Update README.md

Project.toml: Remove Revise.jl
Update references, copyright notices, links
2025-12-07 00:38:51 -06:00 · 2022-07-18 09:54:15 -06:00 · 2022-07-18 09:42:00 -06:00 · 2022-07-18 09:40:52 -06:00 · 2022-07-17 15:50:42 -06:00 · 2022-07-17 14:44:58 -06:00
62 changed files with 973 additions and 799 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,21 @@ 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, UChicago Argonne, LLC
+Copyright © 2020-2022, UChicago Argonne, LLC
 All Rights Reserved
--- a/6
+++ b/6
@@ -2,14 +2,14 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
-VERSION := 0.2
+VERSION := 0.3
 clean:
 	rm -rfv build Manifest.toml test/Manifest.toml deps/formatter/build deps/formatter/Manifest.toml
 docs:
-	cd docs; make clean; make dirhtml
+	cd docs; julia --project=. make.jl; cd ..
-	rsync -avP --delete-after docs/_build/dirhtml/ ../docs/$(VERSION)/
+	rsync -avP --delete-after docs/build/ ../docs/$(VERSION)/
 format:
 	cd deps/formatter; ../../juliaw format.jl
--- 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 = "0.2.2"
+version = "0.3.0"
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
--- a/README.md
+++ b/README.md
@@ -87,10 +87,11 @@ UnitCommitment.write("/tmp/output.json", solution)
 ## Documentation
-1. [Usage](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/usage/)
+1. [Usage](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/usage/)
-2. [Data Format](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/format/)
+2. [Data Format](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/format/)
-3. [Instances](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/instances/)
+3. [Instances](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/instances/)
-4. [JuMP Model](https://anl-ceeesa.github.io/UnitCommitment.jl/0.2/model/)
+4. [JuMP Model](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/model/)
 5. [API Reference](https://anl-ceeesa.github.io/UnitCommitment.jl/0.3/api/)
 ## Authors
 * **Alinson S. Xavier** (Argonne National Laboratory)
@@ -110,15 +111,15 @@ 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, 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).
+* **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).
-If you use the instances, we additionally request that you cite the original sources, as described in the [instances page](docs/instances.md).
+If you use the instances, we additionally request that you cite the original sources, as described in the documentation.
 ## License
 ```text
 UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment
-Copyright © 2020-2021, UChicago Argonne, LLC. All Rights Reserved.
+Copyright © 2020-2022, 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/docs/Makefile
+++ b/docs/Makefile
@@ -1,14 +0,0 @@
 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
@@ -0,0 +1,4 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"
--- a/docs/_static/custom.css
+++ b/docs/_static/custom.css
@@ -1,49 +0,0 @@
 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
@@ -1,16 +0,0 @@
 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/make.jl
+++ b/docs/make.jl
@@ -0,0 +1,16 @@
 using Documenter, UnitCommitment
 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/api.md
+++ b/docs/src/api.md
@@ -0,0 +1,48 @@
 # API Reference
 ## Read data, build model & optimize
 ```@docs
 UnitCommitment.read
 UnitCommitment.read_benchmark
 UnitCommitment.build_model
 UnitCommitment.optimize!
 UnitCommitment.solution
 UnitCommitment.validate
 UnitCommitment.write
 ```
 ## 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/cost_curve.png
+++ b/docs/src/assets/cost_curve.png
--- a/docs/src/assets/custom.css
+++ b/docs/src/assets/custom.css
@@ -0,0 +1,36 @@
@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/format.md
+++ b/docs/src/format.md
@@ -1,52 +1,40 @@
 ```{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. For a complete example, see [case14](https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances/matpower/case14).
+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.
 ### Parameters
-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.
+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.
 | 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 $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
 | `Flexiramp penalty ($/MW)` | Penalty for system-wide shortage in meeting flexible ramping product requirements (in $/MW). This is charged per time step. | `500` | Y
 #### Example
 ```json
 {
    "Parameters": {
        "Version": "0.3",
        "Time horizon (h)": 4,
-        "Power balance penalty ($/MW)": 1000.0,
+        "Power balance penalty ($/MW)": 1000.0
        "Reserve shortfall penalty ($/MW)": -1.0,
        "Flexiramp penalty ($/MW)": 100.0
    }
 }
 ```
@@ -98,20 +86,20 @@ This section describes all generators in the system, including thermal units, re
 | `Initial status (h)`  | If set to a positive number, indicates the amount of time (in hours) the generator has been on at the beginning of the simulation, and if set to a negative number, the amount of time the generator has been off. For example, if `Initial status (h)` is `-2`, this means that the generator was off since `-02:00` (h:min). The simulation starts at time `00:00`. If `Initial status (h)` is `3`, this means that the generator was on since `-03:00`. A value of zero is not acceptable. | Required | N
 | `Initial power (MW)`  | Amount of power the generator at time step `-1`, immediately before the planning horizon starts. | Required | N
 | `Must run?`               | If `true`, the generator should be committed, even if that is not economical (Boolean). | `false` | Y
-| `Provides spinning reserves?`    | If `true`, this generator may provide spinning reserves (Boolean). | `true` | Y
+| `Reserve eligibility` | List of reserve products this generator is eligibe to provide. By default, the generator is not eligible to provide any reserves. | `[]` | N
 | `Provides flexible capacity?`    | If `true`, this generator may provide flexible ramping product (Boolean). | `true` | Y
 #### Production costs and limits
 Production costs are represented as piecewise-linear curves. Figure 1 shows an example cost curve with three segments, where it costs \$1400, \$1600, \$2200 and \$2400 to generate, respectively, 100, 110, 130 and 135 MW of power. To model this generator, `Production cost curve (MW)` should be set to `[100, 110, 130, 135]`, and `Production cost curve ($)`  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="../_static/cost_curve.png" style="max-width: 500px"/>
+    <img src="../assets/cost_curve.png" style="max-width: 500px"/>
    <div><b>Figure 1.</b> Piecewise-linear production cost curve.</div>
    <br/>
 </center>
 ```
 #### Additional remarks:
@@ -139,14 +127,13 @@ Note that this curve also specifies the production limits. Specifically, the fir
            "Minimum uptime (h)": 4,
            "Initial status (h)": 12,
            "Must run?": false,
-            "Provides spinning reserves?": true,
+            "Reserve eligibility": ["r1"],
            "Provides flexible capacity?": false,
        },
        "gen2": {
            "Bus": "b5",
            "Production cost curve (MW)": [0.0, [10.0, 8.0, 0.0, 3.0]],
            "Production cost curve ($)": [0.0, 0.0],
-            "Provides spinning reserves?": true,
+            "Reserve eligibility": ["r1", "r2"],
        }
    }
 }
@@ -176,7 +163,7 @@ This section describes components in the system which may increase or reduce the
 }
 ```
-### Transmission Lines
+### Transmission lines
 This section describes the characteristics of transmission system, such as its topology and the susceptance of each transmission line.
@@ -216,42 +203,34 @@ This section describes the hourly amount of reserves required.
 | Key                   | Description                                        | Default   |  Time series?
 | :-------------------- | :------------------------------------------------- | --------- |  :----:
-| `Spinning (MW)`       | Minimum amount of system-wide spinning reserves (in MW). Only generators which are online may provide this reserve. | `0.0` | Y
+| `Type` | Type of reserve product. Must be either "spinning" or "flexiramp". | Required | N
-| `Up-flexiramp (MW)`       | Minimum amount of system-wide upward flexible ramping product (in MW). Only generators which are online may provide this reserve. | `0.0` | Y
+| `Amount (MW)` | Amount of reserves required. | Required | Y
-| `Down-flexiramp (MW)`       | Minimum amount of system-wide downward flexible ramping product (in MW). Only generators which are online may provide this reserve. | `0.0` | Y
+| `Shortfall penalty ($/MW)` | Penalty for shortage in meeting the reserve requirements (in $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
 #### Example 1
 ```json
 {
    "Reserves": {
-        "Spinning (MW)": [
+        "r1": {
-            57.30552,
+            "Type": "spinning",
-            53.88429,
+            "Amount (MW)": [
-            51.31838,
+                57.30552,
-            50.46307
+                53.88429,
-        ]
+                51.31838,
-    }
+                50.46307
-}
+            ],
-```
+            "Shortfall penalty ($/MW)": 5.0
-
+        },
-#### Example 2
+        "r2": {
-
+            "Type": "flexiramp",
-```json
+            "Amount (MW)": [
-{
+                20.31042,
-    "Reserves": {
+                23.65273,
-        "Up-flexiramp (MW)": [
+                27.41784,
-            20.31042,
+                25.34057
-            23.65273,
+            ],
-            27.41784,
+        }
            25.34057
        ],
         "Down-flexiramp (MW)": [
            19.41546,
            21.45377,
            23.53402,
            24.80973
        ]
    }
 }
 ```
@@ -314,10 +293,8 @@ 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.
 * Upward and downward flexible ramping products can only be acquired under the WanHob2016 formulation, which does not support spinning reserves. 
 * Network topology remains the same for all time periods
 * Only N-1 transmission contingencies are supported. Generator contingencies are not currently supported.
 * Time-varying minimum production amounts are not currently compatible with ramp/startup/shutdown limits.
-
+* 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 +6,23 @@
 * **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
 * **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature, converted into a common data format, and extended using data-driven methods to make them more challenging and realistic.
