserve products and zonal reserves.
 - Add flexiramp reserve products, following WanHob2016's formulation (@oyurdakul, #21).
 - Add 365 variations for each MATPOWER instance, corresponding to each day of the year.
 ### Changed
 - To support multiple/zonal reserves, the input data format has been modified as follows:
    - In `Generators`, replace `Provides spinning reserves?` by `Reserve eligibility`
    - In `Parameters`, remove `Reserve shortfall penalty`
    - Revise `Reserves` section
 - To allow new versions of UnitCommitment.jl to read old instance files, a new required field `Version` has been added to the `Parameters` section. To load v0.2 files in v0.3, please add `{"Parameters":{"Version":"0.2"}}` to the file.
 - Benchmark test cases are now downloaded on-the-fly as needed, instead of being stored in our GitHub repository. Test cases can also be directly downloaded from: https://axavier.org/UnitCommitment.jl/
 ## [0.2.2] - 2021-07-21
 ### Fixed
 - Fix small bug in validation scripts related to startup costs
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,4 +1,4 @@
-Copyright © 2020-2022, UChicago Argonne, LLC
+Copyright © 2020, UChicago Argonne, LLC
 All Rights Reserved
--- a/20
+++ b/20
@@ -2,10 +2,22 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
-VERSION := 0.3
+VERSION := 0.2
 clean:
 	rm -rfv build
 docs:
-	cd docs; julia --project=. make.jl; cd ..
+	cd docs; make clean; make dirhtml
-	rsync -avP --delete-after docs/build/ ../docs/$(VERSION)/
+	rsync -avP --delete-after docs/_build/dirhtml/ ../docs/$(VERSION)/
-.PHONY: docs
+format:
 	cd deps/formatter; ../../juliaw format.jl
 test: test/Manifest.toml
 	./juliaw test/runtests.jl
 test/Manifest.toml: test/Project.toml
 	julia --project=test -e "using Pkg; Pkg.instantiate()"
 .PHONY: docs test format install-deps
--- 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 = "0.3.0"
+version = "0.2.2"
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
@@ -24,7 +24,7 @@ DataStructures = "0.18"
 Distributions = "0.25"
 GZip = "0.5"
 JSON = "0.21"
-JuMP = "1"
+JuMP = "0.21"
-MathOptInterface = "1"
+MathOptInterface = "0.9"
 PackageCompiler = "1"
 julia = "1"
--- a/README.md
+++ b/README.md
@@ -87,22 +87,19 @@ UnitCommitment.write("/tmp/output.json", solution)
 ## Documentation
-1. [Usage](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/usage/)
+1. [Usage](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/usage/)
-2. [Data Format](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/format/)
+2. [Data Format](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/format/)
-3. [Instances](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/instances/)
+3. [Instances](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/instances/)
-4. [JuMP Model](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/model/)
+4. [JuMP Model](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/model/)
 5. [API Reference](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/api/)
 ## Authors
 * **Alinson S. Xavier** (Argonne National Laboratory)
 * **Aleksandr M. Kazachkov** (University of Florida)
 * **Ogün Yurdakul** (Technische Universität Berlin)
 * **Jun He** (Purdue University)
 * **Feng Qiu** (Argonne National Laboratory)
 ## Acknowledgments
-* We would like to thank **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
+* We would like to **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
 * Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357
@@ -112,15 +109,15 @@ UnitCommitment.write("/tmp/output.json", solution)
 If you use UnitCommitment.jl in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
-* **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Feng Qiu**. "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment (Version 0.3)". Zenodo (2022). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
+* **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu**. "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
-If you use the instances, we additionally request that you cite the original sources, as described in the documentation.
+If you use the instances, we additionally request that you cite the original sources, as described in the [instances page](docs/instances.md).
 ## License
 ```text
 UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment
-Copyright © 2020-2022, UChicago Argonne, LLC. All Rights Reserved.
+Copyright © 2020-2021, UChicago Argonne, LLC. All Rights Reserved.
 Redistribution and use in source and binary forms, with or without modification, are permitted
 provided that the following conditions are met:
--- a/deps/formatter/Project.toml
+++ b/deps/formatter/Project.toml
@@ -0,0 +1,5 @@
 [deps]
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 [compat]
 JuliaFormatter = "0.14.4"
--- a/deps/formatter/format.jl
+++ b/deps/formatter/format.jl
@@ -0,0 +1,9 @@
 using JuliaFormatter
 format(
    [
        "../../src", 
        "../../test",
        "../../benchmark/run.jl",
    ],
    verbose=true,
 )
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -0,0 +1,14 @@
 SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
 SOURCEDIR     = .
 BUILDDIR      = _build
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 .PHONY: help Makefile
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -1,5 +0,0 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"
--- a/docs/src/assets/cost_curve.png
+++ b/docs/src/assets/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/docs/src/format.md
+++ b/docs/src/format.md
@@ -1,40 +1,50 @@
 ```{sectnum}
 ---
 start: 2
 depth: 2
 suffix: .
 ---
 ```
 Data Format
 ===========
 Input Data Format
 -----------------
 Instances are specified by JSON files containing the following main sections:
-* [Parameters](#Parameters)
+* Parameters
-* [Buses](#Buses)
+* Buses
-* [Generators](#Generators)
+* Generators
-* [Price-sensitive loads](#Price-sensitive-loads)
+* Price-sensitive loads
-* [Transmission lines](#Transmission-lines)
+* Transmission lines
-* [Reserves](#Reserves)
+* Reserves
-* [Contingencies](#Contingencies)
+* Contingencies
-Each section is described in detail below. See [case118/2017-01-01.json.gz](https://axavier.org/UnitCommitment.jl/0.3/instances/matpower/case118/2017-01-01.json.gz) for a complete example.
+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).
 ### Parameters
-This section describes system-wide parameters, such as power balance penalty, and optimization parameters, such as the length of the planning horizon and the time.
+This section describes system-wide parameters, such as power balance and reserve shortfall penalties, and optimization parameters, such as the length of the planning horizon and the time.
 | Key                            | Description                                       | Default  | Time series?
 | :----------------------------- | :------------------------------------------------ | :------: | :------------:
 | `Version` | Version of UnitCommitment.jl this file was written for. Required to ensure that the file remains readable in future versions of the package. If you are following this page to construct the file, this field should equal `0.3`. | Required | N
 | `Time horizon (h)` | Length of the planning horizon (in hours). | Required | N
 | `Time step (min)` | Length of each time step (in minutes). Must be a divisor of 60 (e.g. 60, 30, 20, 15, etc). | `60` | N
 | `Power balance penalty ($/MW)` | Penalty for system-wide shortage or surplus in production (in $/MW). This is charged per time step. For example, if there is a shortage of 1 MW for three time steps, three times this amount will be charged. | `1000.0` | Y
 | `Reserve shortfall penalty ($/MW)` | Penalty for system-wide shortage in meeting reserve requirements (in $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
 #### Example
 ```json
 {
    "Parameters": {
        "Version": "0.3",
        "Time horizon (h)": 4,
-        "Power balance penalty ($/MW)": 1000.0
+        "Power balance penalty ($/MW)": 1000.0,
        "Reserve shortfall penalty ($/MW)": -1.0
    }
 }
 ```
@@ -70,17 +80,11 @@ This section describes the characteristics of each bus in the system.
 ### Generators
-This section describes all generators in the system. Two types of units can be specified:
+This section describes all generators in the system, including thermal units, renewable units and virtual units.
 - **Thermal units:** Units that produce power by converting heat into electrical energy, such as coal and oil power plants. These units use a more complex model, with binary decision variables, and various constraints to enforce ramp rates and minimum up/down time.
 - **Profiled units:** Simplified model for units that do not require the constraints mentioned above, only a maximum and minimum power output for each time period. Typically used for renewables and hydro.
 #### Thermal Units
 | Key                       | Description                                      | Default | Time series?
 | :------------------------ | :------------------------------------------------| ------- | :-----------:
 | `Bus`                     | Identifier of the bus where this generator is located (string). | Required | N
 | `Type`                     | Type of the generator (string). For thermal generators, this must be `Thermal`.  | 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 `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
@@ -92,31 +96,18 @@ This section describes all generators in the system. Two types of units can be s
 | `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
-| `Reserve eligibility` | List of reserve products this generator is eligibe to provide. By default, the generator is not eligible to provide any reserves. | `[]` | N
+| `Provides spinning reserves?`    | If `true`, this generator may provide spinning reserves (Boolean). | `true` | Y
 | `Commitment status` | List of commitment status over the time horizon. At time `t`, if `true`, the generator must be commited at that time period; if `false`, the generator must not be commited at that time period. If `null` at time `t`, the generator's commitment status is then decided by the model. By default, the status is a list of `null` values. | `null` | Y
 #### Profiled Units
 | Key               | Description                                       | Default  | Time series?
 | :---------------- | :------------------------------------------------ | :------: | :------------:
 | `Bus`             | Identifier of the bus where this generator is located (string). | Required | N
 | `Type`            | Type of the generator (string). For profiled generators, this must be `Profiled`.  | Required | N
 | `Cost ($/MW)`     | Cost incurred for serving each MW of power by this generator. | Required | Y
 | `Minimum power (MW)` | Minimum amount of power this generator may supply. | `0.0` | Y
 | `Maximum power (MW)` | Maximum amount of power this generator may supply. | Required | 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 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 ($)`  should be set to `[1400, 1600, 2200, 2400]`.
 Note that this curve also specifies the production limits. Specifically, the first point identifies the minimum power output when the unit is operational, while the last point identifies the maximum power output.
 ```@raw html
 <center>
-    <img src="../assets/cost_curve.png" style="max-width: 500px"/>
+    <img src="../_static/cost_curve.png" style="max-width: 500px"/>
    <div><b>Figure 1.</b> Piecewise-linear production cost curve.</div>
    <br/>
 </center>
 ```
 #### Additional remarks:
@@ -132,7 +123,6 @@ Note that this curve also specifies the production limits. Specifically, the fir
    "Generators": {
        "gen1": {
            "Bus": "b1",
            "Type": "Thermal",
            "Production cost curve (MW)": [100.0, 110.0, 130.0, 135.0],
            "Production cost curve ($)": [1400.0, 1600.0, 2200.0, 2400.0],
            "Startup costs ($)": [300.0, 400.0],
@@ -144,26 +134,14 @@ Note that this curve also specifies the production limits. Specifically, the fir
            "Minimum downtime (h)": 4,
            "Minimum uptime (h)": 4,
            "Initial status (h)": 12,
            "Initial power (MW)": 115,
            "Must run?": false,
-            "Reserve eligibility": ["r1"]
+            "Provides spinning reserves?": true,
        },
        "gen2": {
            "Bus": "b5",
            "Type": "Thermal",
            "Production cost curve (MW)": [0.0, [10.0, 8.0, 0.0, 3.0]],
            "Production cost curve ($)": [0.0, 0.0],
-            "Initial status (h)": -100,
+            "Provides spinning reserves?": true,
            "Initial power (MW)": 0,
            "Reserve eligibility": ["r1", "r2"],
            "Commitment status": [true, false, null, true]
        },
        "gen3": {
            "Bus": "b6",
            "Type": "Profiled",
            "Minimum power (MW)": 10.0,
            "Maximum power (MW)": 120.0,
            "Cost ($/MW)": 100.0
        }
    }
 }
@@ -193,7 +171,7 @@ This section describes components in the system which may increase or reduce the
 }
 ```
-### Transmission lines
+### Transmission Lines
 This section describes the characteristics of transmission system, such as its topology and the susceptance of each transmission line.
@@ -228,39 +206,24 @@ This section describes the characteristics of transmission system, such as its t
 ### Reserves
-This section describes the hourly amount of reserves required.
+This section describes the hourly amount of operating reserves required.
 | Key                   | Description                                        | Default   |  Time series?
 | :-------------------- | :------------------------------------------------- | --------- |  :----:
-| `Type` | Type of reserve product. Must be either "spinning" or "flexiramp". | Required | N
+| `Spinning (MW)`       | Minimum amount of system-wide spinning reserves (in MW). Only generators which are online may provide this reserve. | `0.0` | Y
 | `Amount (MW)` | Amount of reserves required. | Required | Y
 | `Shortfall penalty ($/MW)` | Penalty for shortage in meeting the reserve requirements (in $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
-#### Example 1
+#### Example
 ```json
 {
    "Reserves": {
-        "r1": {
+        "Spinning (MW)": [
-            "Type": "spinning",
+            57.30552,
-            "Amount (MW)": [
+            53.88429,
-                57.30552,
+            51.31838,
-                53.88429,
+            50.46307
-                51.31838,
+        ]
                50.46307
            ],
            "Shortfall penalty ($/MW)": 5.0
        },
        "r2": {
            "Type": "flexiramp",
            "Amount (MW)": [
                20.31042,
                23.65273,
                27.41784,
                25.34057
            ],
        }
    }
 }
 ```
@@ -323,8 +286,9 @@ The output data format is also JSON-based, but it is not currently documented si
 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.
-* Flexible ramping products can only be acquired under the `WanHob2016` formulation, which does not support spinning reserves. 
+
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -6,24 +6,24 @@
 * **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
 * **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature, converted into a common data format, and extended using data-driven methods to make them more challenging and realistic.
-* **Model Implementation**: The package provides a Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000](https://doi.org/10.1109/59.871739), [MorLatRam2013](https://doi.org/10.1109/TPWRS.2013.2251373), [DamKucRajAta2016](https://doi.org/10.1007/s10107-015-0919-9), [PanGua2016](https://doi.org/10.1287/opre.2016.1520)), multiple piecewise-linear costs formulations ([Gar1962](https://doi.org/10.1109/AIEEPAS.1962.4501405), [CarArr2006](https://doi.org/10.1109/TPWRS.2006.876672), [KnuOstWat2018](https://doi.org/10.1109/TPWRS.2017.2783850)) and contingency screening methods ([XavQiuWanThi2019](https://doi.org/10.1109/TPWRS.2019.2892620)). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
+* **Model Implementation**: The package provides a Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
 * **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
-## Table of Contents
+[ArrCon2000]: https://doi.org/10.1109/59.871739
 [CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
 [DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
 [Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
 [KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
 [MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
 [PanGua2016]: https://doi.org/10.1287/opre.2016.1520
 [XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
-```@contents
+### Authors
 Pages = ["usage.md", "format.md", "instances.md", "model.md", "api.md"]
 Depth = 3
 ```
 ## Authors
 * **Alinson S. Xavier** (Argonne National Laboratory)
 * **Aleksandr M. Kazachkov** (University of Florida)
 * **Ogün Yurdakul** (Technische Universität Berlin)
 * **Jun He** (Purdue University)
 * **Feng Qiu** (Argonne National Laboratory)
-## Acknowledgments
+### Acknowledgments
 * We would like to thank **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
@@ -31,19 +31,19 @@ Depth = 3
 * Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.
-## Citing
+### Citing
 If you use UnitCommitment.jl in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
-* **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Feng Qiu**, "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment (Version 0.3)". Zenodo (2022). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
+* **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu**, "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
 If you use the instances, we additionally request that you cite the original sources, as described in the [instances page](instances.md).
-## License
+### License
 ```text
 UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment
-Copyright © 2020-2022, UChicago Argonne, LLC. All Rights Reserved.
+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:
@@ -67,3 +67,16 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING N
 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/docs/src/instances.md
+++ b/docs/src/instances.md
@@ -1,11 +1,19 @@
 ```{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. If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources, as listed below. Benchmark instances can be loaded with `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md). Instance files can also be [directly downloaded from our website](https://axavier.org/UnitCommitment.jl/0.3/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. If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources, as listed below. Benchmark instances can be loaded with `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md).
-!!! warning
+```{warning}
-
+The instances included in UC.jl are still under development and may change in the future. If you use these instances in your research, for reproducibility, you should specify what version of UC.jl they came from.
-    The instances included in UC.jl are still under development and may change in the future. If you use these instances in your research, for reproducibility, you should specify what version of UC.jl they came from.
+```
 MATPOWER
@@ -25,7 +33,7 @@ Because most MATPOWER test cases were originally designed for power flow studies
 * **Contingencies** were set to include all N-1 transmission line contingencies that do not generate islands or isolated buses. More specifically, there is one contingency for each transmission line, as long as that transmission line is not a bridge in the network graph.
-For each MATPOWER test case, UC.jl provides 365 variations (`2017-01-01` to `2017-12-31`) corresponding different days of the year.
+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.
 ### MATPOWER/UW-PSTCA
@@ -33,11 +41,11 @@ A variety of smaller IEEE test cases, [compiled by University of Washington](htt
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case14/2017-01-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
+| `matpower/case14/2017-02-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
-| `matpower/case30/2017-01-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
+| `matpower/case30/2017-02-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
-| `matpower/case57/2017-01-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
+| `matpower/case57/2017-02-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
-| `matpower/case118/2017-01-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
+| `matpower/case118/2017-02-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
-| `matpower/case300/2017-01-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
+| `matpower/case300/2017-02-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
 ### MATPOWER/Polish
@@ -46,14 +54,14 @@ Test cases based on the Polish 400, 220 and 110 kV networks, originally provided
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case2383wp/2017-01-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
+| `matpower/case2383wp/2017-02-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
-| `matpower/case2736sp/2017-01-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
+| `matpower/case2736sp/2017-02-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
-| `matpower/case2737sop/2017-01-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
+| `matpower/case2737sop/2017-02-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
-| `matpower/case2746wop/2017-01-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
+| `matpower/case2746wop/2017-02-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
-| `matpower/case2746wp/2017-01-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
+| `matpower/case2746wp/2017-02-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
-| `matpower/case3012wp/2017-01-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
+| `matpower/case3012wp/2017-02-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
-| `matpower/case3120sp/2017-01-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
+| `matpower/case3120sp/2017-02-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
-| `matpower/case3375wp/2017-01-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
+| `matpower/case3375wp/2017-02-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
 ### MATPOWER/PEGASE
@@ -61,11 +69,11 @@ Test cases from the [Pan European Grid Advanced Simulation and State Estimation
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case89pegase/2017-01-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case89pegase/2017-02-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case1354pegase/2017-01-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case1354pegase/2017-02-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case2869pegase/2017-01-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case2869pegase/2017-02-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case9241pegase/2017-01-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case9241pegase/2017-02-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case13659pegase/2017-01-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case13659pegase/2017-02-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
 ### MATPOWER/RTE
@@ -73,14 +81,14 @@ Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case1888rte/2017-01-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
+| `matpower/case1888rte/2017-02-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
-| `matpower/case1951rte/2017-01-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
+| `matpower/case1951rte/2017-02-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
-| `matpower/case2848rte/2017-01-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
+| `matpower/case2848rte/2017-02-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
-| `matpower/case2868rte/2017-01-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
+| `matpower/case2868rte/2017-02-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
-| `matpower/case6468rte/2017-01-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
+| `matpower/case6468rte/2017-02-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
-| `matpower/case6470rte/2017-01-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
+| `matpower/case6470rte/2017-02-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
-| `matpower/case6495rte/2017-01-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
+| `matpower/case6495rte/2017-02-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
-| `matpower/case6515rte/2017-01-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
+| `matpower/case6515rte/2017-02-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
 PGLIB-UC Instances
@@ -280,7 +288,7 @@ Tejada19
 References
 ----------
-* [UCJL] **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Feng Qiu.** "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment (Version 0.3)". Zenodo (2022). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874)
+* [UCJL] **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu.** "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874)
 * [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)
@@ -288,9 +296,14 @@ References
 * [BaBlEh19] **Clayton Barrows, Aaron Bloom, Ali Ehlen, Jussi Ikaheimo, Jennie Jorgenson, Dheepak Krishnamurthy, Jessica Lau et al.** "The IEEE Reliability Test System: A Proposed 2019 Update." IEEE Transactions on Power Systems (2019). [DOI: 10.1109/TPWRS.2019.2925557](https://doi.org/10.1109/TPWRS.2019.2925557)
-* [JoFlMa16] **C. Josz, S. Fliscounakis, J. Maeght, and P. Panciatici.** "AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE". [ArXiv (2016)](https://arxiv.org/abs/1603.01533).
+* [JoFlMa16] **C. Josz, S. Fliscounakis, J. Maeght, and P. Panciatici.** "AC Power Flow
 Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE". [ArXiv (2016)](https://arxiv.org/abs/1603.01533).
-* [FlPaCa13] **S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel.** "Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive and corrective actions", Power Systems, IEEE Trans. on, (28)4:4909-4917, 2013. [DOI: 10.1109/TPWRS.2013.2251015](https://doi.org/10.1109/TPWRS.2013.2251015)
+* [FlPaCa13] **S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel.**
 "Contingency ranking with respect to overloads in very large power
 systems taking into account uncertainty, preventive and corrective
 actions", Power Systems, IEEE Trans. on, (28)4:4909-4917, 2013.
 [DOI: 10.1109/TPWRS.2013.2251015](https://doi.org/10.1109/TPWRS.2013.2251015)
 * [MTPWR] **D. Zimmerman, C. E. Murillo-Sandnchez and R. J. Thomas.** "Matpower:  Steady-state  operations,  planning,  and  analysis  tools  forpower systems research and education", IEEE Transactions on PowerSystems, vol. 26, no. 1, pp. 12 –19, Feb. 2011. [DOI: 10.1109/TPWRS.2010.2051168](https://doi.org/10.1109/TPWRS.2010.2051168)
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -1,16 +0,0 @@
 using Documenter, UnitCommitment, JuMP
 makedocs(
    sitename="UnitCommitment.jl",
    pages=[
        "Home" => "index.md",
        "usage.md",
        "format.md",
        "instances.md",
        "model.md",
        "api.md",
    ],
    format = Documenter.HTML(
        assets=["assets/custom.css"],
    )
 )
--- a/docs/src/model.md
+++ b/docs/src/model.md
@@ -1,3 +1,11 @@
 ```{sectnum}
 ---
 start: 4
 depth: 2
 suffix: .
 ---
 ```
 JuMP Model
 ==========
@@ -8,30 +16,21 @@ Decision variables
 ### Generators
 #### Thermal Units
 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[r,g,t]` | $r_g(t)$ | Amount of reserve `r` provided by unit `g` at time `t`. | 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
 #### Profiled Units
 Name | Symbol | Description | Unit
 :-----|:------:|:-------------|:------:
 `prod_profiled[s,t]` | $p^{\dagger}_{g}(t)$ | Amount of power produced by profiled unit `g` at time `t`. | MW
 ### 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
@@ -39,24 +38,69 @@ Name | Symbol | Description | Unit
 ### 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
+```{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.
+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
 ------------------
-TODO
+$$
 \begin{align}
    \text{minimize} \;\; &
        \sum_{t \in \mathcal{T}}
          \sum_{g \in \mathcal{G}}
          C^\text{min}_g(t) u_g(t) \\
    &
        + \sum_{t \in \mathcal{T}}
          \sum_{g \in \mathcal{G}}
          \sum_{g \in \mathcal{K}_g}
          C^k_g(t) p^k_g(t) \\
    &
        + \sum_{t \in \mathcal{T}}
          \sum_{g \in \mathcal{G}}
          \sum_{s \in \mathcal{S}_g}
          C^s_{g}(t) \delta^s_g(t) \\
    &
        + \sum_{t \in \mathcal{T}}
          \sum_{l \in \mathcal{L}}
          C^\text{overflow}_{l}(t) f^+_l(t) \\
    &
        + \sum_{t \in \mathcal{T}}
           \sum_{b \in \mathcal{B}}
           C^\text{curtail}(t) s^+_b(t) \\
    &
        - \sum_{t \in \mathcal{T}}
           \sum_{s \in \mathcal{PS}}
           R_{s}(t) d_{s}(t) \\
 \end{align}
 $$
 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
 -----------
--- a/docs/src/api.md
+++ b/docs/src/api.md
@@ -1,62 +0,0 @@
 # API Reference
 ## Read data, build model & optimize
 ```@docs
 UnitCommitment.read
 UnitCommitment.read_benchmark
 UnitCommitment.build_model
 UnitCommitment.optimize!
 UnitCommitment.solution
 UnitCommitment.validate
 UnitCommitment.write
 ```
 ## Locational Marginal Prices
 ### Conventional LMPs
 ```@docs
 UnitCommitment.compute_lmp(::JuMP.Model,::UnitCommitment.ConventionalLMP)
 ```
 ### Approximated Extended LMPs
 ```@docs
 UnitCommitment.AELMP
 UnitCommitment.compute_lmp(::JuMP.Model,::UnitCommitment.AELMP)
 ```
 ## Modify instance
 ```@docs
 UnitCommitment.slice
 UnitCommitment.randomize!(::UnitCommitment.UnitCommitmentInstance)
 UnitCommitment.generate_initial_conditions!
 ```
 ## Formulations
 ```@docs
 UnitCommitment.Formulation
 UnitCommitment.ShiftFactorsFormulation
 UnitCommitment.ArrCon2000
 UnitCommitment.CarArr2006
 UnitCommitment.DamKucRajAta2016
 UnitCommitment.Gar1962
 UnitCommitment.KnuOstWat2018
 UnitCommitment.MorLatRam2013
 UnitCommitment.PanGua2016
 UnitCommitment.WanHob2016
 ```
 ## Solution Methods
 ```@docs
 UnitCommitment.XavQiuWanThi2019.Method
 ```
 ## Randomization Methods
 ```@docs
 UnitCommitment.XavQiuAhm2021.Randomization
 ```
--- a/docs/src/assets/custom.css
+++ b/docs/src/assets/custom.css
@@ -1,36 +0,0 @@
@media screen and (min-width: 1056px) {
    #documenter .docs-main {
      max-width: 65rem !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;
 }
 code {
    background-color: transparent;
    color: rgb(232, 62, 140);
 }
--- a/docs/src/usage.md
+++ b/docs/src/usage.md
@@ -1,226 +0,0 @@
 Usage
 =====
 Installation
 ------------
 UnitCommitment.jl was tested and developed with [Julia 1.7](https://julialang.org/). To install Julia, please follow the [installation guide on the official Julia website](https://julialang.org/downloads/). To install UnitCommitment.jl, run the Julia interpreter, type `]` to open the package manager, then type:
 ```text
 pkg> add UnitCommitment@0.3
 ```
 To test that the package has been correctly installed, run:
 ```text
 pkg> test UnitCommitment
 ```
 If all tests pass, the package should now be ready to be used by any Julia script on the machine.
 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.
 Typical Usage
 -------------
 ### Solving user-provided instances
 The first step to use UC.jl is to construct a JSON file describing your unit commitment instance. See [Data Format](format.md) for a complete description of the data format UC.jl expects. The next steps, as shown below, are to: (1) read the instance from file; (2) construct the optimization model; (3) run the optimization; and (4) extract the optimal solution.
 ```julia
 using Cbc
 using JSON
 using UnitCommitment
 # 1. Read instance
 instance = UnitCommitment.read("/path/to/input.json")
 # 2. Construct optimization model
 model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
 )
 # 3. Solve model
 UnitCommitment.optimize!(model)
 # 4. Write solution to a file
 solution = UnitCommitment.solution(model)
 UnitCommitment.write("/path/to/output.json", solution)
 ```
 ### Solving benchmark instances
 UnitCommitment.jl contains a large number of benchmark instances collected from the literature and converted into a common data format. To solve one of these instances individually, instead of constructing your own, the function `read_benchmark` can be used, as shown below. See [Instances](instances.md) for the complete list of available instances.
 ```julia
 using UnitCommitment
 instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
 ```
 ## Customizing the formulation
 By default, `build_model` uses a formulation that combines modeling components from different publications, and that has been carefully tested, using our own benchmark scripts, to provide good performance across a wide variety of instances. This default formulation is expected to change over time, as new methods are proposed in the literature. You can, however, construct your own formulation, based on the modeling components that you choose, as shown in the next example.
 ```julia
 using Cbc
 using UnitCommitment
 import UnitCommitment:
    Formulation,
    KnuOstWat2018,
    MorLatRam2013,
    ShiftFactorsFormulation
 instance = UnitCommitment.read_benchmark(
    "matpower/case118/2017-02-01",
 )
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = Cbc.Optimizer,
    formulation = Formulation(
        pwl_costs = KnuOstWat2018.PwlCosts(),
        ramping = MorLatRam2013.Ramping(),
        startup_costs = MorLatRam2013.StartupCosts(),
        transmission = ShiftFactorsFormulation(
            isf_cutoff = 0.005,
            lodf_cutoff = 0.001,
        ),
    ),
 )
 ```
 ## Generating initial conditions
 When creating random unit commitment instances for benchmark purposes, it is often hard to compute, in advance, sensible initial conditions for all generators. Setting initial conditions naively (for example, making all generators initially off and producing no power) can easily cause the instance to become infeasible due to excessive ramping. Initial conditions can also make it hard to modify existing instances. For example, increasing the system load without carefully modifying the initial conditions may make the problem infeasible or unrealistically challenging to solve.
 To help with this issue, UC.jl provides a utility function which can generate feasible initial conditions by solving a single-period optimization problem, as shown below:
 ```julia
 using Cbc
 using UnitCommitment
 # Read original instance
 instance = UnitCommitment.read("instance.json")
 # Generate initial conditions (in-place)
 UnitCommitment.generate_initial_conditions!(instance, Cbc.Optimizer)
 # Construct and solve optimization model
 model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
 )
 UnitCommitment.optimize!(model)
 ```
 !!! warning
    The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
 ## 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).
 ```julia
 using JSON
 using UnitCommitment
 # Read instance
 instance = UnitCommitment.read("instance.json")
 # Read solution (potentially produced by other packages) 
 solution = JSON.parsefile("solution.json")
 # Validate solution and print validation errors
 UnitCommitment.validate(instance, solution)
 ```
 ## Computing Locational Marginal Prices
 Locational marginal prices (LMPs) refer to the cost of supplying electricity at a particular location of the network. Multiple methods for computing LMPs have been proposed in the literature. UnitCommitment.jl implements two commonly-used methods: conventional LMPs and Approximated Extended LMPs (AELMPs). To compute LMPs for a given unit commitment instance, the `compute_lmp` function can be used, as shown in the examples below. The function accepts three arguments -- a solved SCUC model, an LMP method, and a linear optimizer -- and it returns a dictionary mapping `(bus_name, time)` to the marginal price.