-* **Model Implementation**: The package provides a Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
+* **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.
 * **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
-[ArrCon2000]: https://doi.org/10.1109/59.871739
+## Table of Contents
 [CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
 [DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
 [Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
 [KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
 [MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
 [PanGua2016]: https://doi.org/10.1287/opre.2016.1520
 [XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
-### Authors
+```@contents
 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)
 * **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 +30,19 @@
 * 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, 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).
+* **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).
 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, UChicago Argonne, LLC. All Rights Reserved.
+Copyright © 2020-2022, 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:
@@ -67,16 +66,3 @@ 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
@@ -1,19 +1,11 @@
 ```{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).
+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/).
-```{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
@@ -33,7 +25,7 @@ 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 two variations (`2017-02-01` and `2017-08-01`) corresponding respectively to a winter and to a summer test case.
+For each MATPOWER test case, UC.jl provides 365 variations (`2017-01-01` to `2017-12-31`) corresponding different days of the year.
 ### MATPOWER/UW-PSTCA
@@ -41,11 +33,11 @@ A variety of smaller IEEE test cases, [compiled by University of Washington](htt
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case14/2017-02-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
+| `matpower/case14/2017-01-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
-| `matpower/case30/2017-02-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
+| `matpower/case30/2017-01-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
-| `matpower/case57/2017-02-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
+| `matpower/case57/2017-01-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
-| `matpower/case118/2017-02-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
+| `matpower/case118/2017-01-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
-| `matpower/case300/2017-02-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
+| `matpower/case300/2017-01-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
 ### MATPOWER/Polish
@@ -54,14 +46,14 @@ Test cases based on the Polish 400, 220 and 110 kV networks, originally provided
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case2383wp/2017-02-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
+| `matpower/case2383wp/2017-01-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
-| `matpower/case2736sp/2017-02-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
+| `matpower/case2736sp/2017-01-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
-| `matpower/case2737sop/2017-02-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
+| `matpower/case2737sop/2017-01-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
-| `matpower/case2746wop/2017-02-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
+| `matpower/case2746wop/2017-01-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
-| `matpower/case2746wp/2017-02-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
+| `matpower/case2746wp/2017-01-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
-| `matpower/case3012wp/2017-02-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
+| `matpower/case3012wp/2017-01-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
-| `matpower/case3120sp/2017-02-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
+| `matpower/case3120sp/2017-01-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
-| `matpower/case3375wp/2017-02-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
+| `matpower/case3375wp/2017-01-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
 ### MATPOWER/PEGASE
@@ -69,11 +61,11 @@ Test cases from the [Pan European Grid Advanced Simulation and State Estimation
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case89pegase/2017-02-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case89pegase/2017-01-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case1354pegase/2017-02-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case1354pegase/2017-01-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case2869pegase/2017-02-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case2869pegase/2017-01-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case9241pegase/2017-02-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case9241pegase/2017-01-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
-| `matpower/case13659pegase/2017-02-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
+| `matpower/case13659pegase/2017-01-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
 ### MATPOWER/RTE
@@ -81,14 +73,14 @@ Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://
 | Name | Buses | Generators | Lines | Contingencies | References |
 |------|-------|------------|-------|---------------|--------|
-| `matpower/case1888rte/2017-02-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
+| `matpower/case1888rte/2017-01-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
-| `matpower/case1951rte/2017-02-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
+| `matpower/case1951rte/2017-01-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
-| `matpower/case2848rte/2017-02-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
+| `matpower/case2848rte/2017-01-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
-| `matpower/case2868rte/2017-02-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
+| `matpower/case2868rte/2017-01-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
-| `matpower/case6468rte/2017-02-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
+| `matpower/case6468rte/2017-01-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
-| `matpower/case6470rte/2017-02-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
+| `matpower/case6470rte/2017-01-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
-| `matpower/case6495rte/2017-02-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
+| `matpower/case6495rte/2017-01-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
-| `matpower/case6515rte/2017-02-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
+| `matpower/case6515rte/2017-01-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
 PGLIB-UC Instances
@@ -288,7 +280,7 @@ Tejada19
 References
 ----------
-* [UCJL] **Alinson S. Xavier, Aleksandr M. Kazachkov, Feng Qiu.** "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874)
+* [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)
 * [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)
@@ -296,14 +288,9 @@ 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
+* [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).
 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.**
+* [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)
 "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/src/model.md
+++ b/docs/src/model.md
@@ -1,11 +1,3 @@
 ```{sectnum}
 ---
 start: 4
 depth: 2
 suffix: .
 ---
 ```
 JuMP Model
 ==========
@@ -17,20 +9,20 @@ Decision variables
 ### Generators
 Name | Symbol | Description | Unit
-----|:--------:|-------------|:------:
+:-----|:--------:|:-------------|:------:
 `is_on[g,t]` | $u_{g}(t)$ | True if generator `g` is on at time `t`. | Binary
 `switch_on[g,t]` | $v_{g}(t)$ | True is generator `g` switches on at time `t`. | Binary
 `switch_off[g,t]` | $w_{g}(t)$ | True if generator `g` switches off at time `t`. | Binary
 `prod_above[g,t]` |$p'_{g}(t)$ | Amount of power produced by generator `g` above its minimum power output at time `t`. For example, if the minimum power of generator `g` is 100 MW and `g` is producing 115 MW of power at time `t`, then `prod_above[g,t]` equals `15.0`. | MW
 `segprod[g,t,k]` | $p^k_g(t)$ | Amount of power from piecewise linear segment `k` produced by generator `g` at time `t`. For example, if cost curve for generator `g` is defined by the points `(100, 1400)`, `(110, 1600)`, `(130, 2200)` and `(135, 2400)`, and if the generator is producing 115 MW of power at time `t`, then `segprod[g,t,:]` equals `[10.0, 5.0, 0.0]`.| MW
-`reserve[g,t]` | $r_g(t)$ | Amount of reserves provided by generator `g` at time `t`. | MW
+`reserve[r,g,t]` | $r_g(t)$ | Amount of reserve `r` provided by unit `g` at time `t`. | MW
 `startup[g,t,s]` | $\delta^s_g(t)$ | True if generator `g` switches on at time `t` incurring start-up costs from start-up category `s`. | Binary
 ### Buses
 Name | Symbol | Description | Unit
-----|:------:|-------------|:------:
+:-----|:------:|:-------------|:------:
 `net_injection[b,t]` | $n_b(t)$ | Net injection at bus `b` at time `t`. | MW
 `curtail[b,t]` | $s^+_b(t)$ | Amount of load curtailed at bus `b` at time `t` | MW
@@ -38,69 +30,24 @@ 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/usage.md
+++ b/docs/src/usage.md
@@ -1,21 +1,13 @@
 ```{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:
+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@0.2
+pkg> add UnitCommitment@0.3
 ```
 To test that the package has been correctly installed, run:
@@ -126,9 +118,9 @@ model = UnitCommitment.build_model(
 UnitCommitment.optimize!(model)
 ```
-```{warning}
+!!! warning
-The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
+
-```
+    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
--- a/instances/test/case14-flex.json.gz
+++ b/instances/test/case14-flex.json.gz
--- a/src/UnitCommitment.jl
+++ b/src/UnitCommitment.jl
@@ -20,6 +20,7 @@ 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")
--- a/src/instance/migrate.jl
+++ b/src/instance/migrate.jl
@@ -0,0 +1,38 @@
 # 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)
    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
--- a/src/instance/read.jl
+++ b/src/instance/read.jl
@@ -8,20 +8,18 @@ using DataStructures
 using GZip
 import Base: getindex, time
-const INSTANCES_URL = "https://axavier.org/UnitCommitment.jl/0.2/instances"
+const INSTANCES_URL = "https://axavier.org/UnitCommitment.jl/0.3/instances"
 """
    read_benchmark(name::AbstractString)::UnitCommitmentInstance
-Read one of the benchmark unit commitment instances included in the package.
+Read one of the benchmark instances included in the package. See
-See "Instances" section of the documentation for the entire list of benchmark
+[Instances](instances.md) for the entire list of benchmark instances available.
 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;
@@ -48,13 +46,13 @@ end
 """
    read(path::AbstractString)::UnitCommitmentInstance
-Read a unit commitment instance from a file. The file may be gzipped.
+Read instance from a file. The file may be gzipped.
-Example
+# Example
 -------
-    import UnitCommitment
+```julia
-    instance = UnitCommitment.read("/path/to/input.json.gz")
+instance = UnitCommitment.read("/path/to/input.json.gz")
 ```
 """
 function read(path::AbstractString)::UnitCommitmentInstance
    if endswith(path, ".gz")
@@ -80,11 +78,13 @@ function _read_json(path::String)::OrderedDict
 end
 function _from_json(json; repair = true)
    _migrate(json)
    units = Unit[]
    buses = Bus[]
    contingencies = Contingency[]
    lines = TransmissionLine[]
    loads = PriceSensitiveLoad[]
    reserves = Reserve[]
    function scalar(x; default = nothing)
        x !== nothing || return default
@@ -105,6 +105,7 @@ function _from_json(json; repair = true)
    name_to_bus = Dict{String,Bus}()
    name_to_line = Dict{String,TransmissionLine}()
    name_to_unit = Dict{String,Unit}()
    name_to_reserve = Dict{String,Reserve}()
    function timeseries(x; default = nothing)
        x !== nothing || return default
@@ -140,6 +141,24 @@ function _from_json(json; repair = true)
        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)"]),
                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"]
        bus = name_to_bus[dict["Bus"]]
@@ -177,6 +196,13 @@ function _from_json(json; repair = true)
            )
        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)
@@ -210,36 +236,17 @@ function _from_json(json; repair = true)
            scalar(dict["Shutdown limit (MW)"], default = 1e6),
            initial_status,
            initial_power,
            timeseries(
                dict["Provides spinning reserves?"],
                default = [true for t in 1:T],
            ),
            timeseries(
                dict["Provides flexible capacity?"],
                default = [true for t in 1:T],
            ),
            startup_categories,
            unit_reserves,
        )
        push!(bus.units, unit)
        for r in unit_reserves
            push!(r.units, unit)
        end
        name_to_unit[unit_name] = unit
        push!(units, unit)
    end
    # Read spinning, up-flexiramp, and down-flexiramp reserve requirements
    reserves = Reserves(zeros(T), zeros(T), zeros(T))
    if "Reserves" in keys(json)
        reserves.spinning =
            timeseries(json["Reserves"]["Spinning (MW)"], default = zeros(T))
        reserves.upflexiramp = timeseries(
            json["Reserves"]["Up-flexiramp (MW)"],
            default = zeros(T),
        )
        reserves.dwflexiramp = timeseries(
            json["Reserves"]["Down-flexiramp (MW)"],
            default = zeros(T),
        )
    end
    # Read transmission lines
    if "Transmission lines" in keys(json)
        for (line_name, dict) in json["Transmission lines"]
@@ -312,6 +319,7 @@ function _from_json(json; repair = true)
        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,
        flexiramp_shortfall_penalty = flexiramp_shortfall_penalty,
        time = T,
--- a/src/instance/structs.jl
+++ b/src/instance/structs.jl
@@ -20,6 +20,14 @@ mutable struct StartupCategory
    cost::Float64
 end
 Base.@kwdef mutable struct Reserve
    name::String
    type::String
    amount::Vector{Float64}
    units::Vector
    shortfall_penalty::Float64
 end
 mutable struct Unit
    name::String
    bus::Bus
@@ -36,9 +44,8 @@ mutable struct Unit
    shutdown_limit::Float64
    initial_status::Union{Int,Nothing}
    initial_power::Union{Float64,Nothing}
    provides_spinning_reserves::Vector{Bool}
    provides_flexiramp_reserves::Vector{Bool}
    startup_categories::Vector{StartupCategory}
    reserves::Vector{Reserve}
 end
 mutable struct TransmissionLine
@@ -53,12 +60,6 @@ mutable struct TransmissionLine
    flow_limit_penalty::Vector{Float64}
 end
 mutable struct Reserves
    spinning::Vector{Float64}
    upflexiramp::Vector{Float64}
    dwflexiramp::Vector{Float64}
 end
 mutable struct Contingency
    name::String
    lines::Vector{TransmissionLine}
@@ -82,7 +83,8 @@ Base.@kwdef mutable struct UnitCommitmentInstance
    power_balance_penalty::Vector{Float64}
    price_sensitive_loads_by_name::Dict{AbstractString,PriceSensitiveLoad}
    price_sensitive_loads::Vector{PriceSensitiveLoad}
-    reserves::Reserves
+    reserves::Vector{Reserve}
    reserves_by_name::Dict{AbstractString,Reserve}
    shortfall_penalty::Vector{Float64}
    flexiramp_shortfall_penalty::Vector{Float64}
    time::Int
--- a/src/model/.DS_Store
+++ b/src/model/.DS_Store
--- a/src/model/build.jl
+++ b/src/model/build.jl
@@ -9,22 +9,59 @@ 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,
@@ -32,22 +69,6 @@ function build_model(;
    formulation = Formulation(),
    variable_names::Bool = false,
 )::JuMP.Model
    if formulation.ramping == WanHob2016.Ramping() &&
       instance.reserves.spinning >= ones(instance.time) .* 1e-6
        error(
            "Spinning reserves are not supported by the WanHob2016 ramping formulation",
        )
    end
    if formulation.ramping !== WanHob2016.Ramping() && (
        instance.reserves.upflexiramp >= ones(instance.time) .* 1e-6 ||
        instance.reserves.dwflexiramp >= ones(instance.time) .* 1e-6
    )
        error(
            "Flexiramp is supported only by the WanHob2016 ramping formulation",
        )
    end
    @info "Building model..."
    time_model = @elapsed begin
        model = Model()
--- a/src/model/formulations/ArrCon2000/ramp.jl
+++ b/src/model/formulations/ArrCon2000/ramp.jl
@@ -19,10 +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)
    # Gar1962.ProdVars
    prod_above = model[:prod_above]
@@ -41,7 +41,7 @@ function _add_ramp_eqs!(
                    model,
                    g.min_power[t] +
                    prod_above[gn, t] +
-                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) <=
+                    (RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0) <=
                    g.initial_power + RU
                )
            end
@@ -51,7 +51,7 @@ function _add_ramp_eqs!(
                prod_above[gn, t] +
                (
                    RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
-                    reserve[gn, t] : 0.0
+                    reserve[t] : 0.0
                )
            min_prod_last_period =
                g.min_power[t-1] * is_on[gn, t-1] + prod_above[gn, t-1]
@@ -82,7 +82,7 @@ function _add_ramp_eqs!(
                prod_above[gn, t-1] +
                (
                    RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN ?
-                    reserve[gn, t-1] : 0.0
+                    reserve[t-1] : 0.0
                )
            min_prod_this_period =
                g.min_power[t] * is_on[gn, t] + prod_above[gn, t]
--- a/src/model/formulations/DamKucRajAta2016/ramp.jl
+++ b/src/model/formulations/DamKucRajAta2016/ramp.jl
@@ -23,7 +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 = model[:reserve]
+    reserve = _total_reserves(model, g)
    # Gar1962.ProdVars
    prod_above = model[:prod_above]
@@ -48,10 +48,8 @@ function _add_ramp_eqs!(
        # end
        max_prod_this_period =
-            prod_above[gn, t] + (
+            prod_above[gn, t] +
-                RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
+            (RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0)
                reserve[gn, t] : 0.0
            )
        min_prod_last_period = 0.0
        if t > 1 && time_invariant
            min_prod_last_period = prod_above[gn, t-1]
@@ -88,7 +86,7 @@ function _add_ramp_eqs!(
        max_prod_last_period =
            min_prod_last_period + (
                t > 1 && (RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN) ?
-                reserve[gn, t-1] : 0.0
+                reserve[t-1] : 0.0
            )
        min_prod_this_period = prod_above[gn, t]
        on_last_period = 0.0
--- a/src/model/formulations/Gar1962/prod.jl
+++ b/src/model/formulations/Gar1962/prod.jl
@@ -26,7 +26,7 @@ function _add_production_limit_eqs!(
    eq_prod_limit = _init(model, :eq_prod_limit)
    is_on = model[:is_on]
    prod_above = model[:prod_above]
-    reserve = model[:reserve]
+    reserve = _total_reserves(model, g)
    gn = g.name
    for t in 1:model[:instance].time
        # Objective function terms for production costs
@@ -44,7 +44,7 @@ function _add_production_limit_eqs!(
        end
        eq_prod_limit[gn, t] = @constraint(
            model,
-            prod_above[gn, t] + reserve[gn, t] <= power_diff * is_on[gn, t]
+            prod_above[gn, t] + reserve[t] <= power_diff * is_on[gn, t]
        )
    end
 end
--- a/src/model/formulations/MorLatRam2013/ramp.jl
+++ b/src/model/formulations/MorLatRam2013/ramp.jl
@@ -22,7 +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 = model[:reserve]
+    reserve = _total_reserves(model, g)
    # Gar1962.ProdVars
    prod_above = model[:prod_above]
@@ -43,7 +43,7 @@ function _add_ramp_eqs!(
                    model,
                    g.min_power[t] +
                    prod_above[gn, t] +
-                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) <=
+                    (RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0) <=
                    g.initial_power + RU
                )
            end
@@ -61,7 +61,7 @@ function _add_ramp_eqs!(
                    prod_above[gn, t] +
                    (
                        RESERVES_WHEN_START_UP || RESERVES_WHEN_RAMP_UP ?
-                        reserve[gn, t] : 0.0
+                        reserve[t] : 0.0
                    )
                min_prod_last_period =
                    g.min_power[t-1] * is_on[gn, t-1] + prod_above[gn, t-1]
@@ -77,7 +77,7 @@ function _add_ramp_eqs!(
                eq_ramp_up[gn, t] = @constraint(
                    model,
                    prod_above[gn, t] +
-                    (RESERVES_WHEN_RAMP_UP ? reserve[gn, t] : 0.0) -
+                    (RESERVES_WHEN_RAMP_UP ? reserve[t] : 0.0) -
                    prod_above[gn, t-1] <= RU
                )
            end
@@ -105,7 +105,7 @@ function _add_ramp_eqs!(
                    prod_above[gn, t-1] +
                    (
                        RESERVES_WHEN_SHUT_DOWN || RESERVES_WHEN_RAMP_DOWN ?