 !!! warning
    Most mixed-integer linear optimizers, such as `HiGHS`, `Gurobi` and `CPLEX` can be used with `compute_lmp`, with the notable exception of `Cbc`, which does not support dual value evaluations. If using `Cbc`, please provide `Clp` as the linear optimizer.
 ### Conventional LMPs
 LMPs are conventionally computed by: (1) solving the SCUC model, (2) fixing all binary variables to their optimal values, and (3) re-solving the resulting linear programming model. In this approach, the LMPs are defined as the dual variables' values associated with the net injection constraints. The example below shows how to compute conventional LMPs for a given unit commitment instance. First, we build and optimize the SCUC model. Then, we call the `compute_lmp` function, providing as the second argument `ConventionalLMP()`.
 ```julia
 using UnitCommitment
 using HiGHS
 import UnitCommitment: ConventionalLMP
 # Read benchmark instance
 instance = UnitCommitment.read_benchmark("matpower/case118/2018-01-01")
 # Build the model
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = HiGHS.Optimizer,
 )
 # Optimize the model
 UnitCommitment.optimize!(model)
 # Compute the LMPs using the conventional method
 lmp = UnitCommitment.compute_lmp(
    model,
    ConventionalLMP(),
    optimizer = HiGHS.Optimizer,
 )
 # Access the LMPs
 # Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
@show lmp["s1","b1", 1]
 ```
 ### Approximate Extended LMPs
 Approximate Extended LMPs (AELMPs) are an alternative method to calculate locational marginal prices which attemps to minimize uplift payments. The method internally works by modifying the instance data in three ways: (1) it sets the minimum power output of each generator to zero, (2) it averages the start-up cost over the offer blocks for each generator, and (3) it relaxes all integrality constraints. To compute AELMPs, as shown in the example below, we call `compute_lmp` and provide `AELMP()` as the second argument.
 This method has two configurable parameters: `allow_offline_participation` and `consider_startup_costs`. If `allow_offline_participation = true`, then offline generators are allowed to participate in the pricing. If instead `allow_offline_participation = false`, offline generators are not allowed and therefore are excluded from the system. A solved UC model is optional if offline participation is allowed, but is required if not allowed. The method forces offline participation to be allowed if the UC model supplied by the user is not solved. For the second field, If `consider_startup_costs = true`, then start-up costs are integrated and averaged over each unit production; otherwise the production costs stay the same. By default, both fields are set to `true`.
 !!! warning
    This approximation method is still under active research, and has several limitations. The implementation provided in the package is based on MISO Phase I only. It only supports fast start resources. More specifically, the minimum up/down time of all generators must be 1, the initial power of all generators must be 0, and the initial status of all generators must be negative. The method does not support time-varying start-up costs. The method does not support multiple scenarios. If offline participation is not allowed, AELMPs treats an asset to be  offline if it is never on throughout all time periods. 
 ```julia
 using UnitCommitment
 using HiGHS
 import UnitCommitment: AELMP
 # Read benchmark instance
 instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
 # Build the model
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = HiGHS.Optimizer,
 )
 # Optimize the model
 UnitCommitment.optimize!(model)
 # Compute the AELMPs
 aelmp = UnitCommitment.compute_lmp(
    model,
    AELMP(
        allow_offline_participation = false,
        consider_startup_costs = true
    ),
    optimizer = HiGHS.Optimizer
 )
 # Access the AELMPs
 # Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
 # Note: although scenario is supported, the query still keeps the scenario keys for consistency.
@show aelmp["s1", "b1", 1]
 ```
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -0,0 +1,149 @@
 ```{sectnum}
 ---
 start: 1
 depth: 2
 suffix: .
 ---
 ```
 Usage
 =====
 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@0.2
 ```
 To test that the package has been correctly installed, run:
 ```text
 pkg> test UnitCommitment
 ```
 If all tests pass, the package should now be ready to be used by any Julia script on the machine.
 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.
 Typical Usage
 -------------
 ### Solving user-provided instances
 The first step to use UC.jl is to construct a JSON file describing your unit commitment instance. See [Data Format](format.md) for a complete description of the data format UC.jl expects. The next steps, as shown below, are to: (1) read the instance from file; (2) construct the optimization model; (3) run the optimization; and (4) extract the optimal solution.
 ```julia
 using Cbc
 using JSON
 using UnitCommitment
 # 1. Read instance
 instance = UnitCommitment.read("/path/to/input.json")
 # 2. Construct optimization model
 model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
 )
 # 3. Solve model
 UnitCommitment.optimize!(model)
 # 4. Write solution to a file
 solution = UnitCommitment.solution(model)
 UnitCommitment.write("/path/to/output.json", solution)
 ```
 ### Solving benchmark instances
 UnitCommitment.jl contains a large number of benchmark instances collected from the literature and converted into a common data format. To solve one of these instances individually, instead of constructing your own, the function `read_benchmark` can be used, as shown below. See [Instances](instances.md) for the complete list of available instances.
 ```julia
 using UnitCommitment
 instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
 ```
 Advanced usage
 --------------
 ### Customizing the formulation
 By default, `build_model` uses a formulation that combines modeling components from different publications, and that has been carefully tested, using our own benchmark scripts, to provide good performance across a wide variety of instances. This default formulation is expected to change over time, as new methods are proposed in the literature. You can, however, construct your own formulation, based on the modeling components that you choose, as shown in the next example.
 ```julia
 using Cbc
 using UnitCommitment
 import UnitCommitment:
    Formulation,
    KnuOstWat2018,
    MorLatRam2013,
    ShiftFactorsFormulation
 instance = UnitCommitment.read_benchmark(
    "matpower/case118/2017-02-01",
 )
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = Cbc.Optimizer,
    formulation = Formulation(
        pwl_costs = KnuOstWat2018.PwlCosts(),
        ramping = MorLatRam2013.Ramping(),
        startup_costs = MorLatRam2013.StartupCosts(),
        transmission = ShiftFactorsFormulation(
            isf_cutoff = 0.005,
            lodf_cutoff = 0.001,
        ),
    ),
 )
 ```
 ### Generating initial conditions
 When creating random unit commitment instances for benchmark purposes, it is often hard to compute, in advance, sensible initial conditions for all generators. Setting initial conditions naively (for example, making all generators initially off and producing no power) can easily cause the instance to become infeasible due to excessive ramping. Initial conditions can also make it hard to modify existing instances. For example, increasing the system load without carefully modifying the initial conditions may make the problem infeasible or unrealistically challenging to solve.
 To help with this issue, UC.jl provides a utility function which can generate feasible initial conditions by solving a single-period optimization problem, as shown below:
 ```julia
 using Cbc
 using UnitCommitment
 # Read original instance
 instance = UnitCommitment.read("instance.json")
 # Generate initial conditions (in-place)
 UnitCommitment.generate_initial_conditions!(instance, Cbc.Optimizer)
 # Construct and solve optimization model
 model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
 )
 UnitCommitment.optimize!(model)
 ```
 ```{warning}
 The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
 ```
 ### 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).
 ```julia
 using JSON
 using UnitCommitment
 # Read instance
 instance = UnitCommitment.read("instance.json")
 # Read solution (potentially produced by other packages) 
 solution = JSON.parsefile("solution.json")
 # Validate solution and print validation errors
 UnitCommitment.validate(instance, solution)
 ```
--- a/68
+++ b/68
@@ -0,0 +1,68 @@
 #!/bin/bash
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 if [ ! -e Project.toml ]; then
    echo "juliaw: Project.toml not found"
    exit 1
 fi
 if [ ! -e Manifest.toml ]; then
    julia --project=. -e 'using Pkg; Pkg.instantiate()' || exit 1
 fi
 if [ ! -e build/sysimage.so -o Project.toml -nt build/sysimage.so ]; then
    echo "juliaw: rebuilding system image..."
    # Generate temporary project folder
    rm -rf $HOME/.juliaw
    mkdir -p $HOME/.juliaw/src
    cp Project.toml Manifest.toml $HOME/.juliaw
    NAME=$(julia -e 'using TOML; toml = TOML.parsefile("Project.toml"); "name" in keys(toml) && print(toml["name"])')
    if [ ! -z $NAME ]; then
        cat > $HOME/.juliaw/src/$NAME.jl << EOF
 module $NAME
 end
 EOF
    fi
    # Add PackageCompiler dependencies to temporary project
    julia --project=$HOME/.juliaw -e 'using Pkg; Pkg.add(["PackageCompiler", "TOML", "Logging"])'
    # Generate system image scripts
    cat > $HOME/.juliaw/sysimage.jl << EOF
 using PackageCompiler
 using TOML
 using Logging
 Logging.disable_logging(Logging.Info)
 mkpath("$PWD/build")
 println("juliaw: generating precompilation statements...")
 run(\`julia --project="$PWD" --trace-compile="$PWD"/build/precompile.jl \$(ARGS)\`)
 println("juliaw: finding dependencies...")
 project = TOML.parsefile("Project.toml")
 manifest = TOML.parsefile("Manifest.toml")
 deps = Symbol[]
 for dep in keys(project["deps"])
    if "path" in keys(manifest[dep][1])
        println("  - \$(dep) [skip]")
    else
        println("  - \$(dep)")
        push!(deps, Symbol(dep))
    end
 end
 println("juliaw: building system image...")
 create_sysimage(
    deps,
    precompile_statements_file = "$PWD/build/precompile.jl",
    sysimage_path = "$PWD/build/sysimage.so",
 )
 EOF
    julia --project=$HOME/.juliaw $HOME/.juliaw/sysimage.jl $* 
 else
    julia --project=. --sysimage build/sysimage.so $*
 fi
--- a/src/UnitCommitment.jl
+++ b/src/UnitCommitment.jl
@@ -4,12 +4,9 @@
 module UnitCommitment
 using Base: String
 include("instance/structs.jl")
 include("model/formulations/base/structs.jl")
 include("solution/structs.jl")
 include("lmp/structs.jl")
 include("model/formulations/ArrCon2000/structs.jl")
 include("model/formulations/CarArr2006/structs.jl")
@@ -19,11 +16,9 @@ include("model/formulations/KnuOstWat2018/structs.jl")
 include("model/formulations/MorLatRam2013/structs.jl")
 include("model/formulations/PanGua2016/structs.jl")
 include("solution/methods/XavQiuWanThi2019/structs.jl")
 include("model/formulations/WanHob2016/structs.jl")
 include("import/egret.jl")
 include("instance/read.jl")
 include("instance/migrate.jl")
 include("model/build.jl")
 include("model/formulations/ArrCon2000/ramp.jl")
 include("model/formulations/base/bus.jl")
@@ -32,7 +27,6 @@ include("model/formulations/base/psload.jl")
 include("model/formulations/base/sensitivity.jl")
 include("model/formulations/base/system.jl")
 include("model/formulations/base/unit.jl")
 include("model/formulations/base/punit.jl")
 include("model/formulations/CarArr2006/pwlcosts.jl")
 include("model/formulations/DamKucRajAta2016/ramp.jl")
 include("model/formulations/Gar1962/pwlcosts.jl")
@@ -42,7 +36,6 @@ include("model/formulations/KnuOstWat2018/pwlcosts.jl")
 include("model/formulations/MorLatRam2013/ramp.jl")
 include("model/formulations/MorLatRam2013/scosts.jl")
 include("model/formulations/PanGua2016/ramp.jl")
 include("model/formulations/WanHob2016/ramp.jl")
 include("model/jumpext.jl")
 include("solution/fix.jl")
 include("solution/methods/XavQiuWanThi2019/enforce.jl")
@@ -60,7 +53,5 @@ include("utils/log.jl")
 include("utils/benchmark.jl")
 include("validation/repair.jl")
 include("validation/validate.jl")
 include("lmp/conventional.jl")
 include("lmp/aelmp.jl")
 end
--- a/src/import/egret.jl
+++ b/src/import/egret.jl
@@ -18,9 +18,9 @@ function read_egret_solution(path::String)::OrderedDict
    solution = OrderedDict()
    is_on = solution["Is on"] = OrderedDict()
-    production = solution["Thermal production (MW)"] = OrderedDict()
+    production = solution["Production (MW)"] = OrderedDict()
    reserve = solution["Reserve (MW)"] = OrderedDict()
-    production_cost = solution["Thermal production cost (\$)"] = OrderedDict()
+    production_cost = solution["Production cost (\$)"] = OrderedDict()
    startup_cost = solution["Startup cost (\$)"] = OrderedDict()
    for (gen_name, gen_dict) in egret["elements"]["generator"]
--- a/src/instance/migrate.jl
+++ b/src/instance/migrate.jl
@@ -1,50 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataStructures
 using JSON
 function _migrate(json)
    version = json["Parameters"]["Version"]
    if version === nothing
        error(
            "The provided input file cannot be loaded because it does not " *
            "specify what version of UnitCommitment.jl it was written for. " *
            "Please modify the \"Parameters\" section of the file and include " *
            "a \"Version\" entry. For example: {\"Parameters\":{\"Version\":\"0.3\"}}",
        )
    end
    version = VersionNumber(version)
    version >= v"0.3" || _migrate_to_v03(json)
    version >= v"0.4" || _migrate_to_v04(json)
    return
 end
 function _migrate_to_v03(json)
    # Migrate reserves
    if json["Reserves"] !== nothing &&
       json["Reserves"]["Spinning (MW)"] !== nothing
        amount = json["Reserves"]["Spinning (MW)"]
        json["Reserves"] = DefaultOrderedDict(nothing)
        json["Reserves"]["r1"] = DefaultOrderedDict(nothing)
        json["Reserves"]["r1"]["Type"] = "spinning"
        json["Reserves"]["r1"]["Amount (MW)"] = amount
        for (gen_name, gen) in json["Generators"]
            if gen["Provides spinning reserves?"] == true
                gen["Reserve eligibility"] = ["r1"]
            end
        end
    end
 end
 function _migrate_to_v04(json)
    # Migrate thermal units
    if json["Generators"] !== nothing
        for (gen_name, gen) in json["Generators"]
            if gen["Type"] === nothing
                gen["Type"] = "Thermal"
            end
        end
    end
 end
--- a/src/instance/read.jl
+++ b/src/instance/read.jl
@@ -8,18 +8,20 @@ using DataStructures
 using GZip
 import Base: getindex, time
-const INSTANCES_URL = "https://axavier.org/UnitCommitment.jl/0.3/instances"
+const INSTANCES_URL = "https://axavier.org/UnitCommitment.jl/0.2/instances"
 """
    read_benchmark(name::AbstractString)::UnitCommitmentInstance
-Read one of the benchmark instances included in the package. See
+Read one of the benchmark unit commitment instances included in the package.
-[Instances](instances.md) for the entire list of benchmark instances available.
+See "Instances" section of the documentation for the entire list of benchmark
 instances available.
-# Example
+Example
-```julia
+-------
-instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
+
-```
+    import UnitCommitment
    instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
 """
 function read_benchmark(
    name::AbstractString;
@@ -43,76 +45,26 @@ function read_benchmark(
    return UnitCommitment.read(filename)
 end
 function _repair_scenario_names_and_probabilities!(
    scenarios::Vector{UnitCommitmentScenario},
    path::Vector{String},
 )::Nothing
    total_weight = sum([sc.probability for sc in scenarios])
    for (sc_path, sc) in zip(path, scenarios)
        sc.name !== "" ||
            (sc.name = first(split(last(split(sc_path, "/")), ".")))
        sc.probability = (sc.probability / total_weight)
    end
    return
 end
 """
    read(path::AbstractString)::UnitCommitmentInstance
-Read a deterministic test case from the given file. The file may be gzipped.
+Read a unit commitment instance from a file. The file may be gzipped.
-# Example
+Example
 -------
-```julia
+    import UnitCommitment
-instance = UnitCommitment.read("s1.json.gz")
+    instance = UnitCommitment.read("/path/to/input.json.gz")
 ```
 """
-function read(path::String)::UnitCommitmentInstance
+function read(path::AbstractString)::UnitCommitmentInstance
    scenarios = Vector{UnitCommitmentScenario}()
    scenario = _read_scenario(path)
    scenario.name = "s1"
    scenario.probability = 1.0
    scenarios = [scenario]
    instance =
        UnitCommitmentInstance(time = scenario.time, scenarios = scenarios)
    return instance
 end
 """
    read(path::Vector{String})::UnitCommitmentInstance
 Read a stochastic unit commitment instance from the given files. Each file
 describes a scenario. The files may be gzipped.
 # Example
 ```julia
 instance = UnitCommitment.read(["s1.json.gz", "s2.json.gz"])
 ```
 """
 function read(paths::Vector{String})::UnitCommitmentInstance
    scenarios = UnitCommitmentScenario[]
    for p in paths
        push!(scenarios, _read_scenario(p))
    end
    _repair_scenario_names_and_probabilities!(scenarios, paths)
    instance =
        UnitCommitmentInstance(time = scenarios[1].time, scenarios = scenarios)
    return instance
 end
 function _read_scenario(path::String)::UnitCommitmentScenario
    if endswith(path, ".gz")
-        scenario = _read(gzopen(path))
+        return _read(gzopen(path))
    elseif endswith(path, ".json")
        scenario = _read(open(path))
    else
-        error("Unsupported input format")
+        return _read(open(path))
    end
    return scenario
 end
-function _read(file::IO)::UnitCommitmentScenario
+function _read(file::IO)::UnitCommitmentInstance
    return _from_json(
        JSON.parse(file, dicttype = () -> DefaultOrderedDict(nothing)),
    )
@@ -127,15 +79,12 @@ function _read_json(path::String)::OrderedDict
    return JSON.parse(file, dicttype = () -> DefaultOrderedDict(nothing))
 end
-function _from_json(json; repair = true)::UnitCommitmentScenario
+function _from_json(json; repair = true)
-    _migrate(json)
+    units = Unit[]
    thermal_units = ThermalUnit[]
    buses = Bus[]
    contingencies = Contingency[]
    lines = TransmissionLine[]
    loads = PriceSensitiveLoad[]
    reserves = Reserve[]
    profiled_units = ProfiledUnit[]
    function scalar(x; default = nothing)
        x !== nothing || return default
@@ -153,15 +102,9 @@ function _from_json(json; repair = true)::UnitCommitmentScenario
    time_multiplier = 60 ÷ time_step
    T = time_horizon * time_multiplier
    probability = json["Parameters"]["Scenario weight"]
    probability !== nothing || (probability = 1)
    scenario_name = json["Parameters"]["Scenario name"]
    scenario_name !== nothing || (scenario_name = "")
    name_to_bus = Dict{String,Bus}()
    name_to_line = Dict{String,TransmissionLine}()
-    name_to_unit = Dict{String,ThermalUnit}()
+    name_to_unit = Dict{String,Unit}()
    name_to_reserve = Dict{String,Reserve}()
    function timeseries(x; default = nothing)
        x !== nothing || return default
@@ -174,6 +117,10 @@ function _from_json(json; repair = true)::UnitCommitmentScenario
        json["Parameters"]["Power balance penalty (\$/MW)"],
        default = [1000.0 for t in 1:T],
    )
    shortfall_penalty = timeseries(
        json["Parameters"]["Reserve shortfall penalty (\$/MW)"],
        default = [-1.0 for t in 1:T],
    )
    # Read buses
    for (bus_name, dict) in json["Buses"]
@@ -181,155 +128,99 @@ function _from_json(json; repair = true)::UnitCommitmentScenario
            bus_name,
            length(buses),
            timeseries(dict["Load (MW)"]),
-            ThermalUnit[],
+            Unit[],
            PriceSensitiveLoad[],
            ProfiledUnit[],
        )
        name_to_bus[bus_name] = bus
        push!(buses, bus)
    end
    # Read reserves
    if "Reserves" in keys(json)
        for (reserve_name, dict) in json["Reserves"]
            r = Reserve(
                name = reserve_name,
                type = lowercase(dict["Type"]),
                amount = timeseries(dict["Amount (MW)"]),
                thermal_units = [],
                shortfall_penalty = scalar(
                    dict["Shortfall penalty (\$/MW)"],
                    default = -1,
                ),
            )
            name_to_reserve[reserve_name] = r
            push!(reserves, r)
        end
    end
    # Read units
    for (unit_name, dict) in json["Generators"]
        # Read and validate unit type
        unit_type = scalar(dict["Type"], default = nothing)
        unit_type !== nothing || error("unit $unit_name has no type specified")
        bus = name_to_bus[dict["Bus"]]
-        if lowercase(unit_type) === "thermal"
+        # Read production cost curve
-            # Read production cost curve
+        K = length(dict["Production cost curve (MW)"])
-            K = length(dict["Production cost curve (MW)"])
+        curve_mw = hcat(
-            curve_mw = hcat(
+            [timeseries(dict["Production cost curve (MW)"][k]) for k in 1:K]...,
-                [
+        )
-                    timeseries(dict["Production cost curve (MW)"][k]) for
+        curve_cost = hcat(
-                    k in 1:K
+            [timeseries(dict["Production cost curve (\$)"][k]) for k in 1:K]...,
-                ]...,
+        )
-            )
+        min_power = curve_mw[:, 1]
-            curve_cost = hcat(
+        max_power = curve_mw[:, K]
-                [
+        min_power_cost = curve_cost[:, 1]
-                    timeseries(dict["Production cost curve (\$)"][k]) for
+        segments = CostSegment[]
-                    k in 1:K
+        for k in 2:K
-                ]...,
+            amount = curve_mw[:, k] - curve_mw[:, k-1]
-            )
+            cost = (curve_cost[:, k] - curve_cost[:, k-1]) ./ amount
-            min_power = curve_mw[:, 1]
+            replace!(cost, NaN => 0.0)
-            max_power = curve_mw[:, K]
+            push!(segments, CostSegment(amount, cost))
            min_power_cost = curve_cost[:, 1]
            segments = CostSegment[]
            for k in 2:K
                amount = curve_mw[:, k] - curve_mw[:, k-1]
                cost = (curve_cost[:, k] - curve_cost[:, k-1]) ./ amount
                replace!(cost, NaN => 0.0)
                push!(segments, CostSegment(amount, cost))
            end
            # Read startup costs
            startup_delays = scalar(dict["Startup delays (h)"], default = [1])
            startup_costs = scalar(dict["Startup costs (\$)"], default = [0.0])
            startup_categories = StartupCategory[]
            for k in 1:length(startup_delays)
                push!(
                    startup_categories,
                    StartupCategory(
                        startup_delays[k] .* time_multiplier,
                        startup_costs[k],
                    ),
                )
            end
            # Read reserve eligibility
            unit_reserves = Reserve[]
            if "Reserve eligibility" in keys(dict)
                unit_reserves =
                    [name_to_reserve[n] for n in dict["Reserve eligibility"]]
            end
            # Read and validate initial conditions
            initial_power =
                scalar(dict["Initial power (MW)"], default = nothing)
            initial_status =
                scalar(dict["Initial status (h)"], default = nothing)
            if initial_power === nothing
                initial_status === nothing || error(
                    "unit $unit_name has initial status but no initial power",
                )
            else
                initial_status !== nothing || error(
                    "unit $unit_name has initial power but no initial status",
                )
                initial_status != 0 ||
                    error("unit $unit_name has invalid initial status")
                if initial_status < 0 && initial_power > 1e-3
                    error("unit $unit_name has invalid initial power")
                end
                initial_status *= time_multiplier
            end
            # Read commitment status 
            commitment_status = scalar(
                dict["Commitment status"],
                default = Vector{Union{Bool,Nothing}}(nothing, T),
            )
            unit = ThermalUnit(
                unit_name,
                bus,
                max_power,
                min_power,
                timeseries(dict["Must run?"], default = [false for t in 1:T]),
                min_power_cost,
                segments,
                scalar(dict["Minimum uptime (h)"], default = 1) *
                time_multiplier,
                scalar(dict["Minimum downtime (h)"], default = 1) *
                time_multiplier,
                scalar(dict["Ramp up limit (MW)"], default = 1e6),
                scalar(dict["Ramp down limit (MW)"], default = 1e6),
                scalar(dict["Startup limit (MW)"], default = 1e6),
                scalar(dict["Shutdown limit (MW)"], default = 1e6),
                initial_status,
                initial_power,
                startup_categories,
                unit_reserves,
                commitment_status,
            )
            push!(bus.thermal_units, unit)
            for r in unit_reserves
                push!(r.thermal_units, unit)
            end
            name_to_unit[unit_name] = unit
            push!(thermal_units, unit)
        elseif lowercase(unit_type) === "profiled"
            bus = name_to_bus[dict["Bus"]]
            pu = ProfiledUnit(
                unit_name,
                bus,
                timeseries(scalar(dict["Minimum power (MW)"], default = 0.0)),
                timeseries(dict["Maximum power (MW)"]),
                timeseries(dict["Cost (\$/MW)"]),
            )
            push!(bus.profiled_units, pu)
            push!(profiled_units, pu)
        else
            error("unit $unit_name has an invalid type")
        end
        # Read startup costs
        startup_delays = scalar(dict["Startup delays (h)"], default = [1])
        startup_costs = scalar(dict["Startup costs (\$)"], default = [0.0])
        startup_categories = StartupCategory[]
        for k in 1:length(startup_delays)
            push!(
                startup_categories,
                StartupCategory(
                    startup_delays[k] .* time_multiplier,
                    startup_costs[k],
                ),
            )
        end
        # Read and validate initial conditions
        initial_power = scalar(dict["Initial power (MW)"], default = nothing)
        initial_status = scalar(dict["Initial status (h)"], default = nothing)
        if initial_power === nothing
            initial_status === nothing ||
                error("unit $unit_name has initial status but no initial power")
        else
            initial_status !== nothing ||
                error("unit $unit_name has initial power but no initial status")
            initial_status != 0 ||
                error("unit $unit_name has invalid initial status")
            if initial_status < 0 && initial_power > 1e-3
                error("unit $unit_name has invalid initial power")
            end
            initial_status *= time_multiplier
        end
        unit = Unit(
            unit_name,
            bus,
            max_power,
            min_power,
            timeseries(dict["Must run?"], default = [false for t in 1:T]),
            min_power_cost,
            segments,
            scalar(dict["Minimum uptime (h)"], default = 1) * time_multiplier,
            scalar(dict["Minimum downtime (h)"], default = 1) * time_multiplier,
            scalar(dict["Ramp up limit (MW)"], default = 1e6),
            scalar(dict["Ramp down limit (MW)"], default = 1e6),
            scalar(dict["Startup limit (MW)"], default = 1e6),
            scalar(dict["Shutdown limit (MW)"], default = 1e6),
            initial_status,
            initial_power,
            timeseries(
                dict["Provides spinning reserves?"],
                default = [true for t in 1:T],
            ),
            startup_categories,
        )
        push!(bus.units, unit)
        name_to_unit[unit_name] = unit
        push!(units, unit)
    end
    # Read reserves
    reserves = Reserves(zeros(T))
    if "Reserves" in keys(json)
        reserves.spinning =
            timeseries(json["Reserves"]["Spinning (MW)"], default = zeros(T))
    end
    # Read transmission lines
@@ -363,7 +254,7 @@ function _from_json(json; repair = true)::UnitCommitmentScenario
    # Read contingencies
    if "Contingencies" in keys(json)
        for (cont_name, dict) in json["Contingencies"]
-            affected_units = ThermalUnit[]
+            affected_units = Unit[]
            affected_lines = TransmissionLine[]
            if "Affected lines" in keys(dict)
                affected_lines =
@@ -393,9 +284,7 @@ function _from_json(json; repair = true)::UnitCommitmentScenario
        end
    end
-    scenario = UnitCommitmentScenario(
+    instance = UnitCommitmentInstance(
        name = scenario_name,
        probability = probability,
        buses_by_name = Dict(b.name => b for b in buses),
        buses = buses,
        contingencies_by_name = Dict(c.name => c for c in contingencies),
@@ -406,17 +295,13 @@ function _from_json(json; repair = true)::UnitCommitmentScenario
        price_sensitive_loads_by_name = Dict(ps.name => ps for ps in loads),
        price_sensitive_loads = loads,
        reserves = reserves,
-        reserves_by_name = name_to_reserve,
+        shortfall_penalty = shortfall_penalty,
        time = T,
-        thermal_units_by_name = Dict(g.name => g for g in thermal_units),
+        units_by_name = Dict(g.name => g for g in units),
-        thermal_units = thermal_units,
+        units = units,
        profiled_units_by_name = Dict(pu.name => pu for pu in profiled_units),
        profiled_units = profiled_units,
        isf = spzeros(Float64, length(lines), length(buses) - 1),
        lodf = spzeros(Float64, length(lines), length(lines)),
    )
    if repair
-        UnitCommitment.repair!(scenario)
+        UnitCommitment.repair!(instance)
    end
-    return scenario
+    return instance
 end
--- a/src/instance/structs.jl
+++ b/src/instance/structs.jl
@@ -6,9 +6,8 @@ mutable struct Bus
    name::String
    offset::Int
    load::Vector{Float64}
-    thermal_units::Vector
+    units::Vector
    price_sensitive_loads::Vector
    profiled_units::Vector
 end
 mutable struct CostSegment
@@ -21,15 +20,7 @@ mutable struct StartupCategory
    cost::Float64
 end
-Base.@kwdef mutable struct Reserve
+mutable struct Unit
    name::String
    type::String
    amount::Vector{Float64}
    thermal_units::Vector
    shortfall_penalty::Float64
 end
 mutable struct ThermalUnit
    name::String
    bus::Bus
    max_power::Vector{Float64}
@@ -45,9 +36,8 @@ mutable struct ThermalUnit
    shutdown_limit::Float64
    initial_status::Union{Int,Nothing}
    initial_power::Union{Float64,Nothing}
    provides_spinning_reserves::Vector{Bool}
    startup_categories::Vector{StartupCategory}
    reserves::Vector{Reserve}
    commitment_status::Vector{Union{Bool,Nothing}}
 end
 mutable struct TransmissionLine
@@ -62,10 +52,14 @@ mutable struct TransmissionLine
    flow_limit_penalty::Vector{Float64}
 end
 mutable struct Reserves
    spinning::Vector{Float64}
 end
 mutable struct Contingency
    name::String
    lines::Vector{TransmissionLine}
-    thermal_units::Vector{ThermalUnit}
+    units::Vector{Unit}
 end
 mutable struct PriceSensitiveLoad
@@ -75,52 +69,33 @@ mutable struct PriceSensitiveLoad
    revenue::Vector{Float64}
 end
-mutable struct ProfiledUnit
+Base.@kwdef mutable struct UnitCommitmentInstance
    name::String
    bus::Bus
    min_power::Vector{Float64}
    max_power::Vector{Float64}
    cost::Vector{Float64}
 end
 Base.@kwdef mutable struct UnitCommitmentScenario
    buses_by_name::Dict{AbstractString,Bus}
    buses::Vector{Bus}
    contingencies_by_name::Dict{AbstractString,Contingency}
    contingencies::Vector{Contingency}
    isf::Array{Float64,2}
    lines_by_name::Dict{AbstractString,TransmissionLine}
    lines::Vector{TransmissionLine}
    lodf::Array{Float64,2}
    name::String
    power_balance_penalty::Vector{Float64}
    price_sensitive_loads_by_name::Dict{AbstractString,PriceSensitiveLoad}
    price_sensitive_loads::Vector{PriceSensitiveLoad}
-    probability::Float64
+    reserves::Reserves
-    profiled_units_by_name::Dict{AbstractString,ProfiledUnit}
+    shortfall_penalty::Vector{Float64}
    profiled_units::Vector{ProfiledUnit}
    reserves_by_name::Dict{AbstractString,Reserve}
    reserves::Vector{Reserve}
    thermal_units_by_name::Dict{AbstractString,ThermalUnit}
    thermal_units::Vector{ThermalUnit}
    time::Int
-end
+    units_by_name::Dict{AbstractString,Unit}
-
+    units::Vector{Unit}
 Base.@kwdef mutable struct UnitCommitmentInstance
    time::Int
    scenarios::Vector{UnitCommitmentScenario}
 end
 function Base.show(io::IO, instance::UnitCommitmentInstance)
    sc = instance.scenarios[1]
    print(io, "UnitCommitmentInstance(")
-    print(io, "$(length(instance.scenarios)) scenarios, ")
+    print(io, "$(length(instance.units)) units, ")
-    print(io, "$(length(sc.thermal_units)) thermal units, ")
+    print(io, "$(length(instance.buses)) buses, ")
-    print(io, "$(length(sc.profiled_units)) profiled units, ")
+    print(io, "$(length(instance.lines)) lines, ")
-    print(io, "$(length(sc.buses)) buses, ")
+    print(io, "$(length(instance.contingencies)) contingencies, ")
-    print(io, "$(length(sc.lines)) lines, ")
+    print(
-    print(io, "$(length(sc.contingencies)) contingencies, ")
+        io,
-    print(io, "$(length(sc.price_sensitive_loads)) price sensitive loads, ")
+        "$(length(instance.price_sensitive_loads)) price sensitive loads, ",
    )
    print(io, "$(instance.time) time steps")
    print(io, ")")
    return
--- a/src/lmp/aelmp.jl
+++ b/src/lmp/aelmp.jl
@@ -1,212 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using JuMP
 """
    function compute_lmp(
        model::JuMP.Model,
        method::AELMP;
        optimizer,
    )::OrderedDict{Tuple{String,Int},Float64}
 Calculates the approximate extended locational marginal prices of the given unit commitment instance.