-                        reserve[gn, t-1] : 0.0
+                        reserve[t-1] : 0.0
                    )
                min_prod_this_period =
                    g.min_power[t] * is_on[gn, t] + prod_above[gn, t]
@@ -121,7 +121,7 @@ function _add_ramp_eqs!(
                eq_ramp_down[gn, t] = @constraint(
                    model,
                    prod_above[gn, t-1] +
-                    (RESERVES_WHEN_RAMP_DOWN ? reserve[gn, t-1] : 0.0) -
+                    (RESERVES_WHEN_RAMP_DOWN ? reserve[t-1] : 0.0) -
                    prod_above[gn, t] <= RD
                )
            end
--- a/src/model/formulations/PanGua2016/ramp.jl
+++ b/src/model/formulations/PanGua2016/ramp.jl
@@ -12,7 +12,7 @@ function _add_ramp_eqs!(
    # TODO: Move upper case constants to model[:instance]
    RESERVES_WHEN_SHUT_DOWN = true
    gn = g.name
-    reserve = model[:reserve]
+    reserve = _total_reserves(model, g)
    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)
@@ -56,7 +56,7 @@ function _add_ramp_eqs!(
                model,
                prod_above[gn, t] +
                g.min_power[t] * is_on[gn, t] +
-                reserve[gn, t] <=
+                reserve[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
@@ -71,7 +71,7 @@ function _add_ramp_eqs!(
                    model,
                    prod_above[gn, t] +
                    g.min_power[t] * is_on[gn, t] +
-                    reserve[gn, t] <=
+                    reserve[t] <=
                    Pbar * is_on[gn, t] - sum(
                        (Pbar - (SU + i * RU)) * switch_on[gn, t-i] for
                        i in 0:min(UT - 1, TRU, t - 1)
@@ -88,7 +88,7 @@ function _add_ramp_eqs!(
                    model,
                    prod_above[gn, t] +
                    g.min_power[t] * is_on[gn, t] +
-                    (RESERVES_WHEN_SHUT_DOWN ? reserve[gn, t] : 0.0) <=
+                    (RESERVES_WHEN_SHUT_DOWN ? reserve[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
@@ -2,38 +2,12 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 function _add_flexiramp_vars!(model::JuMP.Model, g::Unit)::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)
        mfg[g.name, t] = @variable(model, lower_bound = 0)
        if g.provides_flexiramp_reserves[t]
            upflexiramp[g.name, t] = @variable(model) # up-flexiramp, ur_{it} in Wang & Hobbs (2016)
            dwflexiramp[g.name, t] = @variable(model) # down-flexiramp, dr_{it} in Wang & Hobbs (2016)
        else
            upflexiramp[g.name, t] = 0.0
            dwflexiramp[g.name, t] = 0.0
        end
        upflexiramp_shortfall[t] =
            (model[:instance].flexiramp_shortfall_penalty[t] >= 0) ?
            @variable(model, lower_bound = 0) : 0.0
        dwflexiramp_shortfall[t] =
            (model[:instance].flexiramp_shortfall_penalty[t] >= 0) ?
            @variable(model, lower_bound = 0) : 0.0
    end
    return
 end
 function _add_ramp_eqs!(
    model::JuMP.Model,
    g::Unit,
-    formulation_prod_vars::Gar1962.ProdVars,
+    ::Gar1962.ProdVars,
-    formulation_ramping::WanHob2016.Ramping,
+    ::WanHob2016.Ramping,
-    formulation_status_vars::Gar1962.StatusVars,
+    ::Gar1962.StatusVars,
 )::Nothing
    is_initially_on = (g.initial_status > 0)
    SU = g.startup_limit
@@ -51,49 +25,144 @@ function _add_ramp_eqs!(
    dwflexiramp = model[:dwflexiramp]
    mfg = model[:mfg]
-    for t in 1:model[:instance].time
+    if length(g.reserves) > 1
-        @constraint(
+        error("Each generator may only provide one flexiramp reserve")
-            model,
+    end
-            prod_above[gn, t] + (is_on[gn, t] * minp[t]) <= mfg[gn, t]
+    for r in g.reserves
-        ) # Eq. (19) in Wang & Hobbs (2016)
+        if r.type !== "flexiramp"
-        @constraint(model, mfg[gn, t] <= is_on[gn, t] * maxp[t]) # Eq. (22) in Wang & Hobbs (2016)
+            error(
-        if t != model[:instance].time
+                "This formulation only supports flexiramp reserves, not $(r.type)",
            )
        end
        rn = r.name
        for t in 1:model[:instance].time
            @constraint(
                model,
-                minp[t] * (is_on[gn, t+1] + is_on[gn, t] - 1) <=
+                prod_above[gn, t] + (is_on[gn, t] * minp[t]) <= mfg[rn, gn, t]
-                prod_above[gn, t] - dwflexiramp[gn, t] +
+            ) # Eq. (19) in Wang & Hobbs (2016)
-                (is_on[gn, t] * minp[t])
+            @constraint(model, mfg[rn, gn, t] <= is_on[gn, t] * maxp[t]) # Eq. (22) in Wang & Hobbs (2016)
-            ) # first inequality of Eq. (20) in Wang & Hobbs (2016)
+            if t != model[:instance].time
            @constraint(
                model,
                prod_above[gn, t] - dwflexiramp[gn, t] +
                (is_on[gn, t] * minp[t]) <=
                mfg[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[gn, t] +
                upflexiramp[gn, t] +
                (is_on[gn, t] * minp[t])
            ) # first inequality of Eq. (21) in Wang & Hobbs (2016)
            @constraint(
                model,
                prod_above[gn, t] +
                upflexiramp[gn, t] +
                (is_on[gn, t] * minp[t]) <=
                mfg[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[gn, t] <=
+                    minp[t] * (is_on[gn, t+1] + is_on[gn, t] - 1) <=
                    prod_above[gn, t] - dwflexiramp[rn, gn, t] +
                    (is_on[gn, t] * minp[t])
                ) # first inequality of Eq. (20) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    prod_above[gn, t] - dwflexiramp[rn, gn, t] +
                    (is_on[gn, t] * minp[t]) <=
                    mfg[rn, 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[gn, t] +
                    upflexiramp[rn, gn, t] +
                    (is_on[gn, t] * minp[t])
                ) # first inequality of Eq. (21) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    prod_above[gn, t] +
                    upflexiramp[rn, gn, t] +
                    (is_on[gn, t] * minp[t]) <=
                    mfg[rn, 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[rn, gn, t] <=
                        prod_above[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[gn, t-1] + (is_on[gn, t-1] * minp[t])) -
                        (prod_above[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[rn, 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[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[rn, 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[rn, gn, t]
                ) # first inequality of Eq. (26) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    upflexiramp[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[rn, gn, t]
                ) # first inequality of Eq. (27) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    dwflexiramp[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[rn, gn, t]
                ) # first inequality of Eq. (28) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    upflexiramp[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[rn, gn, t]
                ) # first inequality of Eq. (29) in Wang & Hobbs (2016)
                @constraint(
                    model,
                    dwflexiramp[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[rn, gn, t] <=
                    prod_above[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)
+                ) # Eq. (23) in Wang & Hobbs (2016) for the last time period
                @constraint(
                    model,
                    (prod_above[gn, t-1] + (is_on[gn, t-1] * minp[t])) -
@@ -101,85 +170,8 @@ function _add_ramp_eqs!(
                    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)
+                ) # Eq. (25) in Wang & Hobbs (2016) for the last time period
            else
                @constraint(
                    model,
                    mfg[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[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[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[gn, t]
            ) # first inequality of Eq. (26) in Wang & Hobbs (2016)
            @constraint(
                model,
                upflexiramp[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[gn, t]
            ) # first inequality of Eq. (27) in Wang & Hobbs (2016)
            @constraint(
                model,
                dwflexiramp[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[gn, t]
            ) # first inequality of Eq. (28) in Wang & Hobbs (2016)
            @constraint(model, upflexiramp[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[gn, t]) # first inequality of Eq. (29) in Wang & Hobbs (2016)
            @constraint(
                model,
                dwflexiramp[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[gn, t] <=
                prod_above[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[gn, t-1] + (is_on[gn, t-1] * minp[t])) -
                (prod_above[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
--- a/src/model/formulations/WanHob2016/structs.jl
+++ b/src/model/formulations/WanHob2016/structs.jl
@@ -4,6 +4,7 @@
 """
 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.
--- a/src/model/formulations/base/structs.jl
+++ b/src/model/formulations/base/structs.jl
@@ -9,6 +9,27 @@ 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
@@ -38,10 +59,10 @@ end
 """
    struct ShiftFactorsFormulation <: TransmissionFormulation
-        isf_cutoff::Float64
+        isf_cutoff::Float64 = 0.005
-        lodf_cutoff::Float64
+        lodf_cutoff::Float64 = 0.001
-        precomputed_isf::Union{Nothing,Matrix{Float64}}
+        precomputed_isf=nothing
-        precomputed_lodf::Union{Nothing,Matrix{Float64}}
+        precomputed_lodf=nothing
    end
 Transmission formulation based on Injection Shift Factors (ISF) and Line
@@ -49,15 +70,15 @@ Outage Distribution Factors (LODF). Constraints are enforced in a lazy way.
 Arguments
 ---------
- `precomputed_isf::Union{Matrix{Float64},Nothing} = nothing`:
+- `precomputed_isf`:
    the injection shift factors matrix. If not provided, it will be computed.
- `precomputed_lodf::Union{Matrix{Float64},Nothing} = nothing`: 
+- `precomputed_lodf`: 
    the line outage distribution factors matrix. If not provided, it will be
    computed.