 The AELPM does the following three things:
    1. It sets the minimum power output of each generator to zero
    2. It averages the start-up cost over the offer blocks for each generator
    3. It relaxes all integrality constraints
 Returns a dictionary mapping `(bus_name, time)` to the marginal price.
 WARNING: This approximation method is not fully developed. The implementation is based on MISO Phase I only.
 1. It only supports Fast Start resources. More specifically, the minimum up/down time has to be zero.
 2. The method does NOT support time-varying start-up costs.
 3. An asset is considered offline if it is never on throughout all time periods. 
 4. The method does NOT support multiple scenarios.
 Arguments
 ---------
 - `model`:
    the UnitCommitment model, must be solved before calling this function if offline participation is not allowed.
 - `method`:
    the AELMP method.
 - `optimizer`:
    the optimizer for solving the LP problem.
 Examples
 --------
 ```julia
 using UnitCommitment
 using HiGHS
 import UnitCommitment: AELMP
 # Read benchmark instance
 instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
 # Build the model
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = HiGHS.Optimizer,
 )
 # Optimize the model
 UnitCommitment.optimize!(model)
 # Compute the AELMPs
 aelmp = UnitCommitment.compute_lmp(
    model,
    AELMP(
        allow_offline_participation = false,
        consider_startup_costs = true
    ),
    optimizer = HiGHS.Optimizer
 )
 # Access the AELMPs
 # Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
 # Note: although scenario is supported, the query still keeps the scenario keys for consistency.
@show aelmp["s1", "b1", 1]
 ```
 """
 function compute_lmp(
    model::JuMP.Model,
    method::AELMP;
    optimizer,
 )::OrderedDict{Tuple{String,String,Int},Float64}
    @info "Building the approximation model..."
    instance = deepcopy(model[:instance])
    _aelmp_check_parameters(instance, model, method)
    _modify_scenario!(instance.scenarios[1], model, method)
    # prepare the result dictionary and solve the model 
    elmp = OrderedDict()
    @info "Solving the approximation model."
    approx_model = build_model(instance = instance, variable_names = true)
    # relax the binary constraint, and relax integrality
    for v in all_variables(approx_model)
        if is_binary(v)
            unset_binary(v)
        end
    end
    relax_integrality(approx_model)
    set_optimizer(approx_model, optimizer)
    # solve the model 
    set_silent(approx_model)
    optimize!(approx_model)
    # access the dual values
    @info "Getting dual values (AELMPs)."
    for (key, val) in approx_model[:eq_net_injection]
        elmp[key] = dual(val)
    end
    return elmp
 end
 function _aelmp_check_parameters(
    instance::UnitCommitmentInstance,
    model::JuMP.Model,
    method::AELMP,
 )
    # CHECK: model cannot have multiple scenarios
    if length(instance.scenarios) > 1
        error("The method does NOT support multiple scenarios.")
    end
    sc = instance.scenarios[1]
    # CHECK: model must be solved if allow_offline_participation=false
    if !method.allow_offline_participation
        if isnothing(model) || !has_values(model)
            error(
                "A solved UC model is required if allow_offline_participation=false.",
            )
        end
    end
    all_units = sc.thermal_units
    # CHECK: model cannot handle non-fast-starts (MISO Phase I: can ONLY solve fast-starts)
    if any(u -> u.min_uptime > 1 || u.min_downtime > 1, all_units)
        error(
            "The minimum up/down time of all generators must be 1. AELMP only supports fast-starts.",
        )
    end
    if any(u -> u.initial_power > 0, all_units)
        error("The initial power of all generators must be 0.")
    end
    if any(u -> u.initial_status >= 0, all_units)
        error("The initial status of all generators must be negative.")
    end
    # CHECK: model does not support startup costs (in time series)
    if any(u -> length(u.startup_categories) > 1, all_units)
        error("The method does NOT support time-varying start-up costs.")
    end
 end
 function _modify_scenario!(
    sc::UnitCommitmentScenario,
    model::JuMP.Model,
    method::AELMP,
 )
    # this function modifies the sc units (generators)
    if !method.allow_offline_participation
        # 1. remove (if NOT allowing) the offline generators
        units_to_remove = []
        for unit in sc.thermal_units
            # remove based on the solved UC model result
            # remove the unit if it is never on
            if all(t -> value(model[:is_on][unit.name, t]) == 0, sc.time)
                # unregister from the bus 
                filter!(x -> x.name != unit.name, unit.bus.thermal_units)
                # unregister from the reserve
                for r in unit.reserves
                    filter!(x -> x.name != unit.name, r.thermal_units)
                end
                # append the name to the remove list
                push!(units_to_remove, unit.name)
            end
        end
        # unregister the units from the remove list
        filter!(x -> !(x.name in units_to_remove), sc.thermal_units)
    end
    for unit in sc.thermal_units
        # 2. set min generation requirement to 0 by adding 0 to production curve and cost 
        # min_power & min_costs are vectors with dimension T
        if unit.min_power[1] != 0
            first_cost_segment = unit.cost_segments[1]
            pushfirst!(
                unit.cost_segments,
                CostSegment(
                    ones(size(first_cost_segment.mw)) * unit.min_power[1],
                    ones(size(first_cost_segment.cost)) *
                    unit.min_power_cost[1] / unit.min_power[1],
                ),
            )
            unit.min_power = zeros(size(first_cost_segment.mw))
            unit.min_power_cost = zeros(size(first_cost_segment.cost))
        end
        # 3. average the start-up costs (if considering)
        # if consider_startup_costs = false, then use the current first_startup_cost
        first_startup_cost = unit.startup_categories[1].cost
        if method.consider_startup_costs
            additional_unit_cost = first_startup_cost / unit.max_power[1]
            for i in eachindex(unit.cost_segments)
                unit.cost_segments[i].cost .+= additional_unit_cost
            end
            first_startup_cost = 0.0 # zero out the start up cost
        end
        unit.startup_categories =
            StartupCategory[StartupCategory(0, first_startup_cost)]
    end
    return sc.thermal_units_by_name =
        Dict(g.name => g for g in sc.thermal_units)
 end
--- a/src/lmp/conventional.jl
+++ b/src/lmp/conventional.jl
@@ -1,92 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using JuMP
 """
    function compute_lmp(
        model::JuMP.Model,
        method::ConventionalLMP;
        optimizer,
    )::OrderedDict{Tuple{String,String,Int},Float64}
 Calculates conventional locational marginal prices of the given unit commitment
 instance. Returns a dictionary mapping `(bus_name, time)` to the marginal price.
 Arguments
 ---------
 - `model`:
    the UnitCommitment model, must be solved before calling this function.
 - `method`:
    the LMP method.
 - `optimizer`:
    the optimizer for solving the LP problem.
 Examples
 --------
 ```julia
 using UnitCommitment
 using HiGHS
 import UnitCommitment: ConventionalLMP
 # Read benchmark instance
 instance = UnitCommitment.read_benchmark("matpower/case118/2018-01-01")
 # Build the model
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = HiGHS.Optimizer,
 )
 # Optimize the model
 UnitCommitment.optimize!(model)
 # Compute the LMPs using the conventional method
 lmp = UnitCommitment.compute_lmp(
    model,
    ConventionalLMP(),
    optimizer = HiGHS.Optimizer,
 )
 # Access the LMPs
 # Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
@show lmp["s1", "b1", 1]
 ```
 """
 function compute_lmp(
    model::JuMP.Model,
    ::ConventionalLMP;
    optimizer,
 )::OrderedDict{Tuple{String,String,Int},Float64}
    if !has_values(model)
        error("The UC model must be solved before calculating the LMPs.")
    end
    lmp = OrderedDict()
    @info "Fixing binary variables and relaxing integrality..."
    vals = Dict(v => value(v) for v in all_variables(model))
    for v in all_variables(model)
        if is_binary(v)
            unset_binary(v)
            fix(v, vals[v])
        end
    end
    relax_integrality(model)
    set_optimizer(model, optimizer)
    @info "Solving the LP..."
    JuMP.optimize!(model)
    @info "Getting dual values (LMPs)..."
    for (key, val) in model[:eq_net_injection]
        lmp[key] = dual(val)
    end
    return lmp
 end
--- a/src/lmp/structs.jl
+++ b/src/lmp/structs.jl
@@ -1,28 +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.
 abstract type PricingMethod end
 struct ConventionalLMP <: PricingMethod end
 """
    struct AELMP <: PricingMethod 
        allow_offline_participation::Bool = true
        consider_startup_costs::Bool = true
    end
 Approximate Extended LMPs.
 Arguments
 ---------
 - `allow_offline_participation`:
    If true, offline assets are allowed to participate in pricing.
 - `consider_startup_costs`: 
    If true, the start-up costs are averaged over each unit production; otherwise the production costs stay the same.
 """
 Base.@kwdef struct AELMP <: PricingMethod
    allow_offline_participation::Bool = true
    consider_startup_costs::Bool = true
 end
--- a/src/model/build.jl
+++ b/src/model/build.jl
@@ -9,59 +9,22 @@ import JuMP: value, fix, set_name
    function build_model(;
        instance::UnitCommitmentInstance,
        optimizer = nothing,
        formulation = Formulation(),
        variable_names::Bool = false,
    )::JuMP.Model
 Build the JuMP model corresponding to the given unit commitment instance.
 Arguments
---------
+=========
 - `instance`:
    the instance.
 - `optimizer`:
    the optimizer factory that should be attached to this model (e.g. Cbc.Optimizer).
    If not provided, no optimizer will be attached.
 - `formulation`:
    the MIP formulation to use. By default, uses a formulation that combines
    modeling components from different publications that provides good
    performance across a wide variety of instances. An alternative formulation
    may also be provided.
 - `variable_names`: 
-    if true, set variable and constraint names. Important if the model is going
+    If true, set variable and constraint names. Important if the model is going
    to be exported to an MPS file. For large models, this can take significant
    time, so it's disabled by default.
 Examples
 --------
 ```julia
 # Read benchmark instance
 instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
 # Construct model (using state-of-the-art defaults)
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = Cbc.Optimizer,
 )
 # Construct model (using customized formulation)
 model = UnitCommitment.build_model(
    instance = instance,
    optimizer = Cbc.Optimizer,
    formulation = Formulation(
        pwl_costs = KnuOstWat2018.PwlCosts(),
        ramping = MorLatRam2013.Ramping(),
        startup_costs = MorLatRam2013.StartupCosts(),
        transmission = ShiftFactorsFormulation(
            isf_cutoff = 0.005,
            lodf_cutoff = 0.001,
        ),
    ),
 )
 ```
 """
 function build_model(;
    instance::UnitCommitmentInstance,
@@ -77,30 +40,20 @@ function build_model(;
        end
        model[:obj] = AffExpr()
        model[:instance] = instance
-        for g in instance.scenarios[1].thermal_units
+        _setup_transmission(model, formulation.transmission)
-            _add_unit_commitment!(model, g, formulation)
+        for l in instance.lines
            _add_transmission_line!(model, l, formulation.transmission)
        end
-        for sc in instance.scenarios
+        for b in instance.buses
-            @info "Building scenario $(sc.name) with " *
+            _add_bus!(model, b)
                  "probability $(sc.probability)"
            _setup_transmission(formulation.transmission, sc)
            for l in sc.lines
                _add_transmission_line!(model, l, formulation.transmission, sc)
            end
            for b in sc.buses
                _add_bus!(model, b, sc)
            end
            for ps in sc.price_sensitive_loads
                _add_price_sensitive_load!(model, ps, sc)
            end
            for g in sc.thermal_units
                _add_unit_dispatch!(model, g, formulation, sc)
            end
            for pu in sc.profiled_units
                _add_profiled_unit!(model, pu, sc)
            end
            _add_system_wide_eqs!(model, sc)
        end
        for g in instance.units
            _add_unit!(model, g, formulation)
        end
        for ps in instance.price_sensitive_loads
            _add_price_sensitive_load!(model, ps)
        end
        _add_system_wide_eqs!(model)
        @objective(model, Min, model[:obj])
    end
    @info @sprintf("Built model in %.2f seconds", time_model)
--- a/src/model/formulations/ArrCon2000/ramp.jl
+++ b/src/model/formulations/ArrCon2000/ramp.jl
@@ -4,11 +4,10 @@
 function _add_ramp_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_ramping::ArrCon2000.Ramping,
    formulation_status_vars::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    # TODO: Move upper case constants to model[:instance]
    RESERVES_WHEN_START_UP = true
@@ -20,10 +19,10 @@ function _add_ramp_eqs!(
    RD = g.ramp_down_limit
    SU = g.startup_limit
    SD = g.shutdown_limit
    reserve = model[:reserve]
    eq_ramp_down = _init(model, :eq_ramp_down)
    eq_ramp_up = _init(model, :eq_ramp_up)
    is_initially_on = (g.initial_status > 0)
    reserve = _total_reserves(model, g, sc)
    # Gar1962.ProdVars
    prod_above = model[:prod_above]
@@ -38,27 +37,27 @@ function _add_ramp_eqs!(
        if t == 1
            if is_initially_on
                # min power is _not_ multiplied by is_on because if !is_on, then ramp up is irrelevant
-                eq_ramp_up[sc.name, gn, t] = @constraint(
+                eq_ramp_up[gn, t] = @constraint(
                    model,
                    g.min_power[t] +
-                    prod_above[sc.name, gn, t] +
+                    prod_above[gn, t] +
-                    (RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0) <=
+                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) <=
                    g.initial_power + RU
                )
            end
        else
            max_prod_this_period =
                g.min_power[t] * is_on[gn, t] +
-                prod_above[sc.name, gn, t] +
+                prod_above[gn, t] +
                (
                    RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
-                    reserve[t] : 0.0
+                    reserve[gn, t] : 0.0
                )
            min_prod_last_period =
-                g.min_power[t-1] * is_on[gn, t-1] + prod_above[sc.name, gn, t-1]
+                g.min_power[t-1] * is_on[gn, t-1] + prod_above[gn, t-1]
            # Equation (24) in Kneuven et al. (2020)
-            eq_ramp_up[sc.name, gn, t] = @constraint(
+            eq_ramp_up[gn, t] = @constraint(
                model,
                max_prod_this_period - min_prod_last_period <=
                RU * is_on[gn, t-1] + SU * switch_on[gn, t]
@@ -72,25 +71,24 @@ function _add_ramp_eqs!(
                #      min_power + RD < initial_power < SD
                #      then the generator should be able to shut down at time t = 1,
                #      but the constraint below will force the unit to produce power
-                eq_ramp_down[sc.name, gn, t] = @constraint(
+                eq_ramp_down[gn, t] = @constraint(
                    model,
-                    g.initial_power -
+                    g.initial_power - (g.min_power[t] + prod_above[gn, t]) <= RD
                    (g.min_power[t] + prod_above[sc.name, gn, t]) <= RD
                )
            end
        else
            max_prod_last_period =
                g.min_power[t-1] * is_on[gn, t-1] +
-                prod_above[sc.name, gn, t-1] +
+                prod_above[gn, t-1] +
                (
                    RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN ?
-                    reserve[t-1] : 0.0
+                    reserve[gn, t-1] : 0.0
                )
            min_prod_this_period =
-                g.min_power[t] * is_on[gn, t] + prod_above[sc.name, gn, t]
+                g.min_power[t] * is_on[gn, t] + prod_above[gn, t]
            # Equation (25) in Kneuven et al. (2020)
-            eq_ramp_down[sc.name, gn, t] = @constraint(
+            eq_ramp_down[gn, t] = @constraint(
                model,
                max_prod_last_period - min_prod_this_period <=
                RD * is_on[gn, t] + SD * switch_off[gn, t]
--- a/src/model/formulations/CarArr2006/pwlcosts.jl
+++ b/src/model/formulations/CarArr2006/pwlcosts.jl
@@ -4,11 +4,10 @@
 function _add_production_piecewise_linear_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_pwl_costs::CarArr2006.PwlCosts,
    formulation_status_vars::StatusVarsFormulation,
    sc::UnitCommitmentScenario,
 )::Nothing
    eq_prod_above_def = _init(model, :eq_prod_above_def)
    eq_segprod_limit = _init(model, :eq_segprod_limit)
@@ -27,32 +26,28 @@ function _add_production_piecewise_linear_eqs!(
            #     difference between max power for segments k and k-1 so the
            #     value of cost_segments[k].mw[t] is the max production *for
            #     that segment*
-            eq_segprod_limit[sc.name, gn, t, k] = @constraint(
+            eq_segprod_limit[gn, t, k] = @constraint(
                model,
-                segprod[sc.name, gn, t, k] <= g.cost_segments[k].mw[t]
+                segprod[gn, t, k] <= g.cost_segments[k].mw[t]
            )
            # Also add this as an explicit upper bound on segprod to make the
            # solver's work a bit easier
-            set_upper_bound(
+            set_upper_bound(segprod[gn, t, k], g.cost_segments[k].mw[t])
                segprod[sc.name, gn, t, k],
                g.cost_segments[k].mw[t],
            )
            # Definition of production
            # Equation (43) in Kneuven et al. (2020)
-            eq_prod_above_def[sc.name, gn, t] = @constraint(
+            eq_prod_above_def[gn, t] = @constraint(
                model,
-                prod_above[sc.name, gn, t] ==
+                prod_above[gn, t] == sum(segprod[gn, t, k] for k in 1:K)
                sum(segprod[sc.name, gn, t, k] for k in 1:K)
            )
            # Objective function
            # Equation (44) in Kneuven et al. (2020)
            add_to_expression!(
                model[:obj],
-                segprod[sc.name, gn, t, k],
+                segprod[gn, t, k],
-                sc.probability * g.cost_segments[k].cost[t],
+                g.cost_segments[k].cost[t],
            )
        end
    end
--- a/src/model/formulations/DamKucRajAta2016/ramp.jl
+++ b/src/model/formulations/DamKucRajAta2016/ramp.jl
@@ -4,11 +4,10 @@
 function _add_ramp_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_ramping::DamKucRajAta2016.Ramping,
    formulation_status_vars::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    # TODO: Move upper case constants to model[:instance]
    RESERVES_WHEN_START_UP = true
@@ -24,7 +23,7 @@ function _add_ramp_eqs!(
    gn = g.name
    eq_str_ramp_down = _init(model, :eq_str_ramp_down)
    eq_str_ramp_up = _init(model, :eq_str_ramp_up)
-    reserve = _total_reserves(model, g, sc)
+    reserve = model[:reserve]
    # Gar1962.ProdVars
    prod_above = model[:prod_above]
@@ -49,15 +48,17 @@ function _add_ramp_eqs!(
        # end
        max_prod_this_period =
-            prod_above[sc.name, gn, t] +
+            prod_above[gn, t] + (
-            (RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0)
+                RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
                reserve[gn, t] : 0.0
            )
        min_prod_last_period = 0.0
        if t > 1 && time_invariant
-            min_prod_last_period = prod_above[sc.name, gn, t-1]
+            min_prod_last_period = prod_above[gn, t-1]
            # Equation (35) in Kneuven et al. (2020)
            # Sparser version of (24)
-            eq_str_ramp_up[sc.name, gn, t] = @constraint(
+            eq_str_ramp_up[gn, t] = @constraint(
                model,
                max_prod_this_period - min_prod_last_period <=
                (SU - g.min_power[t] - RU) * switch_on[gn, t] +
@@ -66,8 +67,7 @@ function _add_ramp_eqs!(
        elseif (t == 1 && is_initially_on) || (t > 1 && !time_invariant)
            if t > 1
                min_prod_last_period =
-                    prod_above[sc.name, gn, t-1] +
+                    prod_above[gn, t-1] + g.min_power[t-1] * is_on[gn, t-1]
                    g.min_power[t-1] * is_on[gn, t-1]
            else
                min_prod_last_period = max(g.initial_power, 0.0)
            end
@@ -78,7 +78,7 @@ function _add_ramp_eqs!(
            # Modified version of equation (35) in Kneuven et al. (2020)
            # Equivalent to (24)
-            eq_str_ramp_up[sc.name, gn, t] = @constraint(
+            eq_str_ramp_up[gn, t] = @constraint(
                model,
                max_prod_this_period - min_prod_last_period <=
                (SU - RU) * switch_on[gn, t] + RU * is_on[gn, t]
@@ -88,9 +88,9 @@ function _add_ramp_eqs!(
        max_prod_last_period =
            min_prod_last_period + (
                t > 1 && (RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN) ?
-                reserve[t-1] : 0.0
+                reserve[gn, t-1] : 0.0
            )
-        min_prod_this_period = prod_above[sc.name, gn, t]
+        min_prod_this_period = prod_above[gn, t]
        on_last_period = 0.0
        if t > 1
            on_last_period = is_on[gn, t-1]
@@ -100,7 +100,7 @@ function _add_ramp_eqs!(
        if t > 1 && time_invariant
            # Equation (36) in Kneuven et al. (2020)
-            eq_str_ramp_down[sc.name, gn, t] = @constraint(
+            eq_str_ramp_down[gn, t] = @constraint(
                model,
                max_prod_last_period - min_prod_this_period <=
                (SD - g.min_power[t] - RD) * switch_off[gn, t] +
@@ -112,7 +112,7 @@ function _add_ramp_eqs!(
            # Modified version of equation (36) in Kneuven et al. (2020)
            # Equivalent to (25)
-            eq_str_ramp_down[sc.name, gn, t] = @constraint(
+            eq_str_ramp_down[gn, t] = @constraint(
                model,
                max_prod_last_period - min_prod_this_period <=
                (SD - RD) * switch_off[gn, t] + RD * on_last_period
--- a/src/model/formulations/Gar1962/prod.jl
+++ b/src/model/formulations/Gar1962/prod.jl
@@ -4,35 +4,34 @@
 function _add_production_vars!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    prod_above = _init(model, :prod_above)
    segprod = _init(model, :segprod)
    for t in 1:model[:instance].time
        for k in 1:length(g.cost_segments)
-            segprod[sc.name, g.name, t, k] = @variable(model, lower_bound = 0)
+            segprod[g.name, t, k] = @variable(model, lower_bound = 0)
        end
-        prod_above[sc.name, g.name, t] = @variable(model, lower_bound = 0)
+        prod_above[g.name, t] = @variable(model, lower_bound = 0)
    end
    return
 end
 function _add_production_limit_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    eq_prod_limit = _init(model, :eq_prod_limit)
    is_on = model[:is_on]
    prod_above = model[:prod_above]
-    reserve = _total_reserves(model, g, sc)
+    reserve = model[:reserve]
    gn = g.name
    for t in 1:model[:instance].time
        # Objective function terms for production costs
        # Part of (69) of Kneuven et al. (2020) as C^R_g * u_g(t) term
        add_to_expression!(model[:obj], is_on[gn, t], g.min_power_cost[t])
        # Production limit
        # Equation (18) in Kneuven et al. (2020)
@@ -43,10 +42,9 @@ function _add_production_limit_eqs!(
        if power_diff < 1e-7
            power_diff = 0.0
        end
-        eq_prod_limit[sc.name, gn, t] = @constraint(
+        eq_prod_limit[gn, t] = @constraint(
            model,
-            prod_above[sc.name, gn, t] + reserve[t] <=
+            prod_above[gn, t] + reserve[gn, t] <= power_diff * is_on[gn, t]
            power_diff * is_on[gn, t]
        )
    end
 end
--- a/src/model/formulations/Gar1962/pwlcosts.jl
+++ b/src/model/formulations/Gar1962/pwlcosts.jl
@@ -4,11 +4,10 @@
 function _add_production_piecewise_linear_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_pwl_costs::Gar1962.PwlCosts,
    formulation_status_vars::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    eq_prod_above_def = _init(model, :eq_prod_above_def)
    eq_segprod_limit = _init(model, :eq_segprod_limit)
@@ -25,10 +24,9 @@ function _add_production_piecewise_linear_eqs!(
    for t in 1:model[:instance].time
        # Definition of production
        # Equation (43) in Kneuven et al. (2020)
-        eq_prod_above_def[sc.name, gn, t] = @constraint(
+        eq_prod_above_def[gn, t] = @constraint(
            model,
-            prod_above[sc.name, gn, t] ==
+            prod_above[gn, t] == sum(segprod[gn, t, k] for k in 1:K)
            sum(segprod[sc.name, gn, t, k] for k in 1:K)
        )
        for k in 1:K
@@ -39,25 +37,21 @@ function _add_production_piecewise_linear_eqs!(
            #     difference between max power for segments k and k-1 so the
            #     value of cost_segments[k].mw[t] is the max production *for
            #     that segment*
-            eq_segprod_limit[sc.name, gn, t, k] = @constraint(
+            eq_segprod_limit[gn, t, k] = @constraint(
                model,
-                segprod[sc.name, gn, t, k] <=
+                segprod[gn, t, k] <= g.cost_segments[k].mw[t] * is_on[gn, t]
                g.cost_segments[k].mw[t] * is_on[gn, t]
            )
            # Also add this as an explicit upper bound on segprod to make the
            # solver's work a bit easier
-            set_upper_bound(
+            set_upper_bound(segprod[gn, t, k], g.cost_segments[k].mw[t])
                segprod[sc.name, gn, t, k],
                g.cost_segments[k].mw[t],
            )
            # Objective function
            # Equation (44) in Kneuven et al. (2020)
            add_to_expression!(
                model[:obj],
-                segprod[sc.name, gn, t, k],
+                segprod[gn, t, k],
-                sc.probability * g.cost_segments[k].cost[t],
+                g.cost_segments[k].cost[t],
            )
        end
    end
--- a/src/model/formulations/Gar1962/status.jl
+++ b/src/model/formulations/Gar1962/status.jl
@@ -4,7 +4,7 @@
 function _add_status_vars!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_status_vars::Gar1962.StatusVars,
 )::Nothing
    is_on = _init(model, :is_on)
@@ -20,14 +20,13 @@ function _add_status_vars!(
            switch_on[g.name, t] = @variable(model, binary = true)
            switch_off[g.name, t] = @variable(model, binary = true)
        end
        add_to_expression!(model[:obj], is_on[g.name, t], g.min_power_cost[t])
    end
    return
 end
 function _add_status_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_status_vars::Gar1962.StatusVars,
 )::Nothing
    eq_binary_link = _init(model, :eq_binary_link)
--- a/src/model/formulations/KnuOstWat2018/pwlcosts.jl
+++ b/src/model/formulations/KnuOstWat2018/pwlcosts.jl
@@ -4,11 +4,10 @@
 function _add_production_piecewise_linear_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_pwl_costs::KnuOstWat2018.PwlCosts,
    formulation_status_vars::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    eq_prod_above_def = _init(model, :eq_prod_above_def)
    eq_segprod_limit_a = _init(model, :eq_segprod_limit_a)
@@ -59,27 +58,27 @@ function _add_production_piecewise_linear_eqs!(
            if g.min_uptime > 1
                # Equation (46) in Kneuven et al. (2020)
-                eq_segprod_limit_a[sc.name, gn, t, k] = @constraint(
+                eq_segprod_limit_a[gn, t, k] = @constraint(
                    model,
-                    segprod[sc.name, gn, t, k] <=
+                    segprod[gn, t, k] <=
                    g.cost_segments[k].mw[t] * is_on[gn, t] -
                    Cv * switch_on[gn, t] -
                    (t < T ? Cw * switch_off[gn, t+1] : 0.0)
                )
            else
                # Equation (47a)/(48a) in Kneuven et al. (2020)
-                eq_segprod_limit_b[sc.name, gn, t, k] = @constraint(
+                eq_segprod_limit_b[gn, t, k] = @constraint(
                    model,
-                    segprod[sc.name, gn, t, k] <=
+                    segprod[gn, t, k] <=
                    g.cost_segments[k].mw[t] * is_on[gn, t] -
                    Cv * switch_on[gn, t] -
                    (t < T ? max(0, Cv - Cw) * switch_off[gn, t+1] : 0.0)
                )
                # Equation (47b)/(48b) in Kneuven et al. (2020)
-                eq_segprod_limit_c[sc.name, gn, t, k] = @constraint(
+                eq_segprod_limit_c[gn, t, k] = @constraint(
                    model,
-                    segprod[sc.name, gn, t, k] <=
+                    segprod[gn, t, k] <=
                    g.cost_segments[k].mw[t] * is_on[gn, t] -
                    max(0, Cw - Cv) * switch_on[gn, t] -
                    (t < T ? Cw * switch_off[gn, t+1] : 0.0)
@@ -88,26 +87,22 @@ function _add_production_piecewise_linear_eqs!(
            # Definition of production
            # Equation (43) in Kneuven et al. (2020)
-            eq_prod_above_def[sc.name, gn, t] = @constraint(
+            eq_prod_above_def[gn, t] = @constraint(
                model,
-                prod_above[sc.name, gn, t] ==
+                prod_above[gn, t] == sum(segprod[gn, t, k] for k in 1:K)
                sum(segprod[sc.name, gn, t, k] for k in 1:K)
            )
            # Objective function
            # Equation (44) in Kneuven et al. (2020)
            add_to_expression!(
                model[:obj],
-                segprod[sc.name, gn, t, k],
+                segprod[gn, t, k],
                g.cost_segments[k].cost[t],
            )
            # Also add an explicit upper bound on segprod to make the solver's
            # work a bit easier
-            set_upper_bound(
+            set_upper_bound(segprod[gn, t, k], g.cost_segments[k].mw[t])
                segprod[sc.name, gn, t, k],
                g.cost_segments[k].mw[t],
            )
        end
    end
 end
--- a/src/model/formulations/MorLatRam2013/ramp.jl
+++ b/src/model/formulations/MorLatRam2013/ramp.jl
@@ -4,11 +4,10 @@
 function _add_ramp_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_ramping::MorLatRam2013.Ramping,
    formulation_status_vars::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    # TODO: Move upper case constants to model[:instance]
    RESERVES_WHEN_START_UP = true
@@ -23,7 +22,7 @@ function _add_ramp_eqs!(
    gn = g.name
    eq_ramp_down = _init(model, :eq_ramp_down)
    eq_ramp_up = _init(model, :eq_str_ramp_up)
-    reserve = _total_reserves(model, g, sc)
+    reserve = model[:reserve]
    # Gar1962.ProdVars
    prod_above = model[:prod_above]
@@ -40,11 +39,11 @@ function _add_ramp_eqs!(
        # Ramp up limit
        if t == 1
            if is_initially_on
-                eq_ramp_up[sc.name, gn, t] = @constraint(
+                eq_ramp_up[gn, t] = @constraint(
                    model,
                    g.min_power[t] +
-                    prod_above[sc.name, gn, t] +
+                    prod_above[gn, t] +
-                    (RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0) <=
+                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) <=
                    g.initial_power + RU
                )
            end
@@ -59,14 +58,13 @@ function _add_ramp_eqs!(
                SU = g.startup_limit
                max_prod_this_period =
                    g.min_power[t] * is_on[gn, t] +
-                    prod_above[sc.name, gn, t] +
+                    prod_above[gn, t] +
                    (
                        RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
-                        reserve[t] : 0.0
+                        reserve[gn, t] : 0.0
                    )
                min_prod_last_period =
-                    g.min_power[t-1] * is_on[gn, t-1] +
+                    g.min_power[t-1] * is_on[gn, t-1] + prod_above[gn, t-1]
                    prod_above[sc.name, gn, t-1]