- `isf_cutoff::Float64 = 0.005`: 
+- `isf_cutoff`: 
    the cutoff that should be applied to the ISF matrix. Entries with magnitude
    smaller than this value will be set to zero.
- `lodf_cutoff::Float64 = 0.001`: 
+- `lodf_cutoff`: 
    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
@@ -4,8 +4,8 @@
 function _add_system_wide_eqs!(model::JuMP.Model)::Nothing
    _add_net_injection_eqs!(model)
-    _add_reserve_eqs!(model)
+    _add_spinning_reserve_eqs!(model)
-    _add_flexiramp_eqs!(model)
+    _add_flexiramp_reserve_eqs!(model)
    return
 end
@@ -28,74 +28,69 @@ function _add_net_injection_eqs!(model::JuMP.Model)::Nothing
    return
 end
-function _add_reserve_eqs!(model::JuMP.Model)::Nothing
+function _add_spinning_reserve_eqs!(model::JuMP.Model)::Nothing
    eq_min_reserve = _init(model, :eq_min_reserve)
    instance = model[:instance]
-    for t in 1:instance.time
+    eq_min_spinning_reserve = _init(model, :eq_min_spinning_reserve)
-        # Equation (68) in Kneuven et al. (2020)
+    for r in instance.reserves
-        # As in Morales-España et al. (2013a)
+        r.type == "spinning" || continue
-        # Akin to the alternative formulation with max_power_avail
+        for t in 1:instance.time
-        # from Carrión and Arroyo (2006) and Ostrowski et al. (2012)
+            # Equation (68) in Kneuven et al. (2020)
-        shortfall_penalty = instance.shortfall_penalty[t]
+            # As in Morales-España et al. (2013a)
-        eq_min_reserve[t] = @constraint(
+            # Akin to the alternative formulation with max_power_avail
-            model,
+            # from Carrión and Arroyo (2006) and Ostrowski et al. (2012)
-            sum(model[:reserve][g.name, t] for g in instance.units) +
+            eq_min_spinning_reserve[r.name, t] = @constraint(
-            (shortfall_penalty >= 0 ? model[:reserve_shortfall][t] : 0.0) >=
+                model,
-            instance.reserves.spinning[t]
+                sum(model[:reserve][r.name, g.name, t] for g in r.units) +
-        )
+                model[:reserve_shortfall][r.name, t] >= r.amount[t]
        # Account for shortfall contribution to objective
        if shortfall_penalty >= 0
            add_to_expression!(
                model[:obj],
                shortfall_penalty,
                model[:reserve_shortfall][t],
            )
            # Account for shortfall contribution to objective
            if r.shortfall_penalty >= 0
                add_to_expression!(
                    model[:obj],
                    r.shortfall_penalty,
                    model[:reserve_shortfall][r.name, t],
                )
            end
        end
    end
    return
 end
-function _add_flexiramp_eqs!(model::JuMP.Model)::Nothing
+function _add_flexiramp_reserve_eqs!(model::JuMP.Model)::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[t] and eq_min_dwflexiramp[t] 
+    #       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)
    instance = model[:instance]
-    for t in 1:instance.time
+    for r in instance.reserves
-        flexiramp_shortfall_penalty = instance.flexiramp_shortfall_penalty[t]
+        r.type == "flexiramp" || continue
-        # Eq. (17) in Wang & Hobbs (2016)
+        for t in 1:instance.time
-        eq_min_upflexiramp[t] = @constraint(
+            # Eq. (17) in Wang & Hobbs (2016)
-            model,
+            eq_min_upflexiramp[r.name, t] = @constraint(
-            sum(model[:upflexiramp][g.name, t] for g in instance.units) +
+                model,
-            (
+                sum(model[:upflexiramp][r.name, g.name, t] for g in r.units) + model[:upflexiramp_shortfall][r.name, t] >= r.amount[t]
                flexiramp_shortfall_penalty >= 0 ?
                model[:upflexiramp_shortfall][t] : 0.0
            ) >= instance.reserves.upflexiramp[t]
        )
        # Eq. (18) in Wang & Hobbs (2016)
        eq_min_dwflexiramp[t] = @constraint(
            model,
            sum(model[:dwflexiramp][g.name, t] for g in instance.units) +
            (
                flexiramp_shortfall_penalty >= 0 ?
                model[:dwflexiramp_shortfall][t] : 0.0
            ) >= instance.reserves.dwflexiramp[t]
        )
        # Account for flexiramp shortfall contribution to objective
        if flexiramp_shortfall_penalty >= 0
            add_to_expression!(
                model[:obj],
                flexiramp_shortfall_penalty,
                (
                    model[:upflexiramp_shortfall][t] +
                    model[:dwflexiramp_shortfall][t]
                ),
            )
            # Eq. (18) in Wang & Hobbs (2016)
            eq_min_dwflexiramp[r.name, t] = @constraint(
                model,
                sum(model[:dwflexiramp][r.name, g.name, t] for g in r.units) + model[:dwflexiramp_shortfall][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,
                    (
                        model[:upflexiramp_shortfall][r.name, t] +
                        model[:dwflexiramp_shortfall][r.name, t]
                    ),
                )
            end
        end
    end
    return
--- a/src/model/formulations/base/unit.jl
+++ b/src/model/formulations/base/unit.jl
@@ -12,8 +12,8 @@ function _add_unit!(model::JuMP.Model, g::Unit, formulation::Formulation)
    # Variables
    _add_production_vars!(model, g, formulation.prod_vars)
-    _add_reserve_vars!(model, g)
+    _add_spinning_reserve_vars!(model, g)
-    _add_flexiramp_vars!(model, g)
+    _add_flexiramp_reserve_vars!(model, g)
    _add_startup_shutdown_vars!(model, g)
    _add_status_vars!(model, g, formulation.status_vars)
@@ -43,26 +43,48 @@ end
 _is_initially_on(g::Unit)::Float64 = (g.initial_status > 0 ? 1.0 : 0.0)
-function _add_reserve_vars!(model::JuMP.Model, g::Unit)::Nothing
+function _add_spinning_reserve_vars!(model::JuMP.Model, g::Unit)::Nothing
    reserve = _init(model, :reserve)
    reserve_shortfall = _init(model, :reserve_shortfall)
-    for t in 1:model[:instance].time
+    for r in g.reserves
-        if g.provides_spinning_reserves[t]
+        r.type == "spinning" || continue
-            reserve[g.name, t] = @variable(model, lower_bound = 0)
+        for t in 1:model[:instance].time
-        else
+            reserve[r.name, g.name, t] = @variable(model, lower_bound = 0)
-            reserve[g.name, t] = 0.0
+            if (r.name, t) ∉ keys(reserve_shortfall)
                reserve_shortfall[r.name, t] = @variable(model, lower_bound = 0)
                if r.shortfall_penalty < 0
                    set_upper_bound(reserve_shortfall[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_reserve_eqs!(model::JuMP.Model, g::Unit)::Nothing
+function _add_flexiramp_reserve_vars!(model::JuMP.Model, g::Unit)::Nothing
-    reserve = model[:reserve]
+    upflexiramp = _init(model, :upflexiramp)
-    for t in 1:model[:instance].time
+    upflexiramp_shortfall = _init(model, :upflexiramp_shortfall)
-        add_to_expression!(expr_reserve[g.bus.name, t], reserve[g.name, t], 1.0)
+    mfg = _init(model, :mfg)
    dwflexiramp = _init(model, :dwflexiramp)
    dwflexiramp_shortfall = _init(model, :dwflexiramp_shortfall)
    for r in g.reserves
        r.type == "flexiramp" || continue
        for t in 1:model[:instance].time
            # maximum feasible generation, \bar{g_{its}} in Wang & Hobbs (2016)
            mfg[r.name, g.name, t] = @variable(model, lower_bound = 0)
            upflexiramp[r.name, g.name, t] = @variable(model) # up-flexiramp, ur_{it} in Wang & Hobbs (2016)
            dwflexiramp[r.name, g.name, t] = @variable(model) # down-flexiramp, dr_{it} in Wang & Hobbs (2016)
            if (r.name, t) ∉ keys(upflexiramp_shortfall)
                upflexiramp_shortfall[r.name, t] =
                    @variable(model, lower_bound = 0)
                dwflexiramp_shortfall[r.name, t] =
                    @variable(model, lower_bound = 0)
                if r.shortfall_penalty < 0
                    set_upper_bound(upflexiramp_shortfall[r.name, t], 0.0)
                    set_upper_bound(dwflexiramp_shortfall[r.name, t], 0.0)
                end
            end
        end
    end
    return
 end
@@ -82,7 +104,7 @@ function _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
    eq_startup_limit = _init(model, :eq_startup_limit)
    is_on = model[:is_on]
    prod_above = model[:prod_above]
-    reserve = model[:reserve]
+    reserve = _total_reserves(model, g)
    switch_off = model[:switch_off]
    switch_on = model[:switch_on]
    T = model[:instance].time
@@ -90,7 +112,7 @@ function _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
        # Startup limit
        eq_startup_limit[g.name, t] = @constraint(
            model,
-            prod_above[g.name, t] + reserve[g.name, t] <=
+            prod_above[g.name, t] + reserve[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]
        )
@@ -118,7 +140,7 @@ function _add_ramp_eqs!(
    formulation::RampingFormulation,
 )::Nothing
    prod_above = model[:prod_above]
-    reserve = model[:reserve]
+    reserve = _total_reserves(model, g)
    eq_ramp_up = _init(model, :eq_ramp_up)
    eq_ramp_down = _init(model, :eq_ramp_down)
    for t in 1:model[:instance].time
@@ -127,14 +149,14 @@ function _add_ramp_eqs!(
            if _is_initially_on(g) == 1
                eq_ramp_up[g.name, t] = @constraint(
                    model,
-                    prod_above[g.name, t] + reserve[g.name, t] <=
+                    prod_above[g.name, t] + reserve[t] <=
                    (g.initial_power - g.min_power[t]) + g.ramp_up_limit
                )
            end
        else
            eq_ramp_up[g.name, t] = @constraint(
                model,
-                prod_above[g.name, t] + reserve[g.name, t] <=
+                prod_above[g.name, t] + reserve[t] <=
                prod_above[g.name, t-1] + g.ramp_up_limit
            )
        end
@@ -217,3 +239,15 @@ function _add_net_injection_eqs!(model::JuMP.Model, g::Unit)::Nothing
        )
    end
 end
 function _total_reserves(model, g)::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][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
@@ -18,15 +18,28 @@ function fix!(model::JuMP.Model, solution::AbstractDict)::Nothing
            is_on_value = round(solution["Is on"][g.name][t])
            prod_value =
                round(solution["Production (MW)"][g.name][t], digits = 5)
            reserve_value =
                round(solution["Reserve (MW)"][g.name][t], digits = 5)
            JuMP.fix(is_on[g.name, t], is_on_value, force = true)
            JuMP.fix(
                prod_above[g.name, t],
                prod_value - is_on_value * g.min_power[t],
                force = true,
            )
-            JuMP.fix(reserve[g.name, t], reserve_value, force = true)
+        end
    end
    for r in instance.reserves
        r.type == "spinning" || continue
        for g in r.units
            for t in 1:T
                reserve_value = round(
                    solution["Spinning reserve (MW)"][r.name][g.name][t],
                    digits = 5,
                )
                JuMP.fix(
                    reserve[r.name, g.name, t],
                    reserve_value,
                    force = true,
                )
            end
        end
    end
    return
--- a/src/solution/methods/XavQiuWanThi2019/structs.jl
+++ b/src/solution/methods/XavQiuWanThi2019/structs.jl
@@ -2,14 +2,6 @@
 # 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
 """
@@ -21,6 +13,13 @@ 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
 ------
--- 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.
 """
-    function optimize!(model::JuMP.Model)::Nothing
+    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
@@ -2,6 +2,18 @@
 # 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)
@@ -50,37 +62,6 @@ function solution(model::JuMP.Model)::OrderedDict
    sol["Is on"] = timeseries(model[:is_on], instance.units)
    sol["Switch on"] = timeseries(model[:switch_on], instance.units)
    sol["Switch off"] = timeseries(model[:switch_off], instance.units)
    if instance.reserves.upflexiramp != zeros(T) ||
       instance.reserves.dwflexiramp != zeros(T)
        # Report flexiramp solutions only if either of the up-flexiramp and  
        # down-flexiramp requirements is not a default array of zeros
        sol["Up-flexiramp (MW)"] =
            timeseries(model[:upflexiramp], instance.units)
        sol["Up-flexiramp shortfall (MW)"] = OrderedDict(
            t =>
                (instance.flexiramp_shortfall_penalty[t] >= 0) ?
                round(value(model[:upflexiramp_shortfall][t]), digits = 5) :
                0.0 for t in 1:instance.time
        )
        sol["Down-flexiramp (MW)"] =
            timeseries(model[:dwflexiramp], instance.units)
        sol["Down-flexiramp shortfall (MW)"] = OrderedDict(
            t =>
                (instance.flexiramp_shortfall_penalty[t] >= 0) ?
                round(value(model[:dwflexiramp_shortfall][t]), digits = 5) :
                0.0 for t in 1:instance.time
        )
    else
        # Report spinning reserve solutions only if both up-flexiramp and  
        # down-flexiramp requirements are arrays of zeros.
        sol["Reserve (MW)"] = timeseries(model[:reserve], instance.units)
        sol["Reserve shortfall (MW)"] = OrderedDict(
            t =>
                (instance.shortfall_penalty[t] >= 0) ?
                round(value(model[:reserve_shortfall][t]), digits = 5) :
                0.0 for t in 1:instance.time
        )
    end
    sol["Net injection (MW)"] =
        timeseries(model[:net_injection], instance.buses)
    sol["Load curtail (MW)"] = timeseries(model[:curtail], instance.buses)
@@ -91,5 +72,47 @@ function solution(model::JuMP.Model)::OrderedDict
        sol["Price-sensitive loads (MW)"] =
            timeseries(model[:loads], instance.price_sensitive_loads)
    end
    sol["Spinning reserve (MW)"] = OrderedDict(
        r.name => OrderedDict(
            g.name => [
                value(model[:reserve][r.name, g.name, t]) for
                t in 1:instance.time
            ] for g in r.units
        ) for r in instance.reserves if r.type == "spinning"
    )
    sol["Spinning reserve shortfall (MW)"] = OrderedDict(
        r.name => [
            value(model[:reserve_shortfall][r.name, t]) for
            t in 1:instance.time
        ] for r in instance.reserves if r.type == "spinning"
    )
    sol["Up-flexiramp (MW)"] = OrderedDict(
        r.name => OrderedDict(
            g.name => [
                value(model[:upflexiramp][r.name, g.name, t]) for
                t in 1:instance.time
            ] for g in r.units
        ) for r in instance.reserves if r.type == "flexiramp"
    )
    sol["Up-flexiramp shortfall (MW)"] = OrderedDict(
        r.name => [
            value(model[:upflexiramp_shortfall][r.name, t]) for
            t in 1:instance.time
        ] for r in instance.reserves if r.type == "flexiramp"
    )
    sol["Down-flexiramp (MW)"] = OrderedDict(
        r.name => OrderedDict(
            g.name => [
                value(model[:dwflexiramp][r.name, g.name, t]) for
                t in 1:instance.time
            ] for g in r.units
        ) for r in instance.reserves if r.type == "flexiramp"
    )
    sol["Down-flexiramp shortfall (MW)"] = OrderedDict(
        r.name => [
            value(model[:upflexiramp_shortfall][r.name, t]) for
            t in 1:instance.time
        ] for r in instance.reserves if r.type == "flexiramp"
    )
    return sol
 end
--- a/src/solution/write.jl
+++ b/src/solution/write.jl
@@ -2,6 +2,18 @@
 # 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/randomize/XavQiuAhm2021.jl
+++ b/src/transform/randomize/XavQiuAhm2021.jl
@@ -2,13 +2,6 @@
 # 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
@@ -55,6 +48,13 @@ 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)
@@ -212,4 +212,31 @@ function randomize!(
    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
@@ -12,10 +12,11 @@ conditions are also not modified.
 Example
 -------
-    # Build a 2-hour UC instance
+```julia
-    instance = UnitCommitment.read_benchmark("test/case14")
+# Build a 2-hour UC instance
-    modified = UnitCommitment.slice(instance, 1:2)
+instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
-
+modified = UnitCommitment.slice(instance, 1:2)
 ```
 """
 function slice(
    instance::UnitCommitmentInstance,
@@ -24,13 +25,14 @@ function slice(
    modified = deepcopy(instance)
    modified.time = length(range)
    modified.power_balance_penalty = modified.power_balance_penalty[range]
-    modified.reserves.spinning = modified.reserves.spinning[range]
+    for r in modified.reserves
        r.amount = r.amount[range]
    end
    for u in modified.units
        u.max_power = u.max_power[range]
        u.min_power = u.min_power[range]
        u.must_run = u.must_run[range]
        u.min_power_cost = u.min_power_cost[range]
        u.provides_spinning_reserves = u.provides_spinning_reserves[range]
        for s in u.cost_segments
            s.mw = s.mw[range]
            s.cost = s.cost[range]
--- a/src/validation/validate.jl
+++ b/src/validation/validate.jl
@@ -40,12 +40,19 @@ function validate(
    return true
 end
-function _validate_units(instance, solution; tol = 0.01)
+function _validate_units(instance::UnitCommitmentInstance, solution; tol = 0.01)
    err_count = 0
    for unit in instance.units
        production = solution["Production (MW)"][unit.name]
-        reserve = solution["Reserve (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["Spinning reserve (MW)"][r.name][unit.name] for
                r in spinning_reserves
            )
        end
        actual_production_cost = solution["Production cost (\$)"][unit.name]
        actual_startup_cost = solution["Startup cost (\$)"][unit.name]
        is_on = bin(solution["Is on"][unit.name])
@@ -99,13 +106,18 @@ function _validate_units(instance, solution; tol = 0.01)
            end
            # Verify reserve eligibility
-            if !unit.provides_spinning_reserves[t] && reserve[t] > tol
+            for r in instance.reserves
-                @error @sprintf(
+                if r.type == "spinning"
-                    "Unit %s is not eligible to provide spinning reserves at time %d",
+                    if unit ∉ r.units &&
-                    unit.name,
+                       (unit in keys(solution["Spinning reserve (MW)"][r.name]))
-                    t
+                        @error @sprintf(
-                )
+                            "Unit %s is not eligible to provide reserve %s",
-                err_count += 1
+                            unit.name,
                            r.name,
                        )
                        err_count += 1
                    end
                end
            end
            # If unit is on, must produce at least its minimum power
@@ -137,9 +149,11 @@ function _validate_units(instance, solution; tol = 0.01)
            # 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 %s produces power at time %d while off (%.2f + %.2f > 0)",
                    unit.name,
-                    t
+                    t,
                    production[t],
                    reserve[t],
                )
                err_count += 1
            end
@@ -321,67 +335,65 @@ function _validate_reserve_and_demand(instance, solution, tol = 0.01)
            err_count += 1
        end
-        # Verify flexiramp solutions only if either of the up-flexiramp and  
+        # Verify reserves
-        # down-flexiramp requirements is not a default array of zeros
+        for r in instance.reserves
-        if instance.reserves.upflexiramp != zeros(instance.time) ||
+            if r.type == "spinning"
-           instance.reserves.dwflexiramp != zeros(instance.time)
+                provided = sum(
-            upflexiramp = sum(
+                    solution["Spinning reserve (MW)"][r.name][g.name][t] for
-                solution["Up-flexiramp (MW)"][g.name][t] for
+                    g in r.units
                g in instance.units
            )
            upflexiramp_shortfall =
                (instance.flexiramp_shortfall_penalty[t] >= 0) ?