                eq_ramp_up[gn, t] = @constraint(
                    model,
                    max_prod_this_period - min_prod_last_period <=
@@ -76,11 +74,11 @@ function _add_ramp_eqs!(
                # Equation (26) in Kneuven et al. (2020)
                # TODO: what if RU < SU? places too stringent upper bound
                # prod_above[gn, t] when starting up, and creates diff with (24).
-                eq_ramp_up[sc.name, gn, t] = @constraint(
+                eq_ramp_up[gn, t] = @constraint(
                    model,
-                    prod_above[sc.name, gn, t] +
+                    prod_above[gn, t] +
-                    (RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0) -
+                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) -
-                    prod_above[sc.name, gn, t-1] <= RU
+                    prod_above[gn, t-1] <= RU
                )
            end
        end
@@ -92,10 +90,9 @@ function _add_ramp_eqs!(
                #        min_power + RD < initial_power < SD
                #      then the generator should be able to shut down at time t = 1,
                #      but the constraint below will force the unit to produce power
-                eq_ramp_down[sc.name, gn, t] = @constraint(
+                eq_ramp_down[gn, t] = @constraint(
                    model,
-                    g.initial_power -
+                    g.initial_power - (g.min_power[t] + prod_above[gn, t]) <= RD
                    (g.min_power[t] + prod_above[sc.name, gn, t]) <= RD
                )
            end
        else
@@ -105,13 +102,13 @@ function _add_ramp_eqs!(
                SD = g.shutdown_limit
                max_prod_last_period =
                    g.min_power[t-1] * is_on[gn, t-1] +
-                    prod_above[sc.name, gn, t-1] +
+                    prod_above[gn, t-1] +
                    (
                        RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN ?
-                        reserve[t-1] : 0.0
+                        reserve[gn, t-1] : 0.0
                    )
                min_prod_this_period =
-                    g.min_power[t] * is_on[gn, t] + prod_above[sc.name, gn, t]
+                    g.min_power[t] * is_on[gn, t] + prod_above[gn, t]
                eq_ramp_down[gn, t] = @constraint(
                    model,
                    max_prod_last_period - min_prod_this_period <=
@@ -121,11 +118,11 @@ function _add_ramp_eqs!(
                # Equation (27) in Kneuven et al. (2020)
                # TODO: Similar to above, what to do if shutting down in time t
                # and RD < SD? There is a difference with (25).
-                eq_ramp_down[sc.name, gn, t] = @constraint(
+                eq_ramp_down[gn, t] = @constraint(
                    model,
-                    prod_above[sc.name, gn, t-1] +
+                    prod_above[gn, t-1] +
-                    (RESERVES_WHEN_RAMP_DOWN ? reserve[t-1] : 0.0) -
+                    (RESERVES_WHEN_RAMP_DOWN ? reserve[gn, t-1] : 0.0) -
-                    prod_above[sc.name, gn, t] <= RD
+                    prod_above[gn, t] <= RD
                )
            end
        end
--- a/src/model/formulations/MorLatRam2013/scosts.jl
+++ b/src/model/formulations/MorLatRam2013/scosts.jl
@@ -4,7 +4,7 @@
 function _add_startup_cost_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation::MorLatRam2013.StartupCosts,
 )::Nothing
    eq_startup_choose = _init(model, :eq_startup_choose)
--- a/src/model/formulations/PanGua2016/ramp.jl
+++ b/src/model/formulations/PanGua2016/ramp.jl
@@ -4,16 +4,15 @@
 function _add_ramp_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation_prod_vars::Gar1962.ProdVars,
    formulation_ramping::PanGua2016.Ramping,
    formulation_status_vars::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    # TODO: Move upper case constants to model[:instance]
    RESERVES_WHEN_SHUT_DOWN = true
    gn = g.name
-    reserve = _total_reserves(model, g, sc)
+    reserve = model[:reserve]
    eq_str_prod_limit = _init(model, :eq_str_prod_limit)
    eq_prod_limit_ramp_up_extra_period =
        _init(model, :eq_prod_limit_ramp_up_extra_period)
@@ -53,11 +52,11 @@ function _add_ramp_eqs!(
            # Generalization of (20)
            # Necessary that if any of the switch_on = 1 in the sum,
            # then switch_off[gn, t+1] = 0
-            eq_str_prod_limit[sc.name, gn, t] = @constraint(
+            eq_str_prod_limit[gn, t] = @constraint(
                model,
-                prod_above[sc.name, gn, t] +
+                prod_above[gn, t] +
                g.min_power[t] * is_on[gn, t] +
-                reserve[t] <=
+                reserve[gn, t] <=
                Pbar * is_on[gn, t] -
                (t < T ? (Pbar - SD) * switch_off[gn, t+1] : 0.0) - sum(
                    (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
@@ -68,17 +67,16 @@ function _add_ramp_eqs!(
            if UT - 2 < TRU
                # Equation (40) in Kneuven et al. (2020)
                # Covers an additional time period of the ramp-up trajectory, compared to (38)
-                eq_prod_limit_ramp_up_extra_period[sc.name, gn, t] =
+                eq_prod_limit_ramp_up_extra_period[gn, t] = @constraint(
-                    @constraint(
+                    model,
-                        model,
+                    prod_above[gn, t] +
-                        prod_above[sc.name, gn, t] +
+                    g.min_power[t] * is_on[gn, t] +
-                        g.min_power[t] * is_on[gn, t] +
+                    reserve[gn, t] <=
-                        reserve[t] <=
+                    Pbar * is_on[gn, t] - sum(
-                        Pbar * is_on[gn, t] - sum(
+                        (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
-                            (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
+                        i in 0:min(UT - 1, TRU, t - 1)
                            i in 0:min(UT - 1, TRU, t - 1)
                        )
                    )
                )
            end
            # Add in shutdown trajectory if KSD >= 0 (else this is dominated by (38))
@@ -86,11 +84,11 @@ function _add_ramp_eqs!(
            if KSD > 0
                KSU = min(TRU, UT - 2 - KSD, t - 1)
                # Equation (41) in Kneuven et al. (2020)
-                eq_prod_limit_shutdown_trajectory[sc.name, gn, t] = @constraint(
+                eq_prod_limit_shutdown_trajectory[gn, t] = @constraint(
                    model,
-                    prod_above[sc.name, gn, t] +
+                    prod_above[gn, t] +
                    g.min_power[t] * is_on[gn, t] +
-                    (RESERVES_WHEN_SHUT_DOWN ? reserve[t] : 0.0) <=
+                    (RESERVES_WHEN_SHUT_DOWN ? reserve[gn, t] : 0.0) <=
                    Pbar * is_on[gn, t] - sum(
                        (Pbar - (SD + i * RD)) * switch_off[gn, t+1+i] for
                        i in 0:KSD
--- a/src/model/formulations/WanHob2016/ramp.jl
+++ b/src/model/formulations/WanHob2016/ramp.jl
@@ -1,193 +0,0 @@
 # UnitCommitmentFL.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 function _add_ramp_eqs!(
    model::JuMP.Model,
    g::ThermalUnit,
    ::Gar1962.ProdVars,
    ::WanHob2016.Ramping,
    ::Gar1962.StatusVars,
    sc::UnitCommitmentScenario,
 )::Nothing
    is_initially_on = (g.initial_status > 0)
    SU = g.startup_limit
    SD = g.shutdown_limit
    RU = g.ramp_up_limit
    RD = g.ramp_down_limit
    gn = g.name
    minp = g.min_power
    maxp = g.max_power
    initial_power = g.initial_power
    is_on = model[:is_on]
    prod_above = model[:prod_above]
    upflexiramp = model[:upflexiramp]
    dwflexiramp = model[:dwflexiramp]
    mfg = model[:mfg]
    if length(g.reserves) > 1
        error("Each generator may only provide one flexiramp reserve")
    end
    for r in g.reserves
        if r.type !== "flexiramp"
            error(
                "This formulation only supports flexiramp reserves, not $(r.type)",
            )
        end
        rn = r.name
        for t in 1:model[:instance].time
            @constraint(
                model,
                prod_above[sc.name, gn, t] + (is_on[gn, t] * minp[t]) <=
                mfg[sc.name, gn, t]
            ) # Eq. (19) in Wang & Hobbs (2016)
            @constraint(model, mfg[sc.name, gn, t] <= is_on[gn, t] * maxp[t]) # Eq. (22) in Wang & Hobbs (2016)
            if t != model[:instance].time
                @constraint(
                    model,
                    minp[t] * (is_on[gn, t+1] + is_on[gn, t] - 1) <=
                    prod_above[sc.name, gn, t] -
                    dwflexiramp[sc.name, rn, gn, t] + (is_on[gn, t] * minp[t])
                ) # first inequality of Eq. (20) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    prod_above[sc.name, gn, t] -
                    dwflexiramp[sc.name, rn, gn, t] +
                    (is_on[gn, t] * minp[t]) <=
                    mfg[sc.name, gn, t+1] + (maxp[t] * (1 - is_on[gn, t+1]))
                ) # second inequality of Eq. (20) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    minp[t] * (is_on[gn, t+1] + is_on[gn, t] - 1) <=
                    prod_above[sc.name, gn, t] +
                    upflexiramp[sc.name, rn, gn, t] +
                    (is_on[gn, t] * minp[t])
                ) # first inequality of Eq. (21) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    prod_above[sc.name, gn, t] +
                    upflexiramp[sc.name, rn, gn, t] +
                    (is_on[gn, t] * minp[t]) <=
                    mfg[sc.name, gn, t+1] + (maxp[t] * (1 - is_on[gn, t+1]))
                ) # second inequality of Eq. (21) in Wang & Hobbs (2016)
                if t != 1
                    @constraint(
                        model,
                        mfg[sc.name, gn, t] <=
                        prod_above[sc.name, gn, t-1] +
                        (is_on[gn, t-1] * minp[t]) +
                        (RU * is_on[gn, t-1]) +
                        (SU * (is_on[gn, t] - is_on[gn, t-1])) +
                        maxp[t] * (1 - is_on[gn, t])
                    ) # Eq. (23) in Wang & Hobbs (2016)
                    @constraint(
                        model,
                        (
                            prod_above[sc.name, gn, t-1] +
                            (is_on[gn, t-1] * minp[t])
                        ) - (
                            prod_above[sc.name, gn, t] +
                            (is_on[gn, t] * minp[t])
                        ) <=
                        RD * is_on[gn, t] +
                        SD * (is_on[gn, t-1] - is_on[gn, t]) +
                        maxp[t] * (1 - is_on[gn, t-1])
                    ) # Eq. (25) in Wang & Hobbs (2016)
                else
                    @constraint(
                        model,
                        mfg[sc.name, gn, t] <=
                        initial_power +
                        (RU * is_initially_on) +
                        (SU * (is_on[gn, t] - is_initially_on)) +
                        maxp[t] * (1 - is_on[gn, t])
                    ) # Eq. (23) in Wang & Hobbs (2016) for the first time period
                    @constraint(
                        model,
                        initial_power - (
                            prod_above[sc.name, gn, t] +
                            (is_on[gn, t] * minp[t])
                        ) <=
                        RD * is_on[gn, t] +
                        SD * (is_initially_on - is_on[gn, t]) +
                        maxp[t] * (1 - is_initially_on)
                    ) # Eq. (25) in Wang & Hobbs (2016) for the first time period
                end
                @constraint(
                    model,
                    mfg[sc.name, gn, t] <=
                    (SD * (is_on[gn, t] - is_on[gn, t+1])) +
                    (maxp[t] * is_on[gn, t+1])
                ) # Eq. (24) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    -RD * is_on[gn, t+1] -
                    SD * (is_on[gn, t] - is_on[gn, t+1]) -
                    maxp[t] * (1 - is_on[gn, t]) <=
                    upflexiramp[sc.name, rn, gn, t]
                ) # first inequality of Eq. (26) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    upflexiramp[sc.name, rn, gn, t] <=
                    RU * is_on[gn, t] +
                    SU * (is_on[gn, t+1] - is_on[gn, t]) +
                    maxp[t] * (1 - is_on[gn, t+1])
                ) # second inequality of Eq. (26) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    -RU * is_on[gn, t] - SU * (is_on[gn, t+1] - is_on[gn, t]) -
                    maxp[t] * (1 - is_on[gn, t+1]) <=
                    dwflexiramp[sc.name, rn, gn, t]
                ) # first inequality of Eq. (27) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    dwflexiramp[sc.name, rn, gn, t] <=
                    RD * is_on[gn, t+1] +
                    SD * (is_on[gn, t] - is_on[gn, t+1]) +
                    maxp[t] * (1 - is_on[gn, t])
                ) # second inequality of Eq. (27) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    -maxp[t] * is_on[gn, t] + minp[t] * is_on[gn, t+1] <=
                    upflexiramp[sc.name, rn, gn, t]
                ) # first inequality of Eq. (28) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    upflexiramp[sc.name, rn, gn, t] <= maxp[t] * is_on[gn, t+1]
                ) # second inequality of Eq. (28) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    -maxp[t] * is_on[gn, t+1] <=
                    dwflexiramp[sc.name, rn, gn, t]
                ) # first inequality of Eq. (29) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    dwflexiramp[sc.name, rn, gn, t] <=
                    (maxp[t] * is_on[gn, t]) - (minp[t] * is_on[gn, t+1])
                ) # second inequality of Eq. (29) in Wang & Hobbs (2016)
            else
                @constraint(
                    model,
                    mfg[sc.name, gn, t] <=
                    prod_above[sc.name, gn, t-1] +
                    (is_on[gn, t-1] * minp[t]) +
                    (RU * is_on[gn, t-1]) +
                    (SU * (is_on[gn, t] - is_on[gn, t-1])) +
                    maxp[t] * (1 - is_on[gn, t])
                ) # Eq. (23) in Wang & Hobbs (2016) for the last time period
                @constraint(
                    model,
                    (
                        prod_above[sc.name, gn, t-1] +
                        (is_on[gn, t-1] * minp[t])
                    ) -
                    (prod_above[sc.name, gn, t] + (is_on[gn, t] * minp[t])) <=
                    RD * is_on[gn, t] +
                    SD * (is_on[gn, t-1] - is_on[gn, t]) +
                    maxp[t] * (1 - is_on[gn, t-1])
                ) # Eq. (25) in Wang & Hobbs (2016) for the last time period
            end
        end
    end
 end
--- a/src/model/formulations/WanHob2016/structs.jl
+++ b/src/model/formulations/WanHob2016/structs.jl
@@ -1,18 +0,0 @@
 # UnitCommitmentFL.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 """
 Formulation described in:
    B. Wang and B. F. Hobbs, "Real-Time Markets for Flexiramp: A Stochastic 
    Unit Commitment-Based Analysis," in IEEE Transactions on Power Systems, 
    vol. 31, no. 2, pp. 846-860, March 2016, doi: 10.1109/TPWRS.2015.2411268.
 """
 module WanHob2016
 import ..RampingFormulation
 struct Ramping <: RampingFormulation end
 end
--- a/src/model/formulations/base/bus.jl
+++ b/src/model/formulations/base/bus.jl
@@ -2,30 +2,22 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
-function _add_bus!(
+function _add_bus!(model::JuMP.Model, b::Bus)::Nothing
    model::JuMP.Model,
    b::Bus,
    sc::UnitCommitmentScenario,
 )::Nothing
    net_injection = _init(model, :expr_net_injection)
    curtail = _init(model, :curtail)
    for t in 1:model[:instance].time
        # Fixed load
-        net_injection[sc.name, b.name, t] = AffExpr(-b.load[t])
+        net_injection[b.name, t] = AffExpr(-b.load[t])
        # Load curtailment
-        curtail[sc.name, b.name, t] =
+        curtail[b.name, t] =
            @variable(model, lower_bound = 0, upper_bound = b.load[t])
-        add_to_expression!(
+        add_to_expression!(net_injection[b.name, t], curtail[b.name, t], 1.0)
            net_injection[sc.name, b.name, t],
            curtail[sc.name, b.name, t],
            1.0,
        )
        add_to_expression!(
            model[:obj],
-            curtail[sc.name, b.name, t],
+            curtail[b.name, t],
-            sc.power_balance_penalty[t] * sc.probability,
+            model[:instance].power_balance_penalty[t],
        )
    end
    return
--- a/src/model/formulations/base/line.jl
+++ b/src/model/formulations/base/line.jl
@@ -6,43 +6,43 @@ function _add_transmission_line!(
    model::JuMP.Model,
    lm::TransmissionLine,
    f::ShiftFactorsFormulation,
    sc::UnitCommitmentScenario,
 )::Nothing
    overflow = _init(model, :overflow)
    for t in 1:model[:instance].time
-        overflow[sc.name, lm.name, t] = @variable(model, lower_bound = 0)
+        overflow[lm.name, t] = @variable(model, lower_bound = 0)
        add_to_expression!(
            model[:obj],
-            overflow[sc.name, lm.name, t],
+            overflow[lm.name, t],
-            lm.flow_limit_penalty[t] * sc.probability,
+            lm.flow_limit_penalty[t],
        )
    end
    return
 end
 function _setup_transmission(
    model::JuMP.Model,
    formulation::ShiftFactorsFormulation,
    sc::UnitCommitmentScenario,
 )::Nothing
    instance = model[:instance]
    isf = formulation.precomputed_isf
    lodf = formulation.precomputed_lodf
-    if length(sc.buses) == 1
+    if length(instance.buses) == 1
        isf = zeros(0, 0)
        lodf = zeros(0, 0)
    elseif isf === nothing
        @info "Computing injection shift factors..."
        time_isf = @elapsed begin
            isf = UnitCommitment._injection_shift_factors(
-                buses = sc.buses,
+                lines = instance.lines,
-                lines = sc.lines,
+                buses = instance.buses,
            )
        end
        @info @sprintf("Computed ISF in %.2f seconds", time_isf)
        @info "Computing line outage factors..."
        time_lodf = @elapsed begin
            lodf = UnitCommitment._line_outage_factors(
-                buses = sc.buses,
+                lines = instance.lines,
-                lines = sc.lines,
+                buses = instance.buses,
                isf = isf,
            )
        end
@@ -55,7 +55,7 @@ function _setup_transmission(
        isf[abs.(isf).<formulation.isf_cutoff] .= 0
        lodf[abs.(lodf).<formulation.lodf_cutoff] .= 0
    end
-    sc.isf = isf
+    model[:isf] = isf
-    sc.lodf = lodf
+    model[:lodf] = lodf
    return
 end
--- a/src/model/formulations/base/psload.jl
+++ b/src/model/formulations/base/psload.jl
@@ -5,26 +5,21 @@
 function _add_price_sensitive_load!(
    model::JuMP.Model,
    ps::PriceSensitiveLoad,
    sc::UnitCommitmentScenario,
 )::Nothing
    loads = _init(model, :loads)
    net_injection = _init(model, :expr_net_injection)
    for t in 1:model[:instance].time
        # Decision variable
-        loads[sc.name, ps.name, t] =
+        loads[ps.name, t] =
            @variable(model, lower_bound = 0, upper_bound = ps.demand[t])
        # Objective function terms
-        add_to_expression!(
+        add_to_expression!(model[:obj], loads[ps.name, t], -ps.revenue[t])
            model[:obj],
            loads[sc.name, ps.name, t],
            -ps.revenue[t] * sc.probability,
        )
        # Net injection
        add_to_expression!(
-            net_injection[sc.name, ps.bus.name, t],
+            net_injection[ps.bus.name, t],
-            loads[sc.name, ps.name, t],
+            loads[ps.name, t],
            -1.0,
        )
    end
--- a/src/model/formulations/base/punit.jl
+++ b/src/model/formulations/base/punit.jl
@@ -1,35 +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.
 function _add_profiled_unit!(
    model::JuMP.Model,
    pu::ProfiledUnit,
    sc::UnitCommitmentScenario,
 )::Nothing
    punits = _init(model, :prod_profiled)
    net_injection = _init(model, :expr_net_injection)
    for t in 1:model[:instance].time
        # Decision variable
        punits[sc.name, pu.name, t] = @variable(
            model,
            lower_bound = pu.min_power[t],
            upper_bound = pu.max_power[t]
        )
        # Objective function terms
        add_to_expression!(
            model[:obj],
            punits[sc.name, pu.name, t],
            pu.cost[t] * sc.probability,
        )
        # Net injection
        add_to_expression!(
            net_injection[sc.name, pu.bus.name, t],
            punits[sc.name, pu.name, t],
            1.0,
        )
    end
    return
 end
--- a/src/model/formulations/base/sensitivity.jl
+++ b/src/model/formulations/base/sensitivity.jl
@@ -18,7 +18,7 @@ function _injection_shift_factors(;
    lines::Array{TransmissionLine},
 )
    susceptance = _susceptance_matrix(lines)
-    incidence = _reduced_incidence_matrix(buses = buses, lines = lines)
+    incidence = _reduced_incidence_matrix(lines = lines, buses = buses)
    laplacian = transpose(incidence) * susceptance * incidence
    isf = susceptance * incidence * inv(Array(laplacian))
    return isf
--- a/src/model/formulations/base/structs.jl
+++ b/src/model/formulations/base/structs.jl
@@ -9,27 +9,6 @@ abstract type StartupCostsFormulation end
 abstract type StatusVarsFormulation end
 abstract type ProductionVarsFormulation end
 """
    struct Formulation
        prod_vars::ProductionVarsFormulation
        pwl_costs::PiecewiseLinearCostsFormulation
        ramping::RampingFormulation
        startup_costs::StartupCostsFormulation
        status_vars::StatusVarsFormulation
        transmission::TransmissionFormulation
    end
 Struct provided to `build_model` that holds various formulation components.
 # Fields
 - `prod_vars`: Formulation for the production decision variables
 - `pwl_costs`: Formulation for the piecewise linear costs
 - `ramping`: Formulation for ramping constraints
 - `startup_costs`: Formulation for time-dependent start-up costs
 - `status_vars`: Formulation for the status variables (e.g. `is_on`, `is_off`)
 - `transmission`: Formulation for transmission and N-1 security constraints
 """
 struct Formulation
    prod_vars::ProductionVarsFormulation
    pwl_costs::PiecewiseLinearCostsFormulation
@@ -59,10 +38,10 @@ end
 """
    struct ShiftFactorsFormulation <: TransmissionFormulation
-        isf_cutoff::Float64 = 0.005
+        isf_cutoff::Float64
-        lodf_cutoff::Float64 = 0.001
+        lodf_cutoff::Float64
-        precomputed_isf=nothing
+        precomputed_isf::Union{Nothing,Matrix{Float64}}
-        precomputed_lodf=nothing
+        precomputed_lodf::Union{Nothing,Matrix{Float64}}
    end
 Transmission formulation based on Injection Shift Factors (ISF) and Line
@@ -70,15 +49,15 @@ Outage Distribution Factors (LODF). Constraints are enforced in a lazy way.
 Arguments
 ---------
- `precomputed_isf`:
+- `precomputed_isf::Union{Matrix{Float64},Nothing} = nothing`:
    the injection shift factors matrix. If not provided, it will be computed.
- `precomputed_lodf`: 
+- `precomputed_lodf::Union{Matrix{Float64},Nothing} = nothing`: 
    the line outage distribution factors matrix. If not provided, it will be
    computed.
- `isf_cutoff`: 
+- `isf_cutoff::Float64 = 0.005`: 
    the cutoff that should be applied to the ISF matrix. Entries with magnitude
    smaller than this value will be set to zero.
- `lodf_cutoff`: 
+- `lodf_cutoff::Float64 = 0.001`: 
    the cutoff that should be applied to the LODF matrix. Entries with magnitude
    smaller than this value will be set to zero.