                solution["Up-flexiramp shortfall (MW)"][t] : 0
            if upflexiramp + upflexiramp_shortfall <
               instance.reserves.upflexiramp[t] - tol
                @error @sprintf(
                    "Insufficient up-flexiramp at time %d (%.2f + %.2f should be %.2f)",
                    t,
                    upflexiramp,
                    upflexiramp_shortfall,
                    instance.reserves.upflexiramp[t],
                )
-                err_count += 1
+                shortfall =
-            end
+                    solution["Spinning reserve shortfall (MW)"][r.name][t]
                required = r.amount[t]
-            dwflexiramp = sum(
+                if provided + shortfall < required - tol
-                solution["Down-flexiramp (MW)"][g.name][t] for
+                    @error @sprintf(
-                g in instance.units
+                        "Insufficient reserve %s at time %d (%.2f + %.2f < %.2f)",
-            )
+                        r.name,
-            dwflexiramp_shortfall =
+                        t,
-                (instance.flexiramp_shortfall_penalty[t] >= 0) ?
+                        provided,
-                solution["Down-flexiramp shortfall (MW)"][t] : 0
+                        shortfall,
-
+                        required,
-            if dwflexiramp + dwflexiramp_shortfall <
+                    )
-               instance.reserves.dwflexiramp[t] - tol
+                end
-                @error @sprintf(
+            elseif r.type == "flexiramp"
-                    "Insufficient down-flexiramp at time %d (%.2f + %.2f should be %.2f)",
+                upflexiramp = sum(
-                    t,
+                    solution["Up-flexiramp (MW)"][r.name][g.name][t] for
-                    dwflexiramp,
+                    g in r.units
                    dwflexiramp_shortfall,
                    instance.reserves.dwflexiramp[t],
                )
-                err_count += 1
+                upflexiramp_shortfall =
-            end
+                    solution["Up-flexiramp shortfall (MW)"][r.name][t]
            # Verify spinning reserve solutions only if both up-flexiramp and  
            # down-flexiramp requirements are arrays of zeros.
        else
            reserve =
                sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
            reserve_shortfall =
                (instance.shortfall_penalty[t] >= 0) ?
                solution["Reserve shortfall (MW)"][t] : 0
-            if reserve + reserve_shortfall < instance.reserves.spinning[t] - tol
+                if upflexiramp + upflexiramp_shortfall < r.amount[t] - tol
-                @error @sprintf(
+                    @error @sprintf(
-                    "Insufficient spinning reserves at time %d (%.2f + %.2f should be %.2f)",
+                        "Insufficient up-flexiramp at time %d (%.2f + %.2f < %.2f)",
-                    t,
+                        t,
-                    reserve,
+                        upflexiramp,
-                    reserve_shortfall,
+                        upflexiramp_shortfall,
-                    instance.reserves.spinning[t],
+                        r.amount[t],
                    )
                    err_count += 1
                end
                dwflexiramp = sum(
                    solution["Down-flexiramp (MW)"][r.name][g.name][t] for
                    g in r.units
                )
-                err_count += 1
+                dwflexiramp_shortfall =
                    solution["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: $(r.type)")
            end
        end
    end
--- a/test/fixtures/case118-initcond.json.gz
+++ b/test/fixtures/case118-initcond.json.gz
--- a/test/fixtures/case14-flex.json.gz
+++ b/test/fixtures/case14-flex.json.gz
--- a/test/fixtures/case14-sub-hourly.json.gz
+++ b/test/fixtures/case14-sub-hourly.json.gz
--- a/test/fixtures/case14.json.gz
+++ b/test/fixtures/case14.json.gz
--- a/test/fixtures/ucjl-0.2.json.gz
+++ b/test/fixtures/ucjl-0.2.json.gz
--- a/test/fixtures/ucjl-0.3.json.gz
+++ b/test/fixtures/ucjl-0.3.json.gz
--- a/test/import/egret_test.jl
+++ b/test/import/egret_test.jl
@@ -4,12 +4,9 @@
 using UnitCommitment
 basedir = @__DIR__
@testset "read_egret_solution" begin
-    solution = UnitCommitment.read_egret_solution(
+    solution =
-        "$basedir/../fixtures/egret_output.json.gz",
+        UnitCommitment.read_egret_solution("$FIXTURES/egret_output.json.gz")
    )
    for attr in ["Is on", "Production (MW)", "Production cost (\$)"]
        @test attr in keys(solution)
        @test "115_STEAM_1" in keys(solution[attr])
--- a/test/instance/migrate_test.jl
+++ b/test/instance/migrate_test.jl
@@ -0,0 +1,18 @@
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
@testset "read v0.2" begin
    instance = UnitCommitment.read("$FIXTURES/ucjl-0.2.json.gz")
    @test length(instance.reserves_by_name["r1"].amount) == 4
    @test instance.units_by_name["g2"].reserves[1].name == "r1"
 end
@testset "read v0.3" begin
    instance = UnitCommitment.read("$FIXTURES/ucjl-0.3.json.gz")
    @test length(instance.units) == 6
    @test length(instance.buses) == 14
    @test length(instance.lines) == 20
 end
--- a/test/instance/read_test.jl
+++ b/test/instance/read_test.jl
@@ -5,7 +5,7 @@
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
@testset "read_benchmark" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    @test length(instance.lines) == 20
    @test length(instance.buses) == 14
@@ -37,6 +37,11 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
    @test instance.buses[9].load == [35.36638, 33.25495, 31.67138, 31.14353]
    @test instance.buses_by_name["b9"].name == "b9"
    @test instance.reserves[1].name == "r1"
    @test instance.reserves[1].type == "spinning"
    @test instance.reserves[1].amount == [100.0, 100.0, 100.0, 100.0]
    @test instance.reserves_by_name["r1"].name == "r1"
    unit = instance.units[1]
    @test unit.name == "g1"
    @test unit.bus.name == "b1"
@@ -48,7 +53,6 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
    @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
@@ -64,11 +68,13 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
    @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 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]
    @test length(unit.reserves) == 1
    unit = instance.units[3]
    @test unit.name == "g3"
@@ -81,7 +87,6 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
    @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
@@ -90,8 +95,8 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
        @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 instance.reserves.spinning == zeros(4)
+    @test unit.reserves[1].name == "r1"
    @test instance.contingencies[1].lines == [instance.lines[1]]
    @test instance.contingencies[1].units == []
@@ -107,7 +112,7 @@ using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
 end
@testset "read_benchmark sub-hourly" begin
-    instance = UnitCommitment.read_benchmark("test/case14-sub-hourly")
+    instance = UnitCommitment.read("$FIXTURES/case14-sub-hourly.json.gz")
    @test instance.time == 4
    unit = instance.units[1]
    @test unit.name == "g1"
--- a/test/model/formulations_test.jl
+++ b/test/model/formulations_test.jl
@@ -5,6 +5,7 @@
 using UnitCommitment
 using JuMP
 using Cbc
 using JSON
 import UnitCommitment:
    ArrCon2000,
    CarArr2006,
@@ -19,42 +20,65 @@ import UnitCommitment:
 function _test(
    formulation::Formulation;
-    instances::Array{String} = ["test/case14"],
+    instances = ["case14"],
-    optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0),
+    dump::Bool = false,
 )::Nothing
    for instance_name in instances
-        instance = UnitCommitment.read_benchmark(instance_name)
+        instance = UnitCommitment.read("$(FIXTURES)/$(instance_name).json.gz")
        model = UnitCommitment.build_model(
            instance = instance,
            formulation = formulation,
-            optimizer = optimizer,
+            optimizer = Cbc.Optimizer,
-        )
+            variable_names = true,
        UnitCommitment.optimize!(
            model,
            XavQiuWanThi2019.Method(two_phase_gap = false, gap_limit = 0.1),
        )
        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
@testset "formulations" begin
-    _test(Formulation())
+    @testset "default" begin
-    _test(Formulation(ramping = ArrCon2000.Ramping()))