 """
--- a/src/model/formulations/base/system.jl
+++ b/src/model/formulations/base/system.jl
@@ -2,120 +2,54 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
-function _add_system_wide_eqs!(
+function _add_system_wide_eqs!(model::JuMP.Model)::Nothing
-    model::JuMP.Model,
+    _add_net_injection_eqs!(model)
-    sc::UnitCommitmentScenario,
+    _add_reserve_eqs!(model)
 )::Nothing
    _add_net_injection_eqs!(model, sc)
    _add_spinning_reserve_eqs!(model, sc)
    _add_flexiramp_reserve_eqs!(model, sc)
    return
 end
-function _add_net_injection_eqs!(
+function _add_net_injection_eqs!(model::JuMP.Model)::Nothing
    model::JuMP.Model,
    sc::UnitCommitmentScenario,
 )::Nothing
    T = model[:instance].time
    net_injection = _init(model, :net_injection)
    eq_net_injection = _init(model, :eq_net_injection)
    eq_power_balance = _init(model, :eq_power_balance)
-    for t in 1:T, b in sc.buses
+    for t in 1:T, b in model[:instance].buses
-        n = net_injection[sc.name, b.name, t] = @variable(model)
+        n = net_injection[b.name, t] = @variable(model)
-        eq_net_injection[sc.name, b.name, t] = @constraint(
+        eq_net_injection[b.name, t] =
-            model,
+            @constraint(model, -n + model[:expr_net_injection][b.name, t] == 0)
            -n + model[:expr_net_injection][sc.name, b.name, t] == 0
        )
    end
    for t in 1:T
-        eq_power_balance[sc.name, t] = @constraint(
+        eq_power_balance[t] = @constraint(
            model,
-            sum(net_injection[sc.name, b.name, t] for b in sc.buses) == 0
+            sum(net_injection[b.name, t] for b in model[:instance].buses) == 0
        )
    end
    return
 end
-function _add_spinning_reserve_eqs!(
+function _add_reserve_eqs!(model::JuMP.Model)::Nothing
-    model::JuMP.Model,
+    eq_min_reserve = _init(model, :eq_min_reserve)
-    sc::UnitCommitmentScenario,
+    instance = model[:instance]
-)::Nothing
+    for t in 1:instance.time
-    T = model[:instance].time
+        # Equation (68) in Kneuven et al. (2020)
-    eq_min_spinning_reserve = _init(model, :eq_min_spinning_reserve)
+        # As in Morales-España et al. (2013a)
-    for r in sc.reserves
+        # Akin to the alternative formulation with max_power_avail
-        r.type == "spinning" || continue
+        # from Carrión and Arroyo (2006) and Ostrowski et al. (2012)
-        for t in 1:T
+        shortfall_penalty = instance.shortfall_penalty[t]
-            # Equation (68) in Kneuven et al. (2020)
+        eq_min_reserve[t] = @constraint(
-            # As in Morales-España et al. (2013a)
+            model,
-            # Akin to the alternative formulation with max_power_avail
+            sum(model[:reserve][g.name, t] for g in instance.units) +
-            # from Carrión and Arroyo (2006) and Ostrowski et al. (2012)
+            (shortfall_penalty >= 0 ? model[:reserve_shortfall][t] : 0.0) >=
-            eq_min_spinning_reserve[sc.name, r.name, t] = @constraint(
+            instance.reserves.spinning[t]
-                model,
+        )
                sum(
                    model[:reserve][sc.name, r.name, g.name, t] for
                    g in r.thermal_units
                ) + model[:reserve_shortfall][sc.name, r.name, t] >=
                r.amount[t]
            )
-            # Account for shortfall contribution to objective
+        # Account for shortfall contribution to objective
-            if r.shortfall_penalty >= 0
+        if shortfall_penalty >= 0
-                add_to_expression!(
+            add_to_expression!(
-                    model[:obj],
+                model[:obj],
-                    r.shortfall_penalty * sc.probability,
+                shortfall_penalty,
-                    model[:reserve_shortfall][sc.name, r.name, t],
+                model[:reserve_shortfall][t],
-                )
+            )
            end
        end
    end
    return
 end
 function _add_flexiramp_reserve_eqs!(
    model::JuMP.Model,
    sc::UnitCommitmentScenario,
 )::Nothing
    # Note: The flexpramp requirements in Wang & Hobbs (2016) are imposed as hard constraints 
    #       through Eq. (17) and Eq. (18). The constraints eq_min_upflexiramp and eq_min_dwflexiramp 
    #       provided below are modified versions of Eq. (17) and Eq. (18), respectively, in that   
    #       they include slack variables for flexiramp shortfall, which are penalized in the
    #       objective function.
    eq_min_upflexiramp = _init(model, :eq_min_upflexiramp)
    eq_min_dwflexiramp = _init(model, :eq_min_dwflexiramp)
    T = model[:instance].time
    for r in sc.reserves
        r.type == "flexiramp" || continue
        for t in 1:T
            # Eq. (17) in Wang & Hobbs (2016)
            eq_min_upflexiramp[sc.name, r.name, t] = @constraint(
                model,
                sum(
                    model[:upflexiramp][sc.name, r.name, g.name, t] for
                    g in r.thermal_units
                ) + model[:upflexiramp_shortfall][sc.name, r.name, t] >=
                r.amount[t]
            )
            # Eq. (18) in Wang & Hobbs (2016)
            eq_min_dwflexiramp[sc.name, r.name, t] = @constraint(
                model,
                sum(
                    model[:dwflexiramp][sc.name, r.name, g.name, t] for
                    g in r.thermal_units
                ) + model[:dwflexiramp_shortfall][sc.name, r.name, t] >=
                r.amount[t]
            )
            # Account for flexiramp shortfall contribution to objective
            if r.shortfall_penalty >= 0
                add_to_expression!(
                    model[:obj],
                    r.shortfall_penalty * sc.probability,
                    (
                        model[:upflexiramp_shortfall][sc.name, r.name, t] +
                        model[:dwflexiramp_shortfall][sc.name, r.name, t]
                    ),
                )
            end
        end
    end
    return
--- a/src/model/formulations/base/unit.jl
+++ b/src/model/formulations/base/unit.jl
@@ -2,13 +2,7 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
-# Function for adding variables, constraints, and objective function terms
+function _add_unit!(model::JuMP.Model, g::Unit, formulation::Formulation)
 # related to the binary commitment, startup and shutdown decisions of units
 function _add_unit_commitment!(
    model::JuMP.Model,
    g::ThermalUnit,
    formulation::Formulation,
 )
    if !all(g.must_run) && any(g.must_run)
        error("Partially must-run units are not currently supported")
    end
@@ -17,41 +11,21 @@ function _add_unit_commitment!(
    end
    # Variables
    _add_production_vars!(model, g, formulation.prod_vars)
    _add_reserve_vars!(model, g)
    _add_startup_shutdown_vars!(model, g)
    _add_status_vars!(model, g, formulation.status_vars)
    # Constraints and objective function
    _add_min_uptime_downtime_eqs!(model, g)
-    _add_startup_cost_eqs!(model, g, formulation.startup_costs)
+    _add_net_injection_eqs!(model, g)
-    _add_status_eqs!(model, g, formulation.status_vars)
+    _add_production_limit_eqs!(model, g, formulation.prod_vars)
    _add_commitment_status_eqs!(model, g)
    return
 end
 # Function for adding variables, constraints, and objective function terms
 # related to the continuous dispatch decisions of units
 function _add_unit_dispatch!(
    model::JuMP.Model,
    g::ThermalUnit,
    formulation::Formulation,
    sc::UnitCommitmentScenario,
 )
    # Variables
    _add_production_vars!(model, g, formulation.prod_vars, sc)
    _add_spinning_reserve_vars!(model, g, sc)
    _add_flexiramp_reserve_vars!(model, g, sc)
    # Constraints and objective function
    _add_net_injection_eqs!(model, g, sc)
    _add_production_limit_eqs!(model, g, formulation.prod_vars, sc)
    _add_production_piecewise_linear_eqs!(
        model,
        g,
        formulation.prod_vars,
        formulation.pwl_costs,
        formulation.status_vars,
        sc,
    )
    _add_ramp_eqs!(
        model,
@@ -59,77 +33,40 @@ function _add_unit_dispatch!(
        formulation.prod_vars,
        formulation.ramping,
        formulation.status_vars,
        sc,
    )
-    _add_startup_shutdown_limit_eqs!(model, g, sc)
+    _add_startup_cost_eqs!(model, g, formulation.startup_costs)
    _add_startup_shutdown_limit_eqs!(model, g)
    _add_status_eqs!(model, g, formulation.status_vars)
    return
 end
-_is_initially_on(g::ThermalUnit)::Float64 = (g.initial_status > 0 ? 1.0 : 0.0)
+_is_initially_on(g::Unit)::Float64 = (g.initial_status > 0 ? 1.0 : 0.0)
-function _add_spinning_reserve_vars!(
+function _add_reserve_vars!(model::JuMP.Model, g::Unit)::Nothing
    model::JuMP.Model,
    g::ThermalUnit,
    sc::UnitCommitmentScenario,
 )::Nothing
    reserve = _init(model, :reserve)
    reserve_shortfall = _init(model, :reserve_shortfall)
    for r in g.reserves
        r.type == "spinning" || continue
        for t in 1:model[:instance].time
            reserve[sc.name, r.name, g.name, t] =
                @variable(model, lower_bound = 0)
            if (sc.name, r.name, t) ∉ keys(reserve_shortfall)
                reserve_shortfall[sc.name, r.name, t] =
                    @variable(model, lower_bound = 0)
                if r.shortfall_penalty < 0
                    set_upper_bound(reserve_shortfall[sc.name, r.name, t], 0.0)
                end
            end
        end
    end
    return
 end
 function _add_flexiramp_reserve_vars!(
    model::JuMP.Model,
    g::ThermalUnit,
    sc::UnitCommitmentScenario,
 )::Nothing
    upflexiramp = _init(model, :upflexiramp)
    upflexiramp_shortfall = _init(model, :upflexiramp_shortfall)
    mfg = _init(model, :mfg)
    dwflexiramp = _init(model, :dwflexiramp)
    dwflexiramp_shortfall = _init(model, :dwflexiramp_shortfall)
    for t in 1:model[:instance].time
-        # maximum feasible generation, \bar{g_{its}} in Wang & Hobbs (2016)
+        if g.provides_spinning_reserves[t]
-        mfg[sc.name, g.name, t] = @variable(model, lower_bound = 0)
+            reserve[g.name, t] = @variable(model, lower_bound = 0)
-        for r in g.reserves
+        else
-            r.type == "flexiramp" || continue
+            reserve[g.name, t] = 0.0
            upflexiramp[sc.name, r.name, g.name, t] = @variable(model) # up-flexiramp, ur_{it} in Wang & Hobbs (2016)
            dwflexiramp[sc.name, r.name, g.name, t] = @variable(model) # down-flexiramp, dr_{it} in Wang & Hobbs (2016)
            if (sc.name, r.name, t) ∉ keys(upflexiramp_shortfall)
                upflexiramp_shortfall[sc.name, r.name, t] =
                    @variable(model, lower_bound = 0)
                dwflexiramp_shortfall[sc.name, r.name, t] =
                    @variable(model, lower_bound = 0)
                if r.shortfall_penalty < 0
                    set_upper_bound(
                        upflexiramp_shortfall[sc.name, r.name, t],
                        0.0,
                    )
                    set_upper_bound(
                        dwflexiramp_shortfall[sc.name, r.name, t],
                        0.0,
                    )
                end
            end
        end
        reserve_shortfall[t] =
            (model[:instance].shortfall_penalty[t] >= 0) ?
            @variable(model, lower_bound = 0) : 0.0
    end
    return
 end
-function _add_startup_shutdown_vars!(model::JuMP.Model, g::ThermalUnit)::Nothing
+function _add_reserve_eqs!(model::JuMP.Model, g::Unit)::Nothing
    reserve = model[:reserve]
    for t in 1:model[:instance].time
        add_to_expression!(expr_reserve[g.bus.name, t], reserve[g.name, t], 1.0)
    end
    return
 end
 function _add_startup_shutdown_vars!(model::JuMP.Model, g::Unit)::Nothing
    startup = _init(model, :startup)
    for t in 1:model[:instance].time
        for s in 1:length(g.startup_categories)
@@ -139,36 +76,32 @@ function _add_startup_shutdown_vars!(model::JuMP.Model, g::ThermalUnit)::Nothing
    return
 end
-function _add_startup_shutdown_limit_eqs!(
+function _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
    model::JuMP.Model,
    g::ThermalUnit,
    sc::UnitCommitmentScenario,
 )::Nothing
    eq_shutdown_limit = _init(model, :eq_shutdown_limit)
    eq_startup_limit = _init(model, :eq_startup_limit)
    is_on = model[:is_on]
    prod_above = model[:prod_above]
-    reserve = _total_reserves(model, g, sc)
+    reserve = model[:reserve]
    switch_off = model[:switch_off]
    switch_on = model[:switch_on]
    T = model[:instance].time
    for t in 1:T
        # Startup limit
-        eq_startup_limit[sc.name, g.name, t] = @constraint(
+        eq_startup_limit[g.name, t] = @constraint(
            model,
-            prod_above[sc.name, g.name, t] + reserve[t] <=
+            prod_above[g.name, t] + reserve[g.name, t] <=
            (g.max_power[t] - g.min_power[t]) * is_on[g.name, t] -
            max(0, g.max_power[t] - g.startup_limit) * switch_on[g.name, t]
        )
        # Shutdown limit
        if g.initial_power > g.shutdown_limit
-            eq_shutdown_limit[sc.name, g.name, 0] =
+            eq_shutdown_limit[g.name, 0] =
                @constraint(model, switch_off[g.name, 1] <= 0)
        end
        if t < T
-            eq_shutdown_limit[sc.name, g.name, t] = @constraint(
+            eq_shutdown_limit[g.name, t] = @constraint(
                model,
-                prod_above[sc.name, g.name, t] <=
+                prod_above[g.name, t] <=
                (g.max_power[t] - g.min_power[t]) * is_on[g.name, t] -
                max(0, g.max_power[t] - g.shutdown_limit) *
                switch_off[g.name, t+1]
@@ -180,55 +113,51 @@ end
 function _add_ramp_eqs!(
    model::JuMP.Model,
-    g::ThermalUnit,
+    g::Unit,
    formulation::RampingFormulation,
    sc::UnitCommitmentScenario,
 )::Nothing
    prod_above = model[:prod_above]
-    reserve = _total_reserves(model, g, sc)
+    reserve = model[:reserve]
    eq_ramp_up = _init(model, :eq_ramp_up)
    eq_ramp_down = _init(model, :eq_ramp_down)
    for t in 1:model[:instance].time
        # Ramp up limit 
        if t == 1
            if _is_initially_on(g) == 1
-                eq_ramp_up[sc.name, g.name, t] = @constraint(
+                eq_ramp_up[g.name, t] = @constraint(
                    model,
-                    prod_above[sc.name, g.name, t] + reserve[t] <=
+                    prod_above[g.name, t] + reserve[g.name, t] <=
                    (g.initial_power - g.min_power[t]) + g.ramp_up_limit
                )
            end
        else
-            eq_ramp_up[sc.name, g.name, t] = @constraint(
+            eq_ramp_up[g.name, t] = @constraint(
                model,
-                prod_above[sc.name, g.name, t] + reserve[t] <=
+                prod_above[g.name, t] + reserve[g.name, t] <=
-                prod_above[sc.name, g.name, t-1] + g.ramp_up_limit
+                prod_above[g.name, t-1] + g.ramp_up_limit
            )
        end
        # Ramp down limit
        if t == 1
            if _is_initially_on(g) == 1
-                eq_ramp_down[sc.name, g.name, t] = @constraint(
+                eq_ramp_down[g.name, t] = @constraint(
                    model,
-                    prod_above[sc.name, g.name, t] >=
+                    prod_above[g.name, t] >=
                    (g.initial_power - g.min_power[t]) - g.ramp_down_limit
                )
            end
        else
-            eq_ramp_down[sc.name, g.name, t] = @constraint(
+            eq_ramp_down[g.name, t] = @constraint(
                model,
-                prod_above[sc.name, g.name, t] >=
+                prod_above[g.name, t] >=
-                prod_above[sc.name, g.name, t-1] - g.ramp_down_limit
+                prod_above[g.name, t-1] - g.ramp_down_limit
            )
        end
    end
 end
-function _add_min_uptime_downtime_eqs!(
+function _add_min_uptime_downtime_eqs!(model::JuMP.Model, g::Unit)::Nothing
    model::JuMP.Model,
    g::ThermalUnit,
 )::Nothing
    is_on = model[:is_on]
    switch_off = model[:switch_off]
    switch_on = model[:switch_on]
@@ -271,53 +200,19 @@ function _add_min_uptime_downtime_eqs!(
    end
 end
-function _add_commitment_status_eqs!(model::JuMP.Model, g::ThermalUnit)::Nothing
+function _add_net_injection_eqs!(model::JuMP.Model, g::Unit)::Nothing
    is_on = model[:is_on]
    T = model[:instance].time
    eq_commitment_status = _init(model, :eq_commitment_status)
    for t in 1:T
        if g.commitment_status[t] !== nothing
            eq_commitment_status[g.name, t] = @constraint(
                model,
                is_on[g.name, t] == (g.commitment_status[t] ? 1.0 : 0.0)
            )
        end
    end
    return
 end
 function _add_net_injection_eqs!(
    model::JuMP.Model,
    g::ThermalUnit,
    sc::UnitCommitmentScenario,
 )::Nothing
    expr_net_injection = model[:expr_net_injection]
    for t in 1:model[:instance].time
        # Add to net injection expression
        add_to_expression!(
-            expr_net_injection[sc.name, g.bus.name, t],
+            expr_net_injection[g.bus.name, t],
-            model[:prod_above][sc.name, g.name, t],
+            model[:prod_above][g.name, t],
            1.0,
        )
        add_to_expression!(
-            expr_net_injection[sc.name, g.bus.name, t],
+            expr_net_injection[g.bus.name, t],
            model[:is_on][g.name, t],
            g.min_power[t],
        )
    end
 end
 function _total_reserves(model, g, sc)::Vector
    T = model[:instance].time
    reserve = [0.0 for _ in 1:T]
    spinning_reserves = [r for r in g.reserves if r.type == "spinning"]
    if !isempty(spinning_reserves)
        reserve += [
            sum(
                model[:reserve][sc.name, r.name, g.name, t] for
                r in spinning_reserves
            ) for t in 1:model[:instance].time
        ]
    end
    return reserve
 end
--- a/src/solution/fix.jl
+++ b/src/solution/fix.jl
@@ -10,42 +10,23 @@ solution. Useful for computing LMPs.
 """
 function fix!(model::JuMP.Model, solution::AbstractDict)::Nothing
    instance, T = model[:instance], model[:instance].time
    "Thermal production (MW)" ∈ keys(solution) ?
    solution = Dict("s1" => solution) : nothing
    is_on = model[:is_on]
    prod_above = model[:prod_above]
    reserve = model[:reserve]
-    for sc in instance.scenarios
+    for g in instance.units
-        for g in sc.thermal_units
+        for t in 1:T
-            for t in 1:T
+            is_on_value = round(solution["Is on"][g.name][t])
-                is_on_value = round(solution[sc.name]["Is on"][g.name][t])
+            prod_value =
-                prod_value = round(
+                round(solution["Production (MW)"][g.name][t], digits = 5)
-                    solution[sc.name]["Thermal production (MW)"][g.name][t],
+            reserve_value =
-                    digits = 5,
+                round(solution["Reserve (MW)"][g.name][t], digits = 5)
-                )
+            JuMP.fix(is_on[g.name, t], is_on_value, force = true)
-                JuMP.fix(is_on[g.name, t], is_on_value, force = true)
+            JuMP.fix(
-                JuMP.fix(
+                prod_above[g.name, t],
-                    prod_above[sc.name, g.name, t],
+                prod_value - is_on_value * g.min_power[t],
-                    prod_value - is_on_value * g.min_power[t],
+                force = true,
-                    force = true,
+            )
-                )
+            JuMP.fix(reserve[g.name, t], reserve_value, force = true)
            end
        end
        for r in sc.reserves
            r.type == "spinning" || continue
            for g in r.thermal_units
                for t in 1:T
                    reserve_value = round(
                        solution[sc.name]["Spinning reserve (MW)"][r.name][g.name][t],
                        digits = 5,
                    )
                    JuMP.fix(
                        reserve[sc.name, r.name, g.name, t],
                        reserve_value,
                        force = true,
                    )
                end
            end
        end
    end
    return
--- a/src/solution/methods/XavQiuWanThi2019/enforce.jl
+++ b/src/solution/methods/XavQiuWanThi2019/enforce.jl
@@ -5,15 +5,13 @@
 function _enforce_transmission(
    model::JuMP.Model,
    violations::Vector{_Violation},
    sc::UnitCommitmentScenario,
 )::Nothing
    for v in violations
        _enforce_transmission(
            model = model,
            sc = sc,
            violation = v,
-            isf = sc.isf,
+            isf = model[:isf],
-            lodf = sc.lodf,
+            lodf = model[:lodf],
        )
    end
    return
@@ -21,7 +19,6 @@ end
 function _enforce_transmission(;
    model::JuMP.Model,
    sc::UnitCommitmentScenario,
    violation::_Violation,
    isf::Matrix{Float64},
    lodf::Matrix{Float64},
@@ -34,21 +31,19 @@ function _enforce_transmission(;
    if violation.outage_line === nothing
        limit = violation.monitored_line.normal_flow_limit[violation.time]
        @info @sprintf(
-            "    %8.3f MW overflow in %-5s time %3d (pre-contingency, scenario %s)",
+            "    %8.3f MW overflow in %-5s time %3d (pre-contingency)",
            violation.amount,
            violation.monitored_line.name,
            violation.time,
            sc.name,
        )
    else
        limit = violation.monitored_line.emergency_flow_limit[violation.time]
        @info @sprintf(
-            "    %8.3f MW overflow in %-5s time %3d (outage: line %s, scenario %s)",
+            "    %8.3f MW overflow in %-5s time %3d (outage: line %s)",
            violation.amount,
            violation.monitored_line.name,
            violation.time,
            violation.outage_line.name,
            sc.name,
        )
    end
@@ -56,7 +51,7 @@ function _enforce_transmission(;
    t = violation.time
    flow = @variable(model, base_name = "flow[$fm,$t]")
-    v = overflow[sc.name, violation.monitored_line.name, violation.time]
+    v = overflow[violation.monitored_line.name, violation.time]
    @constraint(model, flow <= limit + v)
    @constraint(model, -flow <= limit + v)
@@ -64,23 +59,23 @@ function _enforce_transmission(;
        @constraint(
            model,
            flow == sum(
-                net_injection[sc.name, b.name, violation.time] *
+                net_injection[b.name, violation.time] *
                isf[violation.monitored_line.offset, b.offset] for
-                b in sc.buses if b.offset > 0
+                b in instance.buses if b.offset > 0
            )
        )
    else
        @constraint(
            model,
            flow == sum(
-                net_injection[sc.name, b.name, violation.time] * (
+                net_injection[b.name, violation.time] * (
                    isf[violation.monitored_line.offset, b.offset] + (
                        lodf[
                            violation.monitored_line.offset,
                            violation.outage_line.offset,
                        ] * isf[violation.outage_line.offset, b.offset]
                    )
-                ) for b in sc.buses if b.offset > 0
+                ) for b in instance.buses if b.offset > 0
            )
        )
    end
--- a/src/solution/methods/XavQiuWanThi2019/find.jl
+++ b/src/solution/methods/XavQiuWanThi2019/find.jl
@@ -5,35 +5,39 @@
 import Base.Threads: @threads
 function _find_violations(
-    model::JuMP.Model,
+    model::JuMP.Model;
    sc::UnitCommitmentScenario;
    max_per_line::Int,
    max_per_period::Int,
 )
    instance = model[:instance]
    net_injection = model[:net_injection]
    overflow = model[:overflow]
-    length(sc.buses) > 1 || return []
+    length(instance.buses) > 1 || return []
    violations = []
-
+    @info "Verifying transmission limits..."
-    non_slack_buses = [b for b in sc.buses if b.offset > 0]
+    time_screening = @elapsed begin
-    net_injection_values = [
+        non_slack_buses = [b for b in instance.buses if b.offset > 0]
-        value(net_injection[sc.name, b.name, t]) for b in non_slack_buses,
+        net_injection_values = [
-        t in 1:instance.time
+            value(net_injection[b.name, t]) for b in non_slack_buses,
-    ]
+            t in 1:instance.time
-    overflow_values = [
+        ]
-        value(overflow[sc.name, lm.name, t]) for lm in sc.lines,
+        overflow_values = [
-        t in 1:instance.time
+            value(overflow[lm.name, t]) for lm in instance.lines,
-    ]
+            t in 1:instance.time
-    violations = UnitCommitment._find_violations(
+        ]
-        instance = instance,
+        violations = UnitCommitment._find_violations(
-        sc = sc,
+            instance = instance,
-        net_injections = net_injection_values,
+            net_injections = net_injection_values,
-        overflow = overflow_values,
+            overflow = overflow_values,
-        isf = sc.isf,
+            isf = model[:isf],
-        lodf = sc.lodf,
+            lodf = model[:lodf],
-        max_per_line = max_per_line,
+            max_per_line = max_per_line,
-        max_per_period = max_per_period,
+            max_per_period = max_per_period,
        )
    end
    @info @sprintf(
        "Verified transmission limits in %.2f seconds",
        time_screening
    )
    return violations
 end
@@ -60,7 +64,6 @@ matrix, where L is the number of transmission lines.
 """
 function _find_violations(;
    instance::UnitCommitmentInstance,
    sc::UnitCommitmentScenario,
    net_injections::Array{Float64,2},
    overflow::Array{Float64,2},
    isf::Array{Float64,2},
@@ -68,8 +71,8 @@ function _find_violations(;
    max_per_line::Int,
    max_per_period::Int,
 )::Array{_Violation,1}
-    B = length(sc.buses) - 1
+    B = length(instance.buses) - 1
-    L = length(sc.lines)
+    L = length(instance.lines)
    T = instance.time
    K = nthreads()
@@ -90,17 +93,17 @@ function _find_violations(;
    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 sc.lines,
+        l.normal_flow_limit[t] + overflow[l.offset, t] for
-        t in 1:T
+        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 sc.lines,
+        l.emergency_flow_limit[t] + overflow[l.offset, t] for
-        t in 1:T
+        l in instance.lines, t in 1:T
    ]
    is_vulnerable::Array{Bool} = zeros(Bool, L)
-    for c in sc.contingencies
+    for c in instance.contingencies
        is_vulnerable[c.lines[1].offset] = true
    end
@@ -141,7 +144,7 @@ function _find_violations(;
                    filters[t],
                    _Violation(
                        time = t,
-                        monitored_line = sc.lines[lm],
+                        monitored_line = instance.lines[lm],
                        outage_line = nothing,
                        amount = pre_v[lm, k],
                    ),
@@ -156,8 +159,8 @@ function _find_violations(;
                    filters[t],
                    _Violation(
                        time = t,
-                        monitored_line = sc.lines[lm],
+                        monitored_line = instance.lines[lm],
-                        outage_line = sc.lines[lc],
+                        outage_line = instance.lines[lc],
                        amount = post_v[lm, lc, k],
                    ),
                )
--- a/src/solution/methods/XavQiuWanThi2019/optimize.jl
+++ b/src/solution/methods/XavQiuWanThi2019/optimize.jl
@@ -3,24 +3,22 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 function optimize!(model::JuMP.Model, method::XavQiuWanThi2019.Method)::Nothing
    if !occursin("Gurobi", JuMP.solver_name(model))
        method.two_phase_gap = false
    end
    function set_gap(gap)
-        JuMP.set_optimizer_attribute(model, "MIPGap", gap)
+        try
-        @info @sprintf("MIP gap tolerance set to %f", gap)
+            JuMP.set_optimizer_attribute(model, "MIPGap", gap)
            @info @sprintf("MIP gap tolerance set to %f", gap)
        catch
            @warn "Could not change MIP gap tolerance"
        end
    end
    initial_time = time()
    large_gap = false
-    has_transmission = false
+    has_transmission = (length(model[:isf]) > 0)
-    for sc in model[:instance].scenarios
+    if has_transmission && method.two_phase_gap
-        if length(sc.isf) > 0
+        set_gap(1e-2)
-            has_transmission = true
+        large_gap = true
-        end
+    else
-        if has_transmission && method.two_phase_gap
+        set_gap(method.gap_limit)
            set_gap(1e-2)
            large_gap = true
        end
    end
    while true
        time_elapsed = time() - initial_time
@@ -36,41 +34,13 @@ function optimize!(model::JuMP.Model, method::XavQiuWanThi2019.Method)::Nothing
        JuMP.set_time_limit_sec(model, time_remaining)
        @info "Solving MILP..."
        JuMP.optimize!(model)
        has_transmission || break
-
+        violations = _find_violations(
-        @info "Verifying transmission limits..."
+            model,
-        time_screening = @elapsed begin
+            max_per_line = method.max_violations_per_line,
-            violations = []
+            max_per_period = method.max_violations_per_period,
            for sc in model[:instance].scenarios
                push!(
                    violations,
                    _find_violations(
                        model,
                        sc,
                        max_per_line = method.max_violations_per_line,
                        max_per_period = method.max_violations_per_period,
                    ),
                )
            end
        end
        @info @sprintf(
            "Verified transmission limits in %.2f seconds",
            time_screening
        )
-
+        if isempty(violations)
        violations_found = false
        for v in violations
            if !isempty(v)
                violations_found = true
            end
        end
        if violations_found
            for (i, v) in enumerate(violations)
                _enforce_transmission(model, v, model[:instance].scenarios[i])
            end
        else
            @info "No violations found"
            if large_gap
                large_gap = false
@@ -78,6 +48,8 @@ function optimize!(model::JuMP.Model, method::XavQiuWanThi2019.Method)::Nothing
            else
                break
            end
        else
            _enforce_transmission(model, violations)
        end
    end
    return
--- a/src/solution/methods/XavQiuWanThi2019/structs.jl
+++ b/src/solution/methods/XavQiuWanThi2019/structs.jl
@@ -2,10 +2,18 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 """
 Lazy constraint solution method described in:
    Xavier, A. S., Qiu, F., Wang, F., & Thimmapuram, P. R. (2019). Transmission
    constraint filtering in large-scale security-constrained unit commitment. 
    IEEE Transactions on Power Systems, 34(3), 2457-2460.
    DOI: https://doi.org/10.1109/TPWRS.2019.2892620
 """
 module XavQiuWanThi2019
 import ..SolutionMethod
 """
-    mutable struct Method
+    struct Method
        time_limit::Float64
        gap_limit::Float64
        two_phase_gap::Bool
@@ -13,20 +21,13 @@ import ..SolutionMethod
        max_violations_per_period::Int
    end
 Lazy constraint solution method described in:
    Xavier, A. S., Qiu, F., Wang, F., & Thimmapuram, P. R. (2019). Transmission
    constraint filtering in large-scale security-constrained unit commitment. 