+        _test(Formulation())
-
+    end
-    # _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
+    @testset "ArrCon2000" begin
-    _test(
+        _test(Formulation(ramping = ArrCon2000.Ramping()))
-        Formulation(
+    end
-            ramping = MorLatRam2013.Ramping(),
+    @testset "DamKucRajAta2016" begin
-            startup_costs = MorLatRam2013.StartupCosts(),
+        _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
-        ),
+    end
-    )
+    @testset "MorLatRam2013" begin
-    _test(Formulation(ramping = PanGua2016.Ramping()))
+        _test(
-    _test(Formulation(pwl_costs = Gar1962.PwlCosts()))
+            Formulation(
-    _test(Formulation(pwl_costs = CarArr2006.PwlCosts()))
+                ramping = MorLatRam2013.Ramping(),
-    _test(Formulation(pwl_costs = KnuOstWat2018.PwlCosts()))
+                startup_costs = MorLatRam2013.StartupCosts(),
-    _test(
+            ),
-        Formulation(ramping = WanHob2016.Ramping()),
+        )
-        instances = ["test/case14-flex"],
+    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
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -8,6 +8,8 @@ using UnitCommitment
 push!(Base.LOAD_PATH, @__DIR__)
 UnitCommitment._setup_logger(level = Base.CoreLogging.Error)
 FIXTURES = "$(@__DIR__)/fixtures"
@testset "UnitCommitment" begin
    include("usage.jl")
    @testset "import" begin
@@ -15,14 +17,17 @@ UnitCommitment._setup_logger(level = Base.CoreLogging.Error)
    end
    @testset "instance" begin
        include("instance/read_test.jl")
        include("instance/migrate_test.jl")
    end
    @testset "model" begin
        include("model/formulations_test.jl")
    end
-    @testset "XavQiuWanThi19" begin
+    @testset "solution" begin
-        include("solution/methods/XavQiuWanThi19/filter_test.jl")
+        @testset "XavQiuWanThi19" begin
-        include("solution/methods/XavQiuWanThi19/find_test.jl")
+            include("solution/methods/XavQiuWanThi19/filter_test.jl")
-        include("solution/methods/XavQiuWanThi19/sensitivity_test.jl")
+            include("solution/methods/XavQiuWanThi19/find_test.jl")
            include("solution/methods/XavQiuWanThi19/sensitivity_test.jl")
        end
    end
    @testset "transform" begin
        include("transform/initcond_test.jl")
--- a/test/solution/methods/XavQiuWanThi19/filter_test.jl
+++ b/test/solution/methods/XavQiuWanThi19/filter_test.jl
@@ -6,7 +6,7 @@ using UnitCommitment, Test, LinearAlgebra
 import UnitCommitment: _Violation, _offer, _query
@testset "_ViolationFilter" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    filter = UnitCommitment._ViolationFilter(max_per_line = 1, max_total = 2)
    _offer(
--- a/test/solution/methods/XavQiuWanThi19/find_test.jl
+++ b/test/solution/methods/XavQiuWanThi19/find_test.jl
@@ -6,7 +6,7 @@ using UnitCommitment, Test, LinearAlgebra
 import UnitCommitment: _Violation, _offer, _query
@testset "find_violations" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    for line in instance.lines, t in 1:instance.time
        line.normal_flow_limit[t] = 1.0
        line.emergency_flow_limit[t] = 1.0
--- a/test/solution/methods/XavQiuWanThi19/sensitivity_test.jl
+++ b/test/solution/methods/XavQiuWanThi19/sensitivity_test.jl
@@ -5,7 +5,7 @@
 using UnitCommitment, Test, LinearAlgebra
@testset "_susceptance_matrix" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    actual = UnitCommitment._susceptance_matrix(instance.lines)
    @test size(actual) == (20, 20)
    expected = Diagonal([
@@ -34,7 +34,7 @@ using UnitCommitment, Test, LinearAlgebra
 end
@testset "_reduced_incidence_matrix" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    actual = UnitCommitment._reduced_incidence_matrix(
        lines = instance.lines,
        buses = instance.buses,
@@ -81,7 +81,7 @@ end
 end
@testset "_injection_shift_factors" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    actual = UnitCommitment._injection_shift_factors(
        lines = instance.lines,
        buses = instance.buses,
@@ -112,7 +112,7 @@ end
 end
@testset "_line_outage_factors" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    isf_before = UnitCommitment._injection_shift_factors(
        lines = instance.lines,
        buses = instance.buses,
--- a/test/transform/initcond_test.jl
+++ b/test/transform/initcond_test.jl
@@ -4,12 +4,9 @@
 using UnitCommitment, Cbc, JuMP
 basedir = @__DIR__
@testset "generate_initial_conditions!" begin
    # Load instance
-    instance =
+    instance = UnitCommitment.read("$FIXTURES/case118-initcond.json.gz")
        UnitCommitment.read("$basedir/../fixtures/case118-initcond.json.gz")
    optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
    # All units should have unknown initial conditions
--- a/test/transform/randomize/XavQiuAhm2021_test.jl
+++ b/test/transform/randomize/XavQiuAhm2021_test.jl
@@ -30,7 +30,9 @@ test_approx(x, y) = @test isapprox(x, y, atol = 1e-3)
        randomize!(
            instance,
-            XavQiuAhm2021.Randomization(randomize_load_profile = false),
+            method = XavQiuAhm2021.Randomization(
                randomize_load_profile = false,
            ),
            rng = MersenneTwister(42),
        )
--- a/test/transform/slice_test.jl
+++ b/test/transform/slice_test.jl
@@ -5,19 +5,18 @@
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON, GZip
@testset "slice" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    modified = UnitCommitment.slice(instance, 1:2)
    # Should update all time-dependent fields
    @test modified.time == 2
    @test length(modified.power_balance_penalty) == 2
-    @test length(modified.reserves.spinning) == 2
+    @test length(modified.reserves_by_name["r1"].amount) == 2
    for u in modified.units
        @test length(u.max_power) == 2
        @test length(u.min_power) == 2
        @test length(u.must_run) == 2
        @test length(u.min_power_cost) == 2
        @test length(u.provides_spinning_reserves) == 2
        for s in u.cost_segments
            @test length(s.mw) == 2
            @test length(s.cost) == 2
--- a/test/usage.jl
+++ b/test/usage.jl
@@ -4,8 +4,8 @@
 using UnitCommitment, LinearAlgebra, Cbc, JuMP, JSON
-@testset "build_model" begin
+@testset "usage" begin
-    instance = UnitCommitment.read_benchmark("test/case14")
+    instance = UnitCommitment.read("$FIXTURES/case14.json.gz")
    for line in instance.lines, t in 1:4
        line.normal_flow_limit[t] = 10.0
    end
--- a/test/validation/repair_test.jl
+++ b/test/validation/repair_test.jl
@@ -4,11 +4,9 @@
 using UnitCommitment, JSON, GZip, DataStructures
 basedir = @__DIR__
 function parse_case14()
    return JSON.parse(
-        GZip.gzopen("$basedir/../../instances/test/case14.json.gz"),
+        GZip.gzopen("$FIXTURES/case14.json.gz"),
        dicttype = () -> DefaultOrderedDict(nothing),
    )
 end
Author	SHA1	Message	Date
Alinson S. Xavier	6573bb7ea2	Update README.md	2022-07-18 09:54:15 -06:00
Alinson S. Xavier	1769f2a932	Project.toml: Remove Revise.jl	2022-07-18 09:42:00 -06:00
Alinson S. Xavier	4dc39363e8	Update references, copyright notices, links	2022-07-18 09:40:52 -06:00
Alinson S. Xavier	5fef01cd99	Improve docs	2022-07-17 15:50:42 -06:00
Alinson S. Xavier	18daaf5358	Switch to Documenter.jl	2022-07-17 14:44:58 -06:00
Alinson S. Xavier	b68b4ff9e4	Update CHANGELOG and docs	2022-07-13 10:14:42 -05:00
Alinson S. Xavier	6e30645084	Allow v0.3 to read v0.2 instance files	2022-07-12 11:57:55 -05:00
Alinson S. Xavier	678e6aa2f5	Update docs	2022-07-11 12:16:06 -05:00
Alinson S. Xavier	fd25580967	Reformat source code	2022-07-11 10:58:42 -05:00
Alinson S Xavier	dc693896a3	Merge branch 'dev' into feature/reserves	2022-06-20 17:17:27 -05:00
Alinson S Xavier	ddebcc6ddb	Merge branch 'dev' into feature/reserves	2022-06-20 14:31:02 -05:00
Alinson S Xavier	3282e5bc3a	Fix all tests	2022-06-20 14:21:02 -05:00
Alinson S. Xavier	15de1901c8	Remove temporary files	2022-06-14 14:55:59 -05:00
Alinson S Xavier	bf2dc4ddc4	Remove instances from repository; download on the fly	2022-06-14 14:38:44 -05:00
Alinson S Xavier	5ca566f147	Remove old reserves	2022-01-20 16:23:22 -06:00
Alinson S Xavier	3220650e39	Implement new reserves	2022-01-20 10:18:19 -06:00
Alinson S Xavier	ca0d250dfa	Parse new reserves	2022-01-19 10:03:22 -06:00
Alinson S Xavier	2bd68b49a5	Reserves: Update docs	2022-01-19 09:23:21 -06:00
`@@ -1,4 +1,4 @@`
	`Copyright © 2020, UChicago Argonne, LLC`	`Copyright © 2020-2022, UChicago Argonne, LLC`

	`All Rights Reserved`	`All Rights Reserved`