    IEEE Transactions on Power Systems, 34(3), 2457-2460.
    DOI: https://doi.org/10.1109/TPWRS.2019.2892620
 Fields
 ------
 - `time_limit`:
    the time limit over the entire optimization procedure.
 - `gap_limit`: 
-    the desired relative optimality gap. Only used when `two_phase_gap=true`.
+    the desired relative optimality gap.
 - `two_phase_gap`: 
    if true, solve the problem with large gap tolerance first, then reduce
    the gap tolerance when no further violated constraints are found.
@@ -38,7 +39,7 @@ Fields
    formulation per time period.
 """
-mutable struct Method <: SolutionMethod
+struct Method <: SolutionMethod
    time_limit::Float64
    gap_limit::Float64
    two_phase_gap::Bool
--- a/src/solution/optimize.jl
+++ b/src/solution/optimize.jl
@@ -3,9 +3,9 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 """
-    optimize!(model::JuMP.Model)::Nothing
+    function optimize!(model::JuMP.Model)::Nothing
-Solve the given unit commitment model. Unlike `JuMP.optimize!`, this uses more
+Solve the given unit commitment model. Unlike JuMP.optimize!, this uses more
 advanced methods to accelerate the solution process and to enforce transmission
 and N-1 security constraints.
 """
--- a/src/solution/solution.jl
+++ b/src/solution/solution.jl
@@ -2,58 +2,36 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 """
    solution(model::JuMP.Model)::OrderedDict
 Extracts the optimal solution from the UC.jl model. The model must be solved beforehand.
 # Example
 ```julia
 UnitCommitment.optimize!(model)
 solution = UnitCommitment.solution(model)
 ```
 """
 function solution(model::JuMP.Model)::OrderedDict
    instance, T = model[:instance], model[:instance].time
-    function timeseries(vars, collection; sc = nothing)
+    function timeseries(vars, collection)
-        if sc === nothing
+        return OrderedDict(
-            return OrderedDict(
+            b.name => [round(value(vars[b.name, t]), digits = 5) for t in 1:T]
-                b.name =>
+            for b in collection
-                    [round(value(vars[b.name, t]), digits = 5) for t in 1:T] for
+        )
                b in collection
            )
        else
            return OrderedDict(
                b.name => [
                    round(value(vars[sc.name, b.name, t]), digits = 5) for
                    t in 1:T
                ] for b in collection
            )
        end
    end
-    function production_cost(g, sc)
+    function production_cost(g)
        return [
            value(model[:is_on][g.name, t]) * g.min_power_cost[t] + sum(
                Float64[
-                    value(model[:segprod][sc.name, g.name, t, k]) *
+                    value(model[:segprod][g.name, t, k]) *
                    g.cost_segments[k].cost[t] for
                    k in 1:length(g.cost_segments)
                ],
            ) for t in 1:T
        ]
    end
-    function production(g, sc)
+    function production(g)
        return [
            value(model[:is_on][g.name, t]) * g.min_power[t] + sum(
                Float64[
-                    value(model[:segprod][sc.name, g.name, t, k]) for
+                    value(model[:segprod][g.name, t, k]) for
                    k in 1:length(g.cost_segments)
                ],
            ) for t in 1:T
        ]
    end
-    function startup_cost(g, sc)
+    function startup_cost(g)
        S = length(g.startup_categories)
        return [
            sum(
@@ -63,87 +41,31 @@ function solution(model::JuMP.Model)::OrderedDict
        ]
    end
    sol = OrderedDict()
-    for sc in instance.scenarios
+    sol["Production (MW)"] =
-        sol[sc.name] = OrderedDict()
+        OrderedDict(g.name => production(g) for g in instance.units)
-        if !isempty(sc.thermal_units)
+    sol["Production cost (\$)"] =
-            sol[sc.name]["Thermal production (MW)"] = OrderedDict(
+        OrderedDict(g.name => production_cost(g) for g in instance.units)
-                g.name => production(g, sc) for g in sc.thermal_units
+    sol["Startup cost (\$)"] =
-            )
+        OrderedDict(g.name => startup_cost(g) for g in instance.units)
-            sol[sc.name]["Thermal production cost (\$)"] = OrderedDict(
+    sol["Is on"] = timeseries(model[:is_on], instance.units)
-                g.name => production_cost(g, sc) for g in sc.thermal_units
+    sol["Switch on"] = timeseries(model[:switch_on], instance.units)
-            )
+    sol["Switch off"] = timeseries(model[:switch_off], instance.units)
-            sol[sc.name]["Startup cost (\$)"] = OrderedDict(
+    sol["Reserve (MW)"] = timeseries(model[:reserve], instance.units)
-                g.name => startup_cost(g, sc) for g in sc.thermal_units
+    sol["Reserve shortfall (MW)"] = OrderedDict(
-            )
+        t =>
-            sol[sc.name]["Is on"] = timeseries(model[:is_on], sc.thermal_units)
+            (instance.shortfall_penalty[t] >= 0) ?
-            sol[sc.name]["Switch on"] =
+            round(value(model[:reserve_shortfall][t]), digits = 5) : 0.0 for
-                timeseries(model[:switch_on], sc.thermal_units)
+        t in 1:instance.time
-            sol[sc.name]["Switch off"] =
+    )
-                timeseries(model[:switch_off], sc.thermal_units)
+    sol["Net injection (MW)"] =
-            sol[sc.name]["Net injection (MW)"] =
+        timeseries(model[:net_injection], instance.buses)
-                timeseries(model[:net_injection], sc.buses, sc = sc)
+    sol["Load curtail (MW)"] = timeseries(model[:curtail], instance.buses)
-            sol[sc.name]["Load curtail (MW)"] =
+    if !isempty(instance.lines)
-                timeseries(model[:curtail], sc.buses, sc = sc)
+        sol["Line overflow (MW)"] = timeseries(model[:overflow], instance.lines)
        end
        if !isempty(sc.lines)
            sol[sc.name]["Line overflow (MW)"] =
                timeseries(model[:overflow], sc.lines, sc = sc)
        end
        if !isempty(sc.price_sensitive_loads)
            sol[sc.name]["Price-sensitive loads (MW)"] =
                timeseries(model[:loads], sc.price_sensitive_loads, sc = sc)
        end
        if !isempty(sc.profiled_units)
            sol[sc.name]["Profiled production (MW)"] =
                timeseries(model[:prod_profiled], sc.profiled_units, sc = sc)
            sol[sc.name]["Profiled production cost (\$)"] = OrderedDict(
                pu.name => [
                    value(model[:prod_profiled][sc.name, pu.name, t]) *
                    pu.cost[t] for t in 1:instance.time
                ] for pu in sc.profiled_units
            )
        end
        sol[sc.name]["Spinning reserve (MW)"] = OrderedDict(
            r.name => OrderedDict(
                g.name => [
                    value(model[:reserve][sc.name, r.name, g.name, t]) for t in 1:instance.time
                ] for g in r.thermal_units
            ) for r in sc.reserves if r.type == "spinning"
        )
        sol[sc.name]["Spinning reserve shortfall (MW)"] = OrderedDict(
            r.name => [
                value(model[:reserve_shortfall][sc.name, r.name, t]) for
                t in 1:instance.time
            ] for r in sc.reserves if r.type == "spinning"
        )
        sol[sc.name]["Up-flexiramp (MW)"] = OrderedDict(
            r.name => OrderedDict(
                g.name => [
                    value(model[:upflexiramp][sc.name, r.name, g.name, t]) for t in 1:instance.time
                ] for g in r.thermal_units
            ) for r in sc.reserves if r.type == "flexiramp"
        )
        sol[sc.name]["Up-flexiramp shortfall (MW)"] = OrderedDict(
            r.name => [
                value(model[:upflexiramp_shortfall][sc.name, r.name, t]) for t in 1:instance.time
            ] for r in sc.reserves if r.type == "flexiramp"
        )
        sol[sc.name]["Down-flexiramp (MW)"] = OrderedDict(
            r.name => OrderedDict(
                g.name => [
                    value(model[:dwflexiramp][sc.name, r.name, g.name, t]) for t in 1:instance.time
                ] for g in r.thermal_units
            ) for r in sc.reserves if r.type == "flexiramp"
        )
        sol[sc.name]["Down-flexiramp shortfall (MW)"] = OrderedDict(
            r.name => [
                value(model[:dwflexiramp_shortfall][sc.name, r.name, t]) for t in 1:instance.time
            ] for r in sc.reserves if r.type == "flexiramp"
        )
    end
-    if length(instance.scenarios) == 1
+    if !isempty(instance.price_sensitive_loads)
-        return first(values(sol))
+        sol["Price-sensitive loads (MW)"] =
-    else
+            timeseries(model[:loads], instance.price_sensitive_loads)
        return sol
    end
    return sol
 end
--- a/src/solution/warmstart.jl
+++ b/src/solution/warmstart.jl
@@ -5,7 +5,7 @@
 function set_warm_start!(model::JuMP.Model, solution::AbstractDict)::Nothing
    instance, T = model[:instance], model[:instance].time
    is_on = model[:is_on]
-    for g in instance.thermal_units
+    for g in instance.units
        for t in 1:T
            JuMP.set_start_value(is_on[g.name, t], solution["Is on"][g.name][t])
            JuMP.set_start_value(
--- a/src/solution/write.jl
+++ b/src/solution/write.jl
@@ -2,18 +2,6 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 """
    write(filename::AbstractString, solution::AbstractDict)::Nothing
 Write the given solution to a JSON file.
 # Example
 ```julia
 solution = UnitCommitment.solution(model)
 UnitCommitment.write("/tmp/output.json", solution)
 ```
 """
 function write(filename::AbstractString, solution::AbstractDict)::Nothing
    open(filename, "w") do file
        return JSON.print(file, solution, 2)
--- a/src/transform/initcond.jl
+++ b/src/transform/initcond.jl
@@ -5,41 +5,36 @@
 using JuMP
 """
-    generate_initial_conditions!(sc, optimizer)
+    generate_initial_conditions!(instance, optimizer)
-Generates feasible initial conditions for the given scenario, by constructing
+Generates feasible initial conditions for the given instance, by constructing
 and solving a single-period mixed-integer optimization problem, using the given
-optimizer. The scenario is modified in-place.
+optimizer. The instance is modified in-place.
 """
 function generate_initial_conditions!(
-    sc::UnitCommitmentScenario,
+    instance::UnitCommitmentInstance,
    optimizer,
 )::Nothing
-    G = sc.thermal_units
+    G = instance.units
-    B = sc.buses
+    B = instance.buses
    PU = sc.profiled_units
    t = 1
    mip = JuMP.Model(optimizer)
    # Decision variables
    @variable(mip, x[G], Bin)
    @variable(mip, p[G] >= 0)
    @variable(mip, pu[PU])
    # Constraint: Minimum power
    @constraint(mip, min_power[g in G], p[g] >= g.min_power[t] * x[g])
    @constraint(mip, pu_min_power[k in PU], pu[k] >= k.min_power[t])
    # Constraint: Maximum power
    @constraint(mip, max_power[g in G], p[g] <= g.max_power[t] * x[g])
    @constraint(mip, pu_max_power[k in PU], pu[k] <= k.max_power[t])
    # Constraint: Production equals demand
    @constraint(
        mip,
        power_balance,
-        sum(b.load[t] for b in B) ==
+        sum(b.load[t] for b in B) == sum(p[g] for g in G)
        sum(p[g] for g in G) + sum(pu[k] for k in PU)
    )
    # Constraint: Must run
@@ -63,12 +58,7 @@ function generate_initial_conditions!(
            return c / mw
        end
    end
-    @objective(
+    @objective(mip, Min, sum(p[g] * cost_slope(g) for g in G))
        mip,
        Min,
        sum(p[g] * cost_slope(g) for g in G) +
        sum(pu[k] * k.cost[t] for k in PU)
    )
    JuMP.optimize!(mip)
--- a/src/transform/randomize/XavQiuAhm2021.jl
+++ b/src/transform/randomize/XavQiuAhm2021.jl
@@ -2,11 +2,17 @@
 # Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 """
 Methods described in:
    Xavier, Álinson S., Feng Qiu, and Shabbir Ahmed. "Learning to solve
    large-scale security-constrained unit commitment problems." INFORMS
    Journal on Computing 33.2 (2021): 739-756. DOI: 10.1287/ijoc.2020.0976
 """
 module XavQiuAhm2021
 using Distributions
 import ..UnitCommitmentInstance
 import ..UnitCommitmentScenario
 """
    struct Randomization
@@ -49,13 +55,6 @@ load profile, as follows:
 The default parameters were obtained based on an analysis of publicly available
 bid and hourly data from PJM, corresponding to the month of January, 2017. For
 more details, see Section 4.2 of the paper.
 # References
 - **Xavier, Álinson S., Feng Qiu, and Shabbir Ahmed.** *"Learning to solve
  large-scale security-constrained unit commitment problems."* INFORMS Journal
  on Computing 33.2 (2021): 739-756. DOI: 10.1287/ijoc.2020.0976
 """
 Base.@kwdef struct Randomization
    cost = Uniform(0.95, 1.05)
@@ -119,12 +118,11 @@ Base.@kwdef struct Randomization
 end
 function _randomize_costs(
-    rng,
+    instance::UnitCommitmentInstance,
    sc::UnitCommitmentScenario,
    distribution,
 )::Nothing
-    for unit in sc.thermal_units
+    for unit in instance.units
-        α = rand(rng, distribution)
+        α = rand(distribution)
        unit.min_power_cost *= α
        for k in unit.cost_segments
            k.cost *= α
@@ -133,24 +131,21 @@ function _randomize_costs(
            s.cost *= α
        end
    end
    for pu in sc.profiled_units
        α = rand(rng, distribution)
        pu.cost *= α
    end
    return
 end
 function _randomize_load_share(
-    rng,
+    instance::UnitCommitmentInstance,
    sc::UnitCommitmentScenario,
    distribution,
 )::Nothing
-    α = rand(rng, distribution, length(sc.buses))
+    α = rand(distribution, length(instance.buses))
-    for t in 1:sc.time
+    for t in 1:instance.time
-        total = sum(bus.load[t] for bus in sc.buses)
+        total = sum(bus.load[t] for bus in instance.buses)
-        den =
+        den = sum(
-            sum(bus.load[t] / total * α[i] for (i, bus) in enumerate(sc.buses))
+            bus.load[t] / total * α[i] for
-        for (i, bus) in enumerate(sc.buses)
+            (i, bus) in enumerate(instance.buses)
        )
        for (i, bus) in enumerate(instance.buses)
            bus.load[t] *= α[i] / den
        end
    end
@@ -158,28 +153,26 @@ function _randomize_load_share(
 end
 function _randomize_load_profile(
-    rng,
+    instance::UnitCommitmentInstance,
    sc::UnitCommitmentScenario,
    params::Randomization,
 )::Nothing
    # Generate new system load
    system_load = [1.0]
-    for t in 2:sc.time
+    for t in 2:instance.time
        idx = (t - 1) % length(params.load_profile_mu) + 1
        gamma = rand(
            rng,
            Normal(params.load_profile_mu[idx], params.load_profile_sigma[idx]),
        )
        push!(system_load, system_load[t-1] * gamma)
    end
-    capacity = sum(maximum(u.max_power) for u in sc.thermal_units)
+    capacity = sum(maximum(u.max_power) for u in instance.units)
-    peak_load = rand(rng, params.peak_load) * capacity
+    peak_load = rand(params.peak_load) * capacity
    system_load = system_load ./ maximum(system_load) .* peak_load
    # Scale bus loads to match the new system load
-    prev_system_load = sum(b.load for b in sc.buses)
+    prev_system_load = sum(b.load for b in instance.buses)
-    for b in sc.buses
+    for b in instance.buses
-        for t in 1:sc.time
+        for t in 1:instance.time
            b.load[t] *= system_load[t] / prev_system_load[t]
        end
    end
@@ -193,64 +186,24 @@ end
    function randomize!(
        instance::UnitCommitment.UnitCommitmentInstance,
        method::XavQiuAhm2021.Randomization,
        rng = MersenneTwister(),
    )::Nothing
 Randomize costs and loads based on the method described in XavQiuAhm2021.
 """
 function randomize!(
    instance::UnitCommitment.UnitCommitmentInstance,
-    method::XavQiuAhm2021.Randomization;
+    method::XavQiuAhm2021.Randomization,
    rng = MersenneTwister(),
 )::Nothing
    for sc in instance.scenarios
        randomize!(sc, method; rng)
    end
    return
 end
 function randomize!(
    sc::UnitCommitment.UnitCommitmentScenario,
    method::XavQiuAhm2021.Randomization;
    rng = MersenneTwister(),
 )::Nothing
    if method.randomize_costs
-        XavQiuAhm2021._randomize_costs(rng, sc, method.cost)
+        XavQiuAhm2021._randomize_costs(instance, method.cost)
    end
    if method.randomize_load_share
-        XavQiuAhm2021._randomize_load_share(rng, sc, method.load_share)
+        XavQiuAhm2021._randomize_load_share(instance, method.load_share)
    end
    if method.randomize_load_profile
-        XavQiuAhm2021._randomize_load_profile(rng, sc, method)
+        XavQiuAhm2021._randomize_load_profile(instance, method)
    end
    return
 end
 """
    function randomize!(
        instance::UnitCommitmentInstance;
        method = UnitCommitment.XavQiuAhm2021.Randomization();
        rng = MersenneTwister(),
    )::Nothing
 Randomizes instance parameters according to the provided randomization method.
 # Example
 ```julia
 instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
 UnitCommitment.randomize!(instance)
 model = UnitCommitment.build_model(; instance)
 ```
 """
 function randomize!(
    instance::UnitCommitment.UnitCommitmentInstance;
    method = XavQiuAhm2021.Randomization(),
    rng = MersenneTwister(),
 )::Nothing
    randomize!(instance, method; rng)
    return
 end
 export randomize!
--- a/src/transform/slice.jl
+++ b/src/transform/slice.jl
@@ -12,11 +12,10 @@ conditions are also not modified.
 Example
 -------
-```julia
+    # Build a 2-hour UC instance
-# Build a 2-hour UC instance
+    instance = UnitCommitment.read_benchmark("test/case14")
-instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
+    modified = UnitCommitment.slice(instance, 1:2)
-modified = UnitCommitment.slice(instance, 1:2)
+
 ```
 """
 function slice(
    instance::UnitCommitmentInstance,
@@ -24,38 +23,30 @@ function slice(
 )::UnitCommitmentInstance
    modified = deepcopy(instance)
    modified.time = length(range)
-    for sc in modified.scenarios
+    modified.power_balance_penalty = modified.power_balance_penalty[range]
-        sc.power_balance_penalty = sc.power_balance_penalty[range]
+    modified.reserves.spinning = modified.reserves.spinning[range]
-        for r in sc.reserves
+    for u in modified.units
-            r.amount = r.amount[range]
+        u.max_power = u.max_power[range]
-        end
+        u.min_power = u.min_power[range]
-        for u in sc.thermal_units
+        u.must_run = u.must_run[range]
-            u.max_power = u.max_power[range]
+        u.min_power_cost = u.min_power_cost[range]
-            u.min_power = u.min_power[range]
+        u.provides_spinning_reserves = u.provides_spinning_reserves[range]
-            u.must_run = u.must_run[range]
+        for s in u.cost_segments
-            u.min_power_cost = u.min_power_cost[range]
+            s.mw = s.mw[range]
-            for s in u.cost_segments
+            s.cost = s.cost[range]
                s.mw = s.mw[range]
                s.cost = s.cost[range]
            end
        end
        for pu in sc.profiled_units
            pu.max_power = pu.max_power[range]
            pu.min_power = pu.min_power[range]
            pu.cost = pu.cost[range]
        end
        for b in sc.buses
            b.load = b.load[range]
        end
        for l in sc.lines
            l.normal_flow_limit = l.normal_flow_limit[range]
            l.emergency_flow_limit = l.emergency_flow_limit[range]
            l.flow_limit_penalty = l.flow_limit_penalty[range]
        end
        for ps in sc.price_sensitive_loads
            ps.demand = ps.demand[range]
            ps.revenue = ps.revenue[range]
        end
    end
    for b in modified.buses
        b.load = b.load[range]
    end
    for l in modified.lines
        l.normal_flow_limit = l.normal_flow_limit[range]
        l.emergency_flow_limit = l.emergency_flow_limit[range]
        l.flow_limit_penalty = l.flow_limit_penalty[range]
    end
    for ps in modified.price_sensitive_loads
        ps.demand = ps.demand[range]
        ps.revenue = ps.revenue[range]
    end
    return modified
 end
--- a/src/utils/log.jl
+++ b/src/utils/log.jl
@@ -5,11 +5,20 @@
 import Logging: min_enabled_level, shouldlog, handle_message
 using Base.CoreLogging, Logging, Printf
-Base.@kwdef struct TimeLogger <: AbstractLogger
+struct TimeLogger <: AbstractLogger
    initial_time::Float64
-    file::Union{Nothing,IOStream} = nothing
+    file::Union{Nothing,IOStream}
-    screen_log_level::Any = CoreLogging.Info
+    screen_log_level::Any
-    io_log_level::Any = CoreLogging.Info
+    io_log_level::Any
 end
 function TimeLogger(;
    initial_time::Float64,
    file::Union{Nothing,IOStream} = nothing,
    screen_log_level = CoreLogging.Info,
    io_log_level = CoreLogging.Info,
 )::TimeLogger
    return TimeLogger(initial_time, file, screen_log_level, io_log_level)
 end
 min_enabled_level(logger::TimeLogger) = logger.io_log_level
@@ -52,9 +61,7 @@ function handle_message(
    end
 end
-function _setup_logger(; level = CoreLogging.Info)
+function _setup_logger()
    initial_time = time()
-    return global_logger(
+    return global_logger(TimeLogger(initial_time = initial_time))
        TimeLogger(initial_time = initial_time, screen_log_level = level),
    )
 end
--- a/src/validation/repair.jl
+++ b/src/validation/repair.jl
@@ -3,19 +3,19 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 """
-    repair!(sc)
+    repair!(instance)
-Verifies that the given unit commitment scenario is valid and automatically
+Verifies that the given unit commitment instance is valid and automatically
 fixes some validation errors if possible, issuing a warning for each error
 found. If a validation error cannot be automatically fixed, issues an
 exception.
 Returns the number of validation errors found.
 """
-function repair!(sc::UnitCommitmentScenario)::Int
+function repair!(instance::UnitCommitmentInstance)::Int
    n_errors = 0
-    for g in sc.thermal_units
+    for g in instance.units
        # Startup costs and delays must be increasing
        for s in 2:length(g.startup_categories)
@@ -38,7 +38,7 @@ function repair!(sc::UnitCommitmentScenario)::Int
            end
        end
-        for t in 1:sc.time
+        for t in 1:instance.time
            # Production cost curve should be convex
            for k in 2:length(g.cost_segments)
                cost = g.cost_segments[k].cost[t]
--- a/src/validation/validate.jl
+++ b/src/validation/validate.jl
@@ -28,8 +28,6 @@ function validate(
    instance::UnitCommitmentInstance,
    solution::Union{Dict,OrderedDict},
 )::Bool
    "Thermal production (MW)" ∈ keys(solution) ?
    solution = Dict("s1" => solution) : nothing
    err_count = 0
    err_count += _validate_units(instance, solution)
    err_count += _validate_reserve_and_demand(instance, solution)
@@ -42,410 +40,303 @@ function validate(
    return true
 end
-function _validate_units(instance::UnitCommitmentInstance, solution; tol = 0.01)
+function _validate_units(instance, solution; tol = 0.01)
    err_count = 0
    for sc in instance.scenarios
        for unit in sc.thermal_units
            production = solution[sc.name]["Thermal production (MW)"][unit.name]
            reserve = [0.0 for _ in 1:instance.time]
            spinning_reserves =
                [r for r in unit.reserves if r.type == "spinning"]
            if !isempty(spinning_reserves)
                reserve += sum(
                    solution[sc.name]["Spinning reserve (MW)"][r.name][unit.name]
                    for r in spinning_reserves
                )
            end
            actual_production_cost =
                solution[sc.name]["Thermal production cost (\$)"][unit.name]
            actual_startup_cost =
                solution[sc.name]["Startup cost (\$)"][unit.name]
            is_on = bin(solution[sc.name]["Is on"][unit.name])
-            for t in 1:instance.time
+    for unit in instance.units
-                # Auxiliary variables
+        production = solution["Production (MW)"][unit.name]
-                if t == 1
+        reserve = solution["Reserve (MW)"][unit.name]
-                    is_starting_up = (unit.initial_status < 0) && is_on[t]
+        actual_production_cost = solution["Production cost (\$)"][unit.name]
-                    is_shutting_down = (unit.initial_status > 0) && !is_on[t]
+        actual_startup_cost = solution["Startup cost (\$)"][unit.name]
-                    ramp_up =
+        is_on = bin(solution["Is on"][unit.name])
-                        max(0, production[t] + reserve[t] - unit.initial_power)
+
-                    ramp_down = max(0, unit.initial_power - production[t])
+        for t in 1:instance.time
-                else
+            # Auxiliary variables
-                    is_starting_up = !is_on[t-1] && is_on[t]
+            if t == 1
-                    is_shutting_down = is_on[t-1] && !is_on[t]
+                is_starting_up = (unit.initial_status < 0) && is_on[t]
-                    ramp_up =
+                is_shutting_down = (unit.initial_status > 0) && !is_on[t]
-                        max(0, production[t] + reserve[t] - production[t-1])
+                ramp_up =
-                    ramp_down = max(0, production[t-1] - production[t])
+                    max(0, production[t] + reserve[t] - unit.initial_power)
                ramp_down = max(0, unit.initial_power - production[t])
            else
                is_starting_up = !is_on[t-1] && is_on[t]
                is_shutting_down = is_on[t-1] && !is_on[t]
                ramp_up = max(0, production[t] + reserve[t] - production[t-1])
                ramp_down = max(0, production[t-1] - production[t])
            end
            # Compute production costs
            production_cost, startup_cost = 0, 0
            if is_on[t]
                production_cost += unit.min_power_cost[t]
                residual = max(0, production[t] - unit.min_power[t])
                for s in unit.cost_segments
                    cleared = min(residual, s.mw[t])
                    production_cost += cleared * s.cost[t]
                    residual = max(0, residual - s.mw[t])
                end
            end
            # Production should be non-negative
            if production[t] < -tol
                @error @sprintf(
                    "Unit %s produces negative amount of power at time %d (%.2f)",
                    unit.name,
                    t,
                    production[t]
                )
                err_count += 1
            end
            # Verify must-run
            if !is_on[t] && unit.must_run[t]
                @error @sprintf(
                    "Must-run unit %s is offline at time %d",
                    unit.name,
                    t
                )
                err_count += 1
            end
            # Verify reserve eligibility
            if !unit.provides_spinning_reserves[t] && reserve[t] > tol
                @error @sprintf(
                    "Unit %s is not eligible to provide spinning reserves at time %d",
                    unit.name,
                    t
                )
                err_count += 1
            end
            # If unit is on, must produce at least its minimum power
            if is_on[t] && (production[t] < unit.min_power[t] - tol)
                @error @sprintf(
                    "Unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
                    unit.name,
                    t,
                    production[t],
                    unit.min_power[t]
                )
                err_count += 1
            end
            # If unit is on, must produce at most its maximum power
            if is_on[t] &&
               (production[t] + reserve[t] > unit.max_power[t] + tol)
                @error @sprintf(
                    "Unit %s produces above its maximum limit at time %d (%.2f + %.2f> %.2f)",
                    unit.name,
                    t,
                    production[t],
                    reserve[t],
                    unit.max_power[t]
                )
                err_count += 1
            end
            # If unit is off, must produce zero
            if !is_on[t] && production[t] + reserve[t] > tol
                @error @sprintf(
                    "Unit %s produces power at time %d while off",
                    unit.name,
                    t
                )
                err_count += 1
            end
            # Startup limit
            if is_starting_up && (ramp_up > unit.startup_limit + tol)
                @error @sprintf(
                    "Unit %s exceeds startup limit at time %d (%.2f > %.2f)",
                    unit.name,
                    t,
                    ramp_up,
                    unit.startup_limit
                )
                err_count += 1
            end
            # Shutdown limit
            if is_shutting_down && (ramp_down > unit.shutdown_limit + tol)
                @error @sprintf(
                    "Unit %s exceeds shutdown limit at time %d (%.2f > %.2f)",
                    unit.name,
                    t,
                    ramp_down,
                    unit.shutdown_limit
                )
                err_count += 1
            end
            # Ramp-up limit
            if !is_starting_up &&
               !is_shutting_down &&
               (ramp_up > unit.ramp_up_limit + tol)
                @error @sprintf(
                    "Unit %s exceeds ramp up limit at time %d (%.2f > %.2f)",
                    unit.name,
                    t,
                    ramp_up,
                    unit.ramp_up_limit
                )
                err_count += 1
            end
            # Ramp-down limit
            if !is_starting_up &&
               !is_shutting_down &&
               (ramp_down > unit.ramp_down_limit + tol)
                @error @sprintf(
                    "Unit %s exceeds ramp down limit at time %d (%.2f > %.2f)",
                    unit.name,
                    t,
                    ramp_down,
                    unit.ramp_down_limit
                )
                err_count += 1
            end
            # Verify startup costs & minimum downtime
            if is_starting_up
                # Calculate how much time the unit has been offline
                time_down = 0
                for k in 1:(t-1)
                    if !is_on[t-k]
                        time_down += 1
                    else
                        break
                    end
                end
                if (t == time_down + 1) && (unit.initial_status < 0)
                    time_down -= unit.initial_status
                end
-                # Compute production costs
+                # Calculate startup costs
-                production_cost, startup_cost = 0, 0
+                for c in unit.startup_categories
-                if is_on[t]
+                    if time_down >= c.delay
-                    production_cost += unit.min_power_cost[t]
+                        startup_cost = c.cost
                    residual = max(0, production[t] - unit.min_power[t])
                    for s in unit.cost_segments
                        cleared = min(residual, s.mw[t])
                        production_cost += cleared * s.cost[t]
                        residual = max(0, residual - s.mw[t])
                    end
                end
-                # Production should be non-negative
+                # Check minimum downtime
-                if production[t] < -tol
+                if time_down < unit.min_downtime
                    @error @sprintf(
-                        "Unit %s produces negative amount of power at time %d (%.2f)",
+                        "Unit %s violates minimum downtime at time %d",
                        unit.name,
                        t,
                        production[t]
                    )
                    err_count += 1
                end
                # Verify must-run
                if !is_on[t] && unit.must_run[t]
                    @error @sprintf(
                        "Must-run unit %s is offline at time %d",
                        unit.name,
                        t
                    )
                    err_count += 1
                end
            end
-                # Verify reserve eligibility
+            # Verify minimum uptime
-                for r in sc.reserves
+            if is_shutting_down
-                    if r.type == "spinning"
+
-                        if unit ∉ r.thermal_units && (
+                # Calculate how much time the unit has been online
-                            unit in keys(
+                time_up = 0
-                                solution[sc.name]["Spinning reserve (MW)"][r.name],
+                for k in 1:(t-1)
-                            )
+                    if is_on[t-k]
-                        )
+                        time_up += 1
-                            @error @sprintf(
+                    else
-                                "Unit %s is not eligible to provide reserve %s",
+                        break
                                unit.name,
                                r.name,
                            )
                            err_count += 1
                        end
                    end
                end
                if (t == time_up + 1) && (unit.initial_status > 0)
                    time_up += unit.initial_status
                end
-                # If unit is on, must produce at least its minimum power
+                # Check minimum uptime
-                if is_on[t] && (production[t] < unit.min_power[t] - tol)
+                if time_up < unit.min_uptime
                    @error @sprintf(
-                        "Unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
+                        "Unit %s violates minimum uptime at time %d",
                        unit.name,
-                        t,
+                        t
                        production[t],
                        unit.min_power[t]
                    )
                    err_count += 1
                end
                # If unit is on, must produce at most its maximum power
                if is_on[t] &&
                   (production[t] + reserve[t] > unit.max_power[t] + tol)
                    @error @sprintf(
                        "Unit %s produces above its maximum limit at time %d (%.2f + %.2f> %.2f)",
                        unit.name,
                        t,
                        production[t],
                        reserve[t],
                        unit.max_power[t]
                    )
                    err_count += 1
                end
                # If unit is off, must produce zero
                if !is_on[t] && production[t] + reserve[t] > tol
                    @error @sprintf(
                        "Unit %s produces power at time %d while off (%.2f + %.2f > 0)",
                        unit.name,
                        t,
                        production[t],
                        reserve[t],
                    )
                    err_count += 1
                end
                # Startup limit
                if is_starting_up && (ramp_up > unit.startup_limit + tol)
                    @error @sprintf(
                        "Unit %s exceeds startup limit at time %d (%.2f > %.2f)",
                        unit.name,
                        t,
                        ramp_up,
                        unit.startup_limit
                    )
                    err_count += 1
                end
                # Shutdown limit
                if is_shutting_down && (ramp_down > unit.shutdown_limit + tol)
                    @error @sprintf(
                        "Unit %s exceeds shutdown limit at time %d (%.2f > %.2f)",
                        unit.name,
                        t,
                        ramp_down,
                        unit.shutdown_limit
                    )
                    err_count += 1
                end
                # Ramp-up limit
                if !is_starting_up &&
                   !is_shutting_down &&
                   (ramp_up > unit.ramp_up_limit + tol)
                    @error @sprintf(
                        "Unit %s exceeds ramp up limit at time %d (%.2f > %.2f)",
                        unit.name,
                        t,
                        ramp_up,
                        unit.ramp_up_limit
                    )
                    err_count += 1
                end
                # Ramp-down limit
                if !is_starting_up &&
                   !is_shutting_down &&
                   (ramp_down > unit.ramp_down_limit + tol)
                    @error @sprintf(
                        "Unit %s exceeds ramp down limit at time %d (%.2f > %.2f)",
                        unit.name,
                        t,
                        ramp_down,
                        unit.ramp_down_limit
                    )
                    err_count += 1
                end
                # Verify startup costs & minimum downtime
                if is_starting_up
                    # Calculate how much time the unit has been offline
                    time_down = 0
                    for k in 1:(t-1)
                        if !is_on[t-k]
                            time_down += 1
                        else
                            break
                        end
                    end
                    if (t == time_down + 1) && (unit.initial_status < 0)
                        time_down -= unit.initial_status
                    end
                    # Calculate startup costs
                    for c in unit.startup_categories
                        if time_down >= c.delay
                            startup_cost = c.cost
                        end
                    end
                    # Check minimum downtime
                    if time_down < unit.min_downtime
                        @error @sprintf(
                            "Unit %s violates minimum downtime at time %d",
                            unit.name,
                            t
                        )
                        err_count += 1
                    end
                end
                # Verify minimum uptime
                if is_shutting_down
                    # Calculate how much time the unit has been online
                    time_up = 0
                    for k in 1:(t-1)
                        if is_on[t-k]
                            time_up += 1
                        else
                            break
                        end
                    end
                    if (t == time_up + 1) && (unit.initial_status > 0)
                        time_up += unit.initial_status
                    end
                    # Check minimum uptime
                    if time_up < unit.min_uptime
                        @error @sprintf(
                            "Unit %s violates minimum uptime at time %d",
                            unit.name,
                            t
                        )
                        err_count += 1
                    end
                end
                # Verify production costs
                if abs(actual_production_cost[t] - production_cost) > 1.00
                    @error @sprintf(
                        "Unit %s has unexpected production cost at time %d (%.2f should be %.2f)",
                        unit.name,
                        t,
                        actual_production_cost[t],
                        production_cost
                    )
                    err_count += 1
                end
                # Verify startup costs
                if abs(actual_startup_cost[t] - startup_cost) > 1.00
                    @error @sprintf(
                        "Unit %s has unexpected startup cost at time %d (%.2f should be %.2f)",
                        unit.name,
                        t,
                        actual_startup_cost[t],
                        startup_cost
                    )
                    err_count += 1
                end
            end
        end
        for pu in sc.profiled_units
            production = solution[sc.name]["Profiled production (MW)"][pu.name]
-            for t in 1:instance.time
+            # Verify production costs
-                # Unit must produce at least its minimum power
+            if abs(actual_production_cost[t] - production_cost) > 1.00
-                if production[t] < pu.min_power[t] - tol
+                @error @sprintf(
-                    @error @sprintf(
+                    "Unit %s has unexpected production cost at time %d (%.2f should be %.2f)",
-                        "Profiled unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
+                    unit.name,
-                        pu.name,
+                    t,
-                        t,
+                    actual_production_cost[t],
-                        production[t],
+                    production_cost
-                        pu.min_power[t]
+                )
-                    )
+                err_count += 1
-                    err_count += 1
+            end
                end
-                # Unit must produce at most its maximum power
+            # Verify startup costs
-                if production[t] > pu.max_power[t] + tol
+            if abs(actual_startup_cost[t] - startup_cost) > 1.00
-                    @error @sprintf(
+                @error @sprintf(
-                        "Profiled unit %s produces above its maximum limit at time %d (%.2f > %.2f)",
+                    "Unit %s has unexpected startup cost at time %d (%.2f should be %.2f)",
-                        pu.name,
+                    unit.name,
-                        t,
+                    t,
-                        production[t],
+                    actual_startup_cost[t],
-                        pu.max_power[t]
+                    startup_cost
-                    )
+                )
-                    err_count += 1
+                err_count += 1
                end
            end
        end
    end
    return err_count
 end
 function _validate_reserve_and_demand(instance, solution, tol = 0.01)
    err_count = 0
-    for sc in instance.scenarios
+    for t in 1:instance.time
-        for t in 1:instance.time
+        load_curtail = 0
-            load_curtail = 0
+        fixed_load = sum(b.load[t] for b in instance.buses)
-            fixed_load = sum(b.load[t] for b in sc.buses)
+        ps_load = 0
-            ps_load = 0
+        if length(instance.price_sensitive_loads) > 0
-            production = 0
+            ps_load = sum(
-            if length(sc.price_sensitive_loads) > 0
+                solution["Price-sensitive loads (MW)"][ps.name][t] for
-                ps_load = sum(
+                ps in instance.price_sensitive_loads
-                    solution[sc.name]["Price-sensitive loads (MW)"][ps.name][t]
+            )
-                    for ps in sc.price_sensitive_loads
+        end
-                )
+        production =
-            end
+            sum(solution["Production (MW)"][g.name][t] for g in instance.units)
-            if length(sc.thermal_units) > 0
+        if "Load curtail (MW)" in keys(solution)
-                production = sum(
+            load_curtail = sum(
-                    solution[sc.name]["Thermal production (MW)"][g.name][t]
+                solution["Load curtail (MW)"][b.name][t] for
-                    for g in sc.thermal_units
+                b in instance.buses
-                )
+            )
-            end
+        end
-            if length(sc.profiled_units) > 0
+        balance = fixed_load - load_curtail - production + ps_load
                production += sum(
                    solution[sc.name]["Profiled production (MW)"][pu.name][t]
                    for pu in sc.profiled_units
                )
            end
            if "Load curtail (MW)" in keys(solution)
                load_curtail = sum(
                    solution[sc.name]["Load curtail (MW)"][b.name][t] for
                    b in sc.buses
                )
            end
            balance = fixed_load - load_curtail - production + ps_load
-            # Verify that production equals demand
+        # Verify that production equals demand
-            if abs(balance) > tol
+        if abs(balance) > tol
-                @error @sprintf(
+            @error @sprintf(
-                    "Non-zero power balance at time %d (%.2f + %.2f - %.2f - %.2f != 0)",
+                "Non-zero power balance at time %d (%.2f + %.2f - %.2f - %.2f != 0)",
-                    t,
+                t,
-                    fixed_load,
+                fixed_load,
-                    ps_load,
+                ps_load,
-                    load_curtail,
+                load_curtail,
-                    production,
+                production,
-                )
+            )
-                err_count += 1
+            err_count += 1
-            end
+        end
-            # Verify reserves
+        # Verify spinning reserves
-            for r in sc.reserves
+        reserve =
-                if r.type == "spinning"
+            sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
-                    provided = sum(
+        reserve_shortfall =
-                        solution[sc.name]["Spinning reserve (MW)"][r.name][g.name][t]
+            (instance.shortfall_penalty[t] >= 0) ?
-                        for g in r.thermal_units
+            solution["Reserve shortfall (MW)"][t] : 0
                    )
                    shortfall =
                        solution[sc.name]["Spinning reserve shortfall (MW)"][r.name][t]
                    required = r.amount[t]
-                    if provided + shortfall < required - tol
+        if reserve + reserve_shortfall < instance.reserves.spinning[t] - tol
-                        @error @sprintf(
+            @error @sprintf(
-                            "Insufficient reserve %s at time %d (%.2f + %.2f < %.2f)",
+                "Insufficient spinning reserves at time %d (%.2f + %.2f should be %.2f)",
-                            r.name,
+                t,
-                            t,
+                reserve,
-                            provided,
+                reserve_shortfall,
-                            shortfall,
+                instance.reserves.spinning[t],
-                            required,
+            )
-                        )
+            err_count += 1
                    end
                elseif r.type == "flexiramp"
                    upflexiramp = sum(
                        solution[sc.name]["Up-flexiramp (MW)"][r.name][g.name][t]
                        for g in r.thermal_units
                    )
                    upflexiramp_shortfall =
                        solution[sc.name]["Up-flexiramp shortfall (MW)"][r.name][t]
                    if upflexiramp + upflexiramp_shortfall < r.amount[t] - tol
                        @error @sprintf(
                            "Insufficient up-flexiramp at time %d (%.2f + %.2f < %.2f)",
                            t,
                            upflexiramp,
                            upflexiramp_shortfall,
                            r.amount[t],
                        )
                        err_count += 1
                    end
                    dwflexiramp = sum(
                        solution[sc.name]["Down-flexiramp (MW)"][r.name][g.name][t]
                        for g in r.thermal_units
                    )
                    dwflexiramp_shortfall =
                        solution[sc.name]["Down-flexiramp shortfall (MW)"][r.name][t]
                    if dwflexiramp + dwflexiramp_shortfall < r.amount[t] - tol
                        @error @sprintf(
                            "Insufficient down-flexiramp at time %d (%.2f + %.2f < %.2f)",
                            t,
                            dwflexiramp,
                            dwflexiramp_shortfall,
                            r.amount[t],
                        )
                        err_count += 1
                    end
                else
                    error("Unknown reserve type: $(r.type)")
                end
            end
        end
    end
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -1,20 +1,26 @@
 name = "UnitCommitmentT"
 uuid = "a3b7a17a-ab64-45e4-a924-cd5ae7dc644e"
 authors = ["Alinson S. Xavier <git@axavier.org>"]
 version = "0.1.0"
 [deps]
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
-HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
+Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"
+
 [compat]
 DataStructures = "0.18"
 Distributions = "0.25"
 GZip = "0.5"
 JSON = "0.21"
 JuMP = "0.21"
 MathOptInterface = "0.9"
 PackageCompiler = "1"
 julia = "1"
--- a/test/fixtures/aelmp_simple.json.gz
+++ b/test/fixtures/aelmp_simple.json.gz
--- a/test/fixtures/case118-initcond.json.gz
+++ b/test/fixtures/case118-initcond.json.gz
--- a/test/fixtures/case14-fixed-status.json.gz
+++ b/test/fixtures/case14-fixed-status.json.gz
--- a/test/fixtures/case14-flex.json.gz
+++ b/test/fixtures/case14-flex.json.gz
--- a/test/fixtures/case14-profiled.json.gz
+++ b/test/fixtures/case14-profiled.json.gz
--- a/test/fixtures/case14-sub-hourly.json.gz
+++ b/test/fixtures/case14-sub-hourly.json.gz
--- a/test/fixtures/case14.json.gz
+++ b/test/fixtures/case14.json.gz
--- a/test/fixtures/lmp_simple_test_1.json.gz
+++ b/test/fixtures/lmp_simple_test_1.json.gz
--- a/test/fixtures/lmp_simple_test_2.json.gz
+++ b/test/fixtures/lmp_simple_test_2.json.gz
--- a/test/fixtures/lmp_simple_test_3.json.gz
+++ b/test/fixtures/lmp_simple_test_3.json.gz
--- a/test/fixtures/lmp_simple_test_4.json.gz
+++ b/test/fixtures/lmp_simple_test_4.json.gz
--- a/test/fixtures/ucjl-0.2.json.gz
+++ b/test/fixtures/ucjl-0.2.json.gz
--- a/test/fixtures/ucjl-0.3.json.gz
+++ b/test/fixtures/ucjl-0.3.json.gz
--- a/test/import/egret_test.jl
+++ b/test/import/egret_test.jl
@@ -0,0 +1,23 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment
 basedir = @__DIR__
@testset "read_egret_solution" begin
    solution = UnitCommitment.read_egret_solution(
        "$basedir/../fixtures/egret_output.json.gz",
    )
    for attr in ["Is on", "Production (MW)", "Production cost (\$)"]
        @test attr in keys(solution)
        @test "115_STEAM_1" in keys(solution[attr])
        @test length(solution[attr]["115_STEAM_1"]) == 48
    end
    @test solution["Production cost (\$)"]["315_CT_6"][15:20] ==
          [0.0, 0.0, 884.44, 1470.71, 1470.71, 884.44]
    @test solution["Startup cost (\$)"]["315_CT_6"][15:20] ==
          [0.0, 0.0, 5665.23, 0.0, 0.0, 0.0]
    @test length(keys(solution["Is on"])) == 154
 end
--- a/test/instance/read_test.jl
+++ b/test/instance/read_test.jl
@@ -0,0 +1,121 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
@testset "read_benchmark" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    @test length(instance.lines) == 20
    @test length(instance.buses) == 14
    @test length(instance.units) == 6
    @test length(instance.contingencies) == 19
    @test length(instance.price_sensitive_loads) == 1
    @test instance.time == 4
    @test instance.lines[5].name == "l5"
    @test instance.lines[5].source.name == "b2"
    @test instance.lines[5].target.name == "b5"
    @test instance.lines[5].reactance ≈ 0.17388
    @test instance.lines[5].susceptance ≈ 10.037550333
    @test instance.lines[5].normal_flow_limit == [1e8 for t in 1:4]
    @test instance.lines[5].emergency_flow_limit == [1e8 for t in 1:4]
    @test instance.lines[5].flow_limit_penalty == [5e3 for t in 1:4]
    @test instance.lines_by_name["l5"].name == "l5"
    @test instance.lines[1].name == "l1"
    @test instance.lines[1].source.name == "b1"
    @test instance.lines[1].target.name == "b2"
    @test instance.lines[1].reactance ≈ 0.059170
    @test instance.lines[1].susceptance ≈ 29.496860773945
    @test instance.lines[1].normal_flow_limit == [300.0 for t in 1:4]
    @test instance.lines[1].emergency_flow_limit == [400.0 for t in 1:4]
    @test instance.lines[1].flow_limit_penalty == [1e3 for t in 1:4]
    @test instance.buses[9].name == "b9"
    @test instance.buses[9].load == [35.36638, 33.25495, 31.67138, 31.14353]
    @test instance.buses_by_name["b9"].name == "b9"
    unit = instance.units[1]
    @test unit.name == "g1"
    @test unit.bus.name == "b1"
    @test unit.ramp_up_limit == 1e6
    @test unit.ramp_down_limit == 1e6
    @test unit.startup_limit == 1e6
    @test unit.shutdown_limit == 1e6
    @test unit.must_run == [false for t in 1:4]
    @test unit.min_power_cost == [1400.0 for t in 1:4]
    @test unit.min_uptime == 1
    @test unit.min_downtime == 1
    @test unit.provides_spinning_reserves == [true for t in 1:4]
    for t in 1:1
        @test unit.cost_segments[1].mw[t] == 10.0
        @test unit.cost_segments[2].mw[t] == 20.0
        @test unit.cost_segments[3].mw[t] == 5.0
        @test unit.cost_segments[1].cost[t] ≈ 20.0
        @test unit.cost_segments[2].cost[t] ≈ 30.0
        @test unit.cost_segments[3].cost[t] ≈ 40.0
    end
    @test length(unit.startup_categories) == 3
    @test unit.startup_categories[1].delay == 1
    @test unit.startup_categories[2].delay == 2
    @test unit.startup_categories[3].delay == 3
    @test unit.startup_categories[1].cost == 1000.0
    @test unit.startup_categories[2].cost == 1500.0
    @test unit.startup_categories[3].cost == 2000.0
    @test instance.units_by_name["g1"].name == "g1"
    unit = instance.units[2]
    @test unit.name == "g2"
    @test unit.must_run == [false for t in 1:4]
    unit = instance.units[3]
    @test unit.name == "g3"
    @test unit.bus.name == "b3"
    @test unit.ramp_up_limit == 70.0
    @test unit.ramp_down_limit == 70.0
    @test unit.startup_limit == 70.0
    @test unit.shutdown_limit == 70.0
    @test unit.must_run == [true for t in 1:4]
    @test unit.min_power_cost == [0.0 for t in 1:4]
    @test unit.min_uptime == 1
    @test unit.min_downtime == 1
    @test unit.provides_spinning_reserves == [true for t in 1:4]
    for t in 1:4
        @test unit.cost_segments[1].mw[t] ≈ 33
        @test unit.cost_segments[2].mw[t] ≈ 33
        @test unit.cost_segments[3].mw[t] ≈ 34
        @test unit.cost_segments[1].cost[t] ≈ 33.75
        @test unit.cost_segments[2].cost[t] ≈ 38.04
        @test unit.cost_segments[3].cost[t] ≈ 44.77853
    end
    @test instance.reserves.spinning == zeros(4)
    @test instance.contingencies[1].lines == [instance.lines[1]]
    @test instance.contingencies[1].units == []
    @test instance.contingencies[1].name == "c1"
    @test instance.contingencies_by_name["c1"].name == "c1"
    load = instance.price_sensitive_loads[1]
    @test load.name == "ps1"
    @test load.bus.name == "b3"
    @test load.revenue == [100.0 for t in 1:4]
    @test load.demand == [50.0 for t in 1:4]
    @test instance.price_sensitive_loads_by_name["ps1"].name == "ps1"
 end
@testset "read_benchmark sub-hourly" begin
    instance = UnitCommitment.read_benchmark("test/case14-sub-hourly")
    @test instance.time == 4
    unit = instance.units[1]
    @test unit.name == "g1"
    @test unit.min_uptime == 2
    @test unit.min_downtime == 2
    @test length(unit.startup_categories) == 3
    @test unit.startup_categories[1].delay == 2
    @test unit.startup_categories[2].delay == 4
    @test unit.startup_categories[3].delay == 6
    @test unit.initial_status == -200
 end
--- a/test/model/formulations_test.jl
+++ b/test/model/formulations_test.jl
@@ -0,0 +1,74 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment
 using JuMP
 import UnitCommitment:
    ArrCon2000,
    CarArr2006,
    DamKucRajAta2016,
    Formulation,
    Gar1962,
    KnuOstWat2018,
    MorLatRam2013,
    PanGua2016,
    XavQiuWanThi2019
 if ENABLE_LARGE_TESTS
    using Gurobi
 end
 function _small_test(formulation::Formulation)::Nothing
    instances = ["matpower/case118/2017-02-01", "test/case14"]
    for instance in instances
        # Should not crash
        UnitCommitment.build_model(
            instance = UnitCommitment.read_benchmark(instance),
            formulation = formulation,
        )
    end
    return
 end
 function _large_test(formulation::Formulation)::Nothing
    instances = ["pglib-uc/ca/Scenario400_reserves_1"]
    for instance in instances
        instance = UnitCommitment.read_benchmark(instance)
        model = UnitCommitment.build_model(
            instance = instance,
            formulation = formulation,
            optimizer = Gurobi.Optimizer,
        )
        UnitCommitment.optimize!(
            model,
            XavQiuWanThi2019.Method(two_phase_gap = false, gap_limit = 0.1),
        )
        solution = UnitCommitment.solution(model)
        @test UnitCommitment.validate(instance, solution)
    end
    return
 end
 function _test(formulation::Formulation)::Nothing
    _small_test(formulation)
    if ENABLE_LARGE_TESTS
        _large_test(formulation)
    end
 end
@testset "formulations" begin
    _test(Formulation())
    _test(Formulation(ramping = ArrCon2000.Ramping()))
    # _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
    _test(
        Formulation(
            ramping = MorLatRam2013.Ramping(),
            startup_costs = MorLatRam2013.StartupCosts(),
        ),
    )
    _test(Formulation(ramping = PanGua2016.Ramping()))
    _test(Formulation(pwl_costs = Gar1962.PwlCosts()))
    _test(Formulation(pwl_costs = CarArr2006.PwlCosts()))
    _test(Formulation(pwl_costs = KnuOstWat2018.PwlCosts()))
 end
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -0,0 +1,39 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using Test
 using UnitCommitment
 push!(Base.LOAD_PATH, @__DIR__)
 UnitCommitment._setup_logger()
 const ENABLE_LARGE_TESTS = ("UCJL_LARGE_TESTS" in keys(ENV))
@testset "UnitCommitment" begin
    include("usage.jl")
    @testset "import" begin
        include("import/egret_test.jl")
    end
    @testset "instance" begin
        include("instance/read_test.jl")
    end
    @testset "model" begin
        include("model/formulations_test.jl")
    end
    @testset "XavQiuWanThi19" begin
        include("solution/methods/XavQiuWanThi19/filter_test.jl")
        include("solution/methods/XavQiuWanThi19/find_test.jl")
        include("solution/methods/XavQiuWanThi19/sensitivity_test.jl")
    end
    @testset "transform" begin
        include("transform/initcond_test.jl")
        include("transform/slice_test.jl")
        @testset "randomize" begin
            include("transform/randomize/XavQiuAhm2021_test.jl")
        end
    end
    @testset "validation" begin
        include("validation/repair_test.jl")
    end
 end
--- a/test/solution/methods/XavQiuWanThi19/filter_test.jl
+++ b/test/solution/methods/XavQiuWanThi19/filter_test.jl
@@ -0,0 +1,83 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Test, LinearAlgebra
 import UnitCommitment: _Violation, _offer, _query
@testset "_ViolationFilter" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    filter = UnitCommitment._ViolationFilter(max_per_line = 1, max_total = 2)
    _offer(
        filter,
        _Violation(
            time = 1,
            monitored_line = instance.lines[1],
            outage_line = nothing,
            amount = 100.0,
        ),
    )
    _offer(
        filter,
        _Violation(
            time = 1,
            monitored_line = instance.lines[1],
            outage_line = instance.lines[1],
            amount = 300.0,
        ),
    )
    _offer(
        filter,
        _Violation(
            time = 1,
            monitored_line = instance.lines[1],
            outage_line = instance.lines[5],
            amount = 500.0,
        ),
    )
    _offer(
        filter,
        _Violation(
            time = 1,
            monitored_line = instance.lines[1],
            outage_line = instance.lines[4],
            amount = 400.0,
        ),
    )
    _offer(
        filter,
        _Violation(
            time = 1,
            monitored_line = instance.lines[2],
            outage_line = instance.lines[1],
            amount = 200.0,
        ),
    )
    _offer(
        filter,
        _Violation(
            time = 1,
            monitored_line = instance.lines[2],
            outage_line = instance.lines[8],
            amount = 100.0,
        ),
    )
    actual = _query(filter)
    expected = [
        _Violation(
            time = 1,
            monitored_line = instance.lines[2],
            outage_line = instance.lines[1],
            amount = 200.0,
        ),
        _Violation(
            time = 1,
            monitored_line = instance.lines[1],
            outage_line = instance.lines[5],
            amount = 500.0,
        ),
    ]
    @test actual == expected
 end
--- a/test/solution/methods/XavQiuWanThi19/find_test.jl
+++ b/test/solution/methods/XavQiuWanThi19/find_test.jl
@@ -0,0 +1,35 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Test, LinearAlgebra
 import UnitCommitment: _Violation, _offer, _query
@testset "find_violations" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    for line in instance.lines, t in 1:instance.time
        line.normal_flow_limit[t] = 1.0
        line.emergency_flow_limit[t] = 1.0
    end
    isf = UnitCommitment._injection_shift_factors(
        lines = instance.lines,
        buses = instance.buses,
    )
    lodf = UnitCommitment._line_outage_factors(
        lines = instance.lines,
        buses = instance.buses,
        isf = isf,
    )
    inj = [1000.0 for b in 1:13, t in 1:instance.time]
    overflow = [0.0 for l in instance.lines, t in 1:instance.time]
    violations = UnitCommitment._find_violations(
        instance = instance,
        net_injections = inj,
        overflow = overflow,
        isf = isf,
        lodf = lodf,
        max_per_line = 1,
        max_per_period = 5,
    )
    @test length(violations) == 20
 end
--- a/test/solution/methods/XavQiuWanThi19/sensitivity_test.jl
+++ b/test/solution/methods/XavQiuWanThi19/sensitivity_test.jl
@@ -0,0 +1,143 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Test, LinearAlgebra
@testset "_susceptance_matrix" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    actual = UnitCommitment._susceptance_matrix(instance.lines)
    @test size(actual) == (20, 20)
    expected = Diagonal([
        29.5,
        7.83,
        8.82,
        9.9,
        10.04,
        10.2,
        41.45,
        8.35,
        3.14,
        6.93,
        8.77,
        6.82,
        13.4,
        9.91,
        15.87,
        20.65,
        6.46,
        9.09,
        8.73,
        5.02,
    ])
    @test round.(actual, digits = 2) == expected
 end
@testset "_reduced_incidence_matrix" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    actual = UnitCommitment._reduced_incidence_matrix(
        lines = instance.lines,
        buses = instance.buses,
    )
    @test size(actual) == (20, 13)
    @test actual[1, 1] == -1.0
    @test actual[3, 1] == 1.0
    @test actual[4, 1] == 1.0
    @test actual[5, 1] == 1.0
    @test actual[3, 2] == -1.0
    @test actual[6, 2] == 1.0
    @test actual[4, 3] == -1.0
    @test actual[6, 3] == -1.0
    @test actual[7, 3] == 1.0
    @test actual[8, 3] == 1.0
    @test actual[9, 3] == 1.0
    @test actual[2, 4] == -1.0
    @test actual[5, 4] == -1.0
    @test actual[7, 4] == -1.0
    @test actual[10, 4] == 1.0
    @test actual[10, 5] == -1.0
    @test actual[11, 5] == 1.0
    @test actual[12, 5] == 1.0
    @test actual[13, 5] == 1.0
    @test actual[8, 6] == -1.0
    @test actual[14, 6] == 1.0
    @test actual[15, 6] == 1.0
    @test actual[14, 7] == -1.0
    @test actual[9, 8] == -1.0
    @test actual[15, 8] == -1.0
    @test actual[16, 8] == 1.0
    @test actual[17, 8] == 1.0
    @test actual[16, 9] == -1.0
    @test actual[18, 9] == 1.0
    @test actual[11, 10] == -1.0
    @test actual[18, 10] == -1.0
    @test actual[12, 11] == -1.0
    @test actual[19, 11] == 1.0
    @test actual[13, 12] == -1.0
    @test actual[19, 12] == -1.0
    @test actual[20, 12] == 1.0
    @test actual[17, 13] == -1.0
    @test actual[20, 13] == -1.0
 end
@testset "_injection_shift_factors" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    actual = UnitCommitment._injection_shift_factors(
        lines = instance.lines,
        buses = instance.buses,
    )
    @test size(actual) == (20, 13)
    @test round.(actual, digits = 2) == [
        -0.84 -0.75 -0.67 -0.61 -0.63 -0.66 -0.66 -0.65 -0.65 -0.64 -0.63 -0.63 -0.64
        -0.16 -0.25 -0.33 -0.39 -0.37 -0.34 -0.34 -0.35 -0.35 -0.36 -0.37 -0.37 -0.36
        0.03 -0.53 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
        0.06 -0.14 -0.32 -0.22 -0.25 -0.3 -0.3 -0.29 -0.28 -0.27 -0.25 -0.26 -0.27
        0.08 -0.07 -0.2 -0.29 -0.26 -0.22 -0.22 -0.22 -0.23 -0.25 -0.26 -0.26 -0.24
        0.03 0.47 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
        0.08 0.31 0.5 -0.3 -0.03 0.36 0.36 0.28 0.23 0.1 -0.0 0.02 0.17
        0.0 0.01 0.02 -0.01 -0.22 -0.63 -0.63 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
        0.0 0.01 0.01 -0.01 -0.12 -0.17 -0.17 -0.26 -0.24 -0.18 -0.14 -0.14 -0.21
        -0.0 -0.02 -0.03 0.02 -0.66 -0.2 -0.2 -0.29 -0.36 -0.5 -0.63 -0.61 -0.43
        -0.0 -0.01 -0.02 0.01 0.21 -0.12 -0.12 -0.17 -0.28 -0.53 0.18 0.15 -0.03
        -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 -0.52 -0.17 -0.09
        -0.0 -0.01 -0.01 0.01 0.11 -0.06 -0.06 -0.09 -0.05 0.02 -0.28 -0.59 -0.31
        -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -1.0 -0.0 -0.0 -0.0 -0.0 -0.0 0.0
        0.0 0.01 0.02 -0.01 -0.22 0.37 0.37 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
        0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 -0.72 -0.47 -0.18 -0.15 0.03
        0.0 0.01 0.01 -0.01 -0.14 0.08 0.08 0.12 0.07 -0.03 -0.2 -0.24 -0.6
        0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 0.28 -0.47 -0.18 -0.15 0.03
        -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 0.48 -0.17 -0.09
        -0.0 -0.01 -0.01 0.01 0.14 -0.08 -0.08 -0.12 -0.07 0.03 0.2 0.24 -0.4
    ]
 end
@testset "_line_outage_factors" begin
    instance = UnitCommitment.read_benchmark("test/case14")
    isf_before = UnitCommitment._injection_shift_factors(
        lines = instance.lines,
        buses = instance.buses,
    )
    lodf = UnitCommitment._line_outage_factors(
        lines = instance.lines,
        buses = instance.buses,
        isf = isf_before,
    )
    for contingency in instance.contingencies
        for lc in contingency.lines
            prev_susceptance = lc.susceptance
            lc.susceptance = 0.0
            isf_after = UnitCommitment._injection_shift_factors(
                lines = instance.lines,
                buses = instance.buses,
            )
            lc.susceptance = prev_susceptance
            for lm in instance.lines
                expected = isf_after[lm.offset, :]
                actual =
                    isf_before[lm.offset, :] +
                    lodf[lm.offset, lc.offset] * isf_before[lc.offset, :]
                @test norm(expected - actual) < 1e-6
            end
        end
    end
 end
--- a/test/src/UnitCommitmentT.jl
+++ b/test/src/UnitCommitmentT.jl
@@ -1,58 +0,0 @@
 module UnitCommitmentT
 using JuliaFormatter
 using UnitCommitment
 using Test
 include("usage.jl")
 include("import/egret_test.jl")
 include("instance/read_test.jl")
 include("instance/migrate_test.jl")
 include("model/formulations_test.jl")
 include("solution/methods/XavQiuWanThi19/filter_test.jl")
 include("solution/methods/XavQiuWanThi19/find_test.jl")
 include("solution/methods/XavQiuWanThi19/sensitivity_test.jl")
 include("transform/initcond_test.jl")
 include("transform/slice_test.jl")
 include("transform/randomize/XavQiuAhm2021_test.jl")
 include("validation/repair_test.jl")
 include("lmp/conventional_test.jl")
 include("lmp/aelmp_test.jl")
 basedir = dirname(@__FILE__)
 function fixture(path::String)::String
    return "$basedir/../fixtures/$path"
 end
 function runtests()
    println("Running tests...")
    UnitCommitment._setup_logger(level = Base.CoreLogging.Error)
    @testset "UnitCommitment" begin
        usage_test()
        import_egret_test()
        instance_read_test()
        instance_migrate_test()
        model_formulations_test()
        solution_methods_XavQiuWanThi19_filter_test()
        solution_methods_XavQiuWanThi19_find_test()
        solution_methods_XavQiuWanThi19_sensitivity_test()
        transform_initcond_test()
        transform_slice_test()
        transform_randomize_XavQiuAhm2021_test()
        validation_repair_test()
        lmp_conventional_test()
        lmp_aelmp_test()
    end
    return
 end
 function format()
    JuliaFormatter.format(basedir, verbose = true)
    JuliaFormatter.format("$basedir/../../src", verbose = true)
    return
 end
 export runtests, format
 end # module UnitCommitmentT
--- a/test/src/import/egret_test.jl
+++ b/test/src/import/egret_test.jl
@@ -1,23 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment
 function import_egret_test()
    @testset "read_egret_solution" begin
        solution =
            UnitCommitment.read_egret_solution(fixture("egret_output.json.gz"))
        for attr in
            ["Is on", "Thermal production (MW)", "Thermal 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["Thermal 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/src/instance/migrate_test.jl
+++ b/test/src/instance/migrate_test.jl
@@ -1,24 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
 function instance_migrate_test()
    @testset "read v0.2" begin
        instance = UnitCommitment.read(fixture("/ucjl-0.2.json.gz"))
        @test length(instance.scenarios) == 1
        sc = instance.scenarios[1]
        @test length(sc.reserves_by_name["r1"].amount) == 4
        @test sc.thermal_units_by_name["g2"].reserves[1].name == "r1"
    end
    @testset "read v0.3" begin
        instance = UnitCommitment.read(fixture("/ucjl-0.3.json.gz"))
        @test length(instance.scenarios) == 1
        sc = instance.scenarios[1]
        @test length(sc.thermal_units) == 6
        @test length(sc.buses) == 14
        @test length(sc.lines) == 20
    end
 end
--- a/test/src/instance/read_test.jl
+++ b/test/src/instance/read_test.jl
@@ -1,168 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
 function instance_read_test()
    @testset "read_benchmark" begin
        instance = UnitCommitment.read(fixture("case14.json.gz"))
        @test repr(instance) == (
            "UnitCommitmentInstance(1 scenarios, 6 thermal units, 0 profiled units, 14 buses, " *
            "20 lines, 19 contingencies, 1 price sensitive loads, 4 time steps)"
        )
        @test length(instance.scenarios) == 1
        sc = instance.scenarios[1]
        @test length(sc.lines) == 20
        @test length(sc.buses) == 14
        @test length(sc.thermal_units) == 6
        @test length(sc.contingencies) == 19
        @test length(sc.price_sensitive_loads) == 1
        @test instance.time == 4
        @test sc.lines[5].name == "l5"
        @test sc.lines[5].source.name == "b2"
        @test sc.lines[5].target.name == "b5"
        @test sc.lines[5].reactance ≈ 0.17388
        @test sc.lines[5].susceptance ≈ 10.037550333
        @test sc.lines[5].normal_flow_limit == [1e8 for t in 1:4]
        @test sc.lines[5].emergency_flow_limit == [1e8 for t in 1:4]
        @test sc.lines[5].flow_limit_penalty == [5e3 for t in 1:4]
        @test sc.lines_by_name["l5"].name == "l5"
        @test sc.lines[1].name == "l1"
        @test sc.lines[1].source.name == "b1"
        @test sc.lines[1].target.name == "b2"
        @test sc.lines[1].reactance ≈ 0.059170
        @test sc.lines[1].susceptance ≈ 29.496860773945
        @test sc.lines[1].normal_flow_limit == [300.0 for t in 1:4]
        @test sc.lines[1].emergency_flow_limit == [400.0 for t in 1:4]
        @test sc.lines[1].flow_limit_penalty == [1e3 for t in 1:4]
        @test sc.buses[9].name == "b9"
        @test sc.buses[9].load == [35.36638, 33.25495, 31.67138, 31.14353]
        @test sc.buses_by_name["b9"].name == "b9"
        @test sc.reserves[1].name == "r1"
        @test sc.reserves[1].type == "spinning"
        @test sc.reserves[1].amount == [100.0, 100.0, 100.0, 100.0]
        @test sc.reserves_by_name["r1"].name == "r1"
        unit = sc.thermal_units[1]
        @test unit.name == "g1"
        @test unit.bus.name == "b1"
        @test unit.ramp_up_limit == 1e6
        @test unit.ramp_down_limit == 1e6
        @test unit.startup_limit == 1e6
        @test unit.shutdown_limit == 1e6
        @test unit.must_run == [false for t in 1:4]
        @test unit.min_power_cost == [1400.0 for t in 1:4]
        @test unit.min_uptime == 1
        @test unit.min_downtime == 1
        for t in 1:1
            @test unit.cost_segments[1].mw[t] == 10.0
            @test unit.cost_segments[2].mw[t] == 20.0
            @test unit.cost_segments[3].mw[t] == 5.0
            @test unit.cost_segments[1].cost[t] ≈ 20.0
            @test unit.cost_segments[2].cost[t] ≈ 30.0
            @test unit.cost_segments[3].cost[t] ≈ 40.0
        end
        @test length(unit.startup_categories) == 3
        @test unit.startup_categories[1].delay == 1
        @test unit.startup_categories[2].delay == 2
        @test unit.startup_categories[3].delay == 3
        @test unit.startup_categories[1].cost == 1000.0
        @test unit.startup_categories[2].cost == 1500.0
        @test unit.startup_categories[3].cost == 2000.0
        @test length(unit.reserves) == 0
        @test sc.thermal_units_by_name["g1"].name == "g1"
        unit = sc.thermal_units[2]
        @test unit.name == "g2"
        @test unit.must_run == [false for t in 1:4]
        @test length(unit.reserves) == 1
        unit = sc.thermal_units[3]
        @test unit.name == "g3"
        @test unit.bus.name == "b3"
        @test unit.ramp_up_limit == 70.0
        @test unit.ramp_down_limit == 70.0
        @test unit.startup_limit == 70.0
        @test unit.shutdown_limit == 70.0
        @test unit.must_run == [true for t in 1:4]
        @test unit.min_power_cost == [0.0 for t in 1:4]
        @test unit.min_uptime == 1
        @test unit.min_downtime == 1
        for t in 1:4
            @test unit.cost_segments[1].mw[t] ≈ 33
            @test unit.cost_segments[2].mw[t] ≈ 33
            @test unit.cost_segments[3].mw[t] ≈ 34
            @test unit.cost_segments[1].cost[t] ≈ 33.75
            @test unit.cost_segments[2].cost[t] ≈ 38.04
            @test unit.cost_segments[3].cost[t] ≈ 44.77853
        end
        @test length(unit.reserves) == 1
        @test unit.reserves[1].name == "r1"
        @test sc.contingencies[1].lines == [sc.lines[1]]
        @test sc.contingencies[1].thermal_units == []
        @test sc.contingencies[1].name == "c1"
        @test sc.contingencies_by_name["c1"].name == "c1"
        load = sc.price_sensitive_loads[1]
        @test load.name == "ps1"
        @test load.bus.name == "b3"
        @test load.revenue == [100.0 for t in 1:4]
        @test load.demand == [50.0 for t in 1:4]
        @test sc.price_sensitive_loads_by_name["ps1"].name == "ps1"
    end
    @testset "read_benchmark sub-hourly" begin
        instance = UnitCommitment.read(fixture("case14-sub-hourly.json.gz"))
        @test instance.time == 4
        unit = instance.scenarios[1].thermal_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 "read_benchmark profiled-units" begin
        instance = UnitCommitment.read(fixture("case14-profiled.json.gz"))
        sc = instance.scenarios[1]
        @test length(sc.profiled_units) == 2
        first_pu = sc.profiled_units[1]
        @test first_pu.name == "g7"
        @test first_pu.bus.name == "b4"
        @test first_pu.cost == [100.0 for t in 1:4]
        @test first_pu.min_power == [60.0 for t in 1:4]
        @test first_pu.max_power == [100.0 for t in 1:4]
        @test sc.profiled_units_by_name["g7"].name == "g7"
        second_pu = sc.profiled_units[2]
        @test second_pu.name == "g8"
        @test second_pu.bus.name == "b5"
        @test second_pu.cost == [50.0 for t in 1:4]
        @test second_pu.min_power == [0.0 for t in 1:4]
        @test second_pu.max_power == [120.0 for t in 1:4]
        @test sc.profiled_units_by_name["g8"].name == "g8"
    end
    @testset "read_benchmark commitmemt-status" begin
        instance = UnitCommitment.read(fixture("case14-fixed-status.json.gz"))
        sc = instance.scenarios[1]
        @test sc.thermal_units[1].commitment_status == [nothing for t in 1:4]
        @test sc.thermal_units[2].commitment_status == [true for t in 1:4]
        @test sc.thermal_units[4].commitment_status == [false for t in 1:4]
        @test sc.thermal_units[6].commitment_status ==
              [false, nothing, true, nothing]
    end
 end
--- a/test/src/lmp/aelmp_test.jl
+++ b/test/src/lmp/aelmp_test.jl
@@ -1,40 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Cbc, HiGHS, JuMP
 import UnitCommitment: AELMP
 function lmp_aelmp_test()
    @testset "aelmp" begin
        path = fixture("aelmp_simple.json.gz")
        # model has to be solved first
        instance = UnitCommitment.read(path)
        model = UnitCommitment.build_model(
            instance = instance,
            optimizer = Cbc.Optimizer,
            variable_names = true,
        )
        JuMP.set_silent(model)
        UnitCommitment.optimize!(model)
        # policy 1: allow offlines; consider startups
        aelmp_1 = UnitCommitment.compute_lmp(
            model,
            AELMP(),
            optimizer = HiGHS.Optimizer,
        )
        @test aelmp_1["s1", "B1", 1] ≈ 231.7 atol = 0.1
        # policy 2: do not allow offlines; but consider startups
        aelmp_2 = UnitCommitment.compute_lmp(
            model,
            AELMP(
                allow_offline_participation = false,
                consider_startup_costs = true,
            ),
            optimizer = HiGHS.Optimizer,
        )
        @test aelmp_2["s1", "B1", 1] ≈ 274.3 atol = 0.1
    end
 end
--- a/test/src/lmp/conventional_test.jl
+++ b/test/src/lmp/conventional_test.jl
@@ -1,53 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Cbc, HiGHS, JuMP
 import UnitCommitment: ConventionalLMP
 function solve_conventional_testcase(path::String)
    instance = UnitCommitment.read(path)
    model = UnitCommitment.build_model(
        instance = instance,
        optimizer = Cbc.Optimizer,
        variable_names = true,
    )
    JuMP.set_silent(model)
    UnitCommitment.optimize!(model)
    lmp = UnitCommitment.compute_lmp(
        model,
        ConventionalLMP(),
        optimizer = HiGHS.Optimizer,
    )
    return lmp
 end
 function lmp_conventional_test()
    @testset "conventional" begin
        # instance 1
        path = fixture("lmp_simple_test_1.json.gz")
        lmp = solve_conventional_testcase(path)
        @test lmp["s1", "A", 1] == 50.0
        @test lmp["s1", "B", 1] == 50.0
        # instance 2
        path = fixture("lmp_simple_test_2.json.gz")
        lmp = solve_conventional_testcase(path)
        @test lmp["s1", "A", 1] == 50.0
        @test lmp["s1", "B", 1] == 60.0
        # instance 3
        path = fixture("lmp_simple_test_3.json.gz")
        lmp = solve_conventional_testcase(path)
        @test lmp["s1", "A", 1] == 50.0
        @test lmp["s1", "B", 1] == 70.0
        @test lmp["s1", "C", 1] == 100.0
        # instance 4
        path = fixture("lmp_simple_test_4.json.gz")
        lmp = solve_conventional_testcase(path)
        @test lmp["s1", "A", 1] == 50.0
        @test lmp["s1", "B", 1] == 70.0
        @test lmp["s1", "C", 1] == 90.0
    end
 end
--- a/test/src/model/formulations_test.jl
+++ b/test/src/model/formulations_test.jl
@@ -1,86 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment
 using JuMP
 using Cbc
 using JSON
 import UnitCommitment:
    ArrCon2000,
    CarArr2006,
    DamKucRajAta2016,
    Formulation,
    Gar1962,
    KnuOstWat2018,
    MorLatRam2013,
    PanGua2016,
    XavQiuWanThi2019,
    WanHob2016
 function _test(
    formulation::Formulation;
    instances = ["case14"],
    dump::Bool = false,
 )::Nothing
    for instance_name in instances
        instance = UnitCommitment.read(fixture("$(instance_name).json.gz"))
        model = UnitCommitment.build_model(
            instance = instance,
            formulation = formulation,
            optimizer = Cbc.Optimizer,
            variable_names = true,
        )
        set_silent(model)
        UnitCommitment.optimize!(model)
        solution = UnitCommitment.solution(model)
        if dump
            open("/tmp/ucjl.json", "w") do f
                return write(f, JSON.json(solution, 2))
            end
            write_to_file(model, "/tmp/ucjl.lp")
        end
        @test UnitCommitment.validate(instance, solution)
    end
    return
 end
 function model_formulations_test()
    @testset "formulations" begin
        @testset "default" begin
            _test(Formulation())
        end
        @testset "ArrCon2000" begin
            _test(Formulation(ramping = ArrCon2000.Ramping()))
        end
        @testset "DamKucRajAta2016" begin
            _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
        end
        @testset "MorLatRam2013" begin
            _test(
                Formulation(
                    ramping = MorLatRam2013.Ramping(),
                    startup_costs = MorLatRam2013.StartupCosts(),
                ),
            )
        end
        @testset "PanGua2016" begin
            _test(Formulation(ramping = PanGua2016.Ramping()))
        end
        @testset "Gar1962" begin
            _test(Formulation(pwl_costs = Gar1962.PwlCosts()))
        end
        @testset "CarArr2006" begin
            _test(Formulation(pwl_costs = CarArr2006.PwlCosts()))
        end
        @testset "KnuOstWat2018" begin
            _test(Formulation(pwl_costs = KnuOstWat2018.PwlCosts()))
        end
        @testset "WanHob2016" begin
            _test(
                Formulation(ramping = WanHob2016.Ramping()),
                instances = ["case14-flex"],
            )
        end
    end
 end
--- a/test/src/solution/methods/XavQiuWanThi19/filter_test.jl
+++ b/test/src/solution/methods/XavQiuWanThi19/filter_test.jl
@@ -1,86 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Test, LinearAlgebra
 import UnitCommitment: _Violation, _offer, _query
 function solution_methods_XavQiuWanThi19_filter_test()
    @testset "_ViolationFilter" begin
        instance = UnitCommitment.read(fixture("case14.json.gz"))
        sc = instance.scenarios[1]
        filter =
            UnitCommitment._ViolationFilter(max_per_line = 1, max_total = 2)
        _offer(
            filter,
            _Violation(
                time = 1,
                monitored_line = sc.lines[1],
                outage_line = nothing,
                amount = 100.0,
            ),
        )
        _offer(
            filter,
            _Violation(
                time = 1,
                monitored_line = sc.lines[1],
                outage_line = sc.lines[1],
                amount = 300.0,
            ),
        )
        _offer(
            filter,
            _Violation(
                time = 1,
                monitored_line = sc.lines[1],
                outage_line = sc.lines[5],
                amount = 500.0,
            ),
        )
        _offer(
            filter,
            _Violation(
                time = 1,
                monitored_line = sc.lines[1],
                outage_line = sc.lines[4],
                amount = 400.0,
            ),
        )
        _offer(
            filter,
            _Violation(
                time = 1,
                monitored_line = sc.lines[2],
                outage_line = sc.lines[1],
                amount = 200.0,
            ),
        )
        _offer(
            filter,
            _Violation(
                time = 1,
                monitored_line = sc.lines[2],
                outage_line = sc.lines[8],
                amount = 100.0,
            ),
        )
        actual = _query(filter)
        expected = [
            _Violation(
                time = 1,
                monitored_line = sc.lines[2],
                outage_line = sc.lines[1],
                amount = 200.0,
            ),
            _Violation(
                time = 1,
                monitored_line = sc.lines[1],
                outage_line = sc.lines[5],
                amount = 500.0,
            ),
        ]
        @test actual == expected
    end
 end
--- a/test/src/solution/methods/XavQiuWanThi19/find_test.jl
+++ b/test/src/solution/methods/XavQiuWanThi19/find_test.jl
@@ -1,39 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Test, LinearAlgebra
 import UnitCommitment: _Violation, _offer, _query
 function solution_methods_XavQiuWanThi19_find_test()
    @testset "find_violations" begin
        instance = UnitCommitment.read(fixture("case14.json.gz"))
        sc = instance.scenarios[1]
        for line in sc.lines, t in 1:instance.time
            line.normal_flow_limit[t] = 1.0
            line.emergency_flow_limit[t] = 1.0
        end
        isf = UnitCommitment._injection_shift_factors(
            lines = sc.lines,
            buses = sc.buses,
        )
        lodf = UnitCommitment._line_outage_factors(
            lines = sc.lines,
            buses = sc.buses,
            isf = isf,
        )
        inj = [1000.0 for b in 1:13, t in 1:instance.time]
        overflow = [0.0 for l in sc.lines, t in 1:instance.time]
        violations = UnitCommitment._find_violations(
            instance = instance,
            sc = sc,
            net_injections = inj,
            overflow = overflow,
            isf = isf,
            lodf = lodf,
            max_per_line = 1,
            max_per_period = 5,
        )
        @test length(violations) == 20
    end
 end
--- a/test/src/solution/methods/XavQiuWanThi19/sensitivity_test.jl
+++ b/test/src/solution/methods/XavQiuWanThi19/sensitivity_test.jl
@@ -1,149 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Test, LinearAlgebra
 function solution_methods_XavQiuWanThi19_sensitivity_test()
    @testset "_susceptance_matrix" begin
        instance = UnitCommitment.read(fixture("/case14.json.gz"))
        sc = instance.scenarios[1]
        actual = UnitCommitment._susceptance_matrix(sc.lines)
        @test size(actual) == (20, 20)
        expected = Diagonal([
            29.5,
            7.83,
            8.82,
            9.9,
            10.04,
            10.2,
            41.45,
            8.35,
            3.14,
            6.93,
            8.77,
            6.82,
            13.4,
            9.91,
            15.87,
            20.65,
            6.46,
            9.09,
            8.73,
            5.02,
        ])
        @test round.(actual, digits = 2) == expected
    end
    @testset "_reduced_incidence_matrix" begin
        instance = UnitCommitment.read(fixture("/case14.json.gz"))
        sc = instance.scenarios[1]
        actual = UnitCommitment._reduced_incidence_matrix(
            lines = sc.lines,
            buses = sc.buses,
        )
        @test size(actual) == (20, 13)
        @test actual[1, 1] == -1.0
        @test actual[3, 1] == 1.0
        @test actual[4, 1] == 1.0
        @test actual[5, 1] == 1.0
        @test actual[3, 2] == -1.0
        @test actual[6, 2] == 1.0
        @test actual[4, 3] == -1.0
        @test actual[6, 3] == -1.0
        @test actual[7, 3] == 1.0
        @test actual[8, 3] == 1.0
        @test actual[9, 3] == 1.0
        @test actual[2, 4] == -1.0
        @test actual[5, 4] == -1.0
        @test actual[7, 4] == -1.0
        @test actual[10, 4] == 1.0
        @test actual[10, 5] == -1.0
        @test actual[11, 5] == 1.0
        @test actual[12, 5] == 1.0
        @test actual[13, 5] == 1.0
        @test actual[8, 6] == -1.0
        @test actual[14, 6] == 1.0
        @test actual[15, 6] == 1.0
        @test actual[14, 7] == -1.0
        @test actual[9, 8] == -1.0
        @test actual[15, 8] == -1.0
        @test actual[16, 8] == 1.0
        @test actual[17, 8] == 1.0
        @test actual[16, 9] == -1.0
        @test actual[18, 9] == 1.0
        @test actual[11, 10] == -1.0
        @test actual[18, 10] == -1.0
        @test actual[12, 11] == -1.0
        @test actual[19, 11] == 1.0
        @test actual[13, 12] == -1.0
        @test actual[19, 12] == -1.0
        @test actual[20, 12] == 1.0
        @test actual[17, 13] == -1.0
        @test actual[20, 13] == -1.0
    end
    @testset "_injection_shift_factors" begin
        instance = UnitCommitment.read(fixture("/case14.json.gz"))
        sc = instance.scenarios[1]
        actual = UnitCommitment._injection_shift_factors(
            lines = sc.lines,
            buses = sc.buses,
        )
        @test size(actual) == (20, 13)
        @test round.(actual, digits = 2) == [
            -0.84 -0.75 -0.67 -0.61 -0.63 -0.66 -0.66 -0.65 -0.65 -0.64 -0.63 -0.63 -0.64
            -0.16 -0.25 -0.33 -0.39 -0.37 -0.34 -0.34 -0.35 -0.35 -0.36 -0.37 -0.37 -0.36
            0.03 -0.53 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
            0.06 -0.14 -0.32 -0.22 -0.25 -0.3 -0.3 -0.29 -0.28 -0.27 -0.25 -0.26 -0.27
            0.08 -0.07 -0.2 -0.29 -0.26 -0.22 -0.22 -0.22 -0.23 -0.25 -0.26 -0.26 -0.24
            0.03 0.47 -0.15 -0.1 -0.12 -0.14 -0.14 -0.14 -0.13 -0.13 -0.12 -0.12 -0.13
            0.08 0.31 0.5 -0.3 -0.03 0.36 0.36 0.28 0.23 0.1 -0.0 0.02 0.17
            0.0 0.01 0.02 -0.01 -0.22 -0.63 -0.63 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
            0.0 0.01 0.01 -0.01 -0.12 -0.17 -0.17 -0.26 -0.24 -0.18 -0.14 -0.14 -0.21
            -0.0 -0.02 -0.03 0.02 -0.66 -0.2 -0.2 -0.29 -0.36 -0.5 -0.63 -0.61 -0.43
            -0.0 -0.01 -0.02 0.01 0.21 -0.12 -0.12 -0.17 -0.28 -0.53 0.18 0.15 -0.03
            -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 -0.52 -0.17 -0.09
            -0.0 -0.01 -0.01 0.01 0.11 -0.06 -0.06 -0.09 -0.05 0.02 -0.28 -0.59 -0.31
            -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 -1.0 -0.0 -0.0 -0.0 -0.0 -0.0 0.0
            0.0 0.01 0.02 -0.01 -0.22 0.37 0.37 -0.45 -0.41 -0.32 -0.24 -0.25 -0.36
            0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 -0.72 -0.47 -0.18 -0.15 0.03
            0.0 0.01 0.01 -0.01 -0.14 0.08 0.08 0.12 0.07 -0.03 -0.2 -0.24 -0.6
            0.0 0.01 0.02 -0.01 -0.21 0.12 0.12 0.17 0.28 -0.47 -0.18 -0.15 0.03
            -0.0 -0.0 -0.0 0.0 0.03 -0.02 -0.02 -0.03 -0.02 0.01 0.48 -0.17 -0.09
            -0.0 -0.01 -0.01 0.01 0.14 -0.08 -0.08 -0.12 -0.07 0.03 0.2 0.24 -0.4
        ]
    end
    @testset "_line_outage_factors" begin
        instance = UnitCommitment.read(fixture("/case14.json.gz"))
        sc = instance.scenarios[1]
        isf_before = UnitCommitment._injection_shift_factors(
            lines = sc.lines,
            buses = sc.buses,
        )
        lodf = UnitCommitment._line_outage_factors(
            lines = sc.lines,
            buses = sc.buses,
            isf = isf_before,
        )
        for contingency in sc.contingencies
            for lc in contingency.lines
                prev_susceptance = lc.susceptance
                lc.susceptance = 0.0
                isf_after = UnitCommitment._injection_shift_factors(
                    lines = sc.lines,
                    buses = sc.buses,
                )
                lc.susceptance = prev_susceptance
                for lm in sc.lines
                    expected = isf_after[lm.offset, :]
                    actual =
                        isf_before[lm.offset, :] +
                        lodf[lm.offset, lc.offset] * isf_before[lc.offset, :]
                    @test norm(expected - actual) < 1e-6
                end
            end
        end
    end
 end
--- a/test/src/transform/initcond_test.jl
+++ b/test/src/transform/initcond_test.jl
@@ -1,30 +0,0 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, Cbc, JuMP
 function transform_initcond_test()
    @testset "generate_initial_conditions!" begin
        # Load instance
        instance = UnitCommitment.read(fixture("case118-initcond.json.gz"))
        optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
        sc = instance.scenarios[1]
        # All units should have unknown initial conditions
        for g in sc.thermal_units
            @test g.initial_power === nothing
            @test g.initial_status === nothing
        end
        # Generate initial conditions
        UnitCommitment.generate_initial_conditions!(sc, optimizer)
        # All units should now have known initial conditions
        for g in sc.thermal_units
            @test g.initial_power !== nothing
            @test g.initial_status !== nothing
        end
        # TODO: Check that initial conditions are feasible
    end
 end
--- a/Show More
+++ b/Show More
`@@ -1,4 +1,4 @@`
	`Copyright © 2020-2022, UChicago Argonne, LLC`	`Copyright © 2020, UChicago Argonne, LLC`

	`All Rights Reserved`	`All Rights Reserved`