web: Move to separate repository

Co-authored-by: Alinson S. Xavier <git@axavier.org>
Co-authored-by: Shaoming Xu <xsm90827@gmail.com>

.gitignore

@@ -1,3 +1,4 @@
+*-off.md
 *.bak
 *.gz
 *.ipynb
@@ -19,6 +20,7 @@
 .apdisk
 .com.apple.timemachine.donotpresent
 .fseventsd
+.idea
 .ipy*
 .vscode
 Icon
@@ -32,12 +34,11 @@ benchmark/tables
 benchmark/tmp.json
 build
 docs/_build
+docs/src/tutorials/customizing.md
+docs/src/tutorials/lmp.md
+docs/src/tutorials/market.md
+docs/src/tutorials/usage.md
 instances/**/*.json
 instances/_source
 local
 notebooks
-docs/src/tutorials/usage.md
-docs/src/tutorials/customizing.md
-docs/src/tutorials/market.md
-docs/src/tutorials/lmp.md
-*-off.md

CHANGELOG.md

@@ -11,9 +11,12 @@ 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
 
-## [0.4.2] - 2025-11-27
+## [0.4.1] - 2025-11-05
 ### Fixed
-- KnuOstWat2018: Fixed a bug in `eq_segprod_limit` constraint (#17)
+- Fix multi-threading issues in Julia 1.12
+
+### Changed
+- The package now requires Julia 1.10 or newer
 
 ## [0.4.0] - 2024-05-21
 ### Added

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 = "0.4.2"
+version = "0.4.1"
 
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"

README.md

@@ -108,7 +108,7 @@ See official documentation at: https://anl-ceeesa.github.io/UnitCommitment.jl/
 
 If you use UnitCommitment.jl in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
 
-* **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Feng Qiu**. "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment (Version 0.4)". Zenodo (2024). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
+* **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Jun He, Feng Qiu**. "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment (Version 0.4)". Zenodo (2024). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
 
 If you use the instances, we additionally request that you cite the original sources, as described in the documentation.
 

docs/src/guides/format.md

@@ -107,7 +107,7 @@ Note that this curve also specifies the production limits. Specifically, the fir
 
 ```@raw html
 <center>
-    <img src="../assets/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>

docs/src/tutorials/decomposition.md

@@ -1,8 +1,8 @@
 # Decomposition methods
 
-## 1. Time decomposition
+## 1. Time decomposition for production cost modeling
 
-Solving unit commitment instances that have long time horizons (for example, year-long 8760-hour instances) requires a substantial amount of computational power. To address this issue, UC.jl offers a time decomposition method, which breaks the instance down into multiple overlapping subproblems, solves them sequentially, then reassembles the solution.
+Solving unit commitment instances that have long time horizons (for example, year-long 8760-hour instances in production cost modeling) requires a substantial amount of computational power. To address this issue, UC.jl offers a time decomposition method, which breaks the instance down into multiple overlapping subproblems, solves them sequentially, then reassembles the solution.
 
 When solving a unit commitment instance with a dense time slot structure, computational complexity can become a significant challenge. For instance, if the instance contains hourly data for an entire year (8760 hours), solving such a model can require a substantial amount of computational power. To address this issue, UC.jl provides a time_decomposition method within the `optimize!` function. This method decomposes the problem into multiple sub-problems, solving them sequentially.
 
@@ -57,7 +57,7 @@ solution = UnitCommitment.optimize!(
 )
 ```
 
-## 2. Scenario decomposition with Progressive Hedging
+## 2. Scenario decomposition with Progressive Hedging for stochstic UC
 
 By default, UC.jl uses the Extensive Form (EF) when solving stochastic instances. This approach involves constructing a single JuMP model that contains data and decision variables for all scenarios. Although EF has optimality guarantees and performs well with small test cases, it can become computationally intractable for large instances or substantial number of scenarios.
 

src/model/formulations/KnuOstWat2018/pwlcosts.jl

@@ -67,19 +67,21 @@ function _add_production_piecewise_linear_eqs!(
                     (t < T ? Cw * switch_off[gn, t+1] : 0.0)
                 )
             else
-                # Equation (47a) in Kneuven et al. (2020)
+                # Equation (47a)/(48a) in Kneuven et al. (2020)
                 eq_segprod_limit_b[sc.name, gn, t, k] = @constraint(
                     model,
                     segprod[sc.name, gn, t, k] <=
                     g.cost_segments[k].mw[t] * is_on[gn, t] -
-                    Cv * switch_on[gn, t]
+                    Cv * switch_on[gn, t] -
+                    (t < T ? max(0, Cv - Cw) * switch_off[gn, t+1] : 0.0)
                 )
 
-                # Equation (47b) in Kneuven et al. (2020)
+                # Equation (47b)/(48b) in Kneuven et al. (2020)
                 eq_segprod_limit_c[sc.name, gn, t, k] = @constraint(
                     model,
                     segprod[sc.name, gn, t, k] <=
                     g.cost_segments[k].mw[t] * is_on[gn, t] -
+                    max(0, Cw - Cv) * switch_on[gn, t] -
                     (t < T ? Cw * switch_off[gn, t+1] : 0.0)
                 )
             end

test/src/UnitCommitmentT.jl

@@ -23,7 +23,6 @@ include("validation/repair_test.jl")
 include("lmp/conventional_test.jl")
 include("lmp/aelmp_test.jl")
 include("market/market_test.jl")
-include("regression.jl")
 
 basedir = dirname(@__FILE__)
 
@@ -55,7 +54,6 @@ function runtests()
         lmp_aelmp_test()
         simple_market_test()
         stochastic_market_test()
-        regression_test()
     end
     return
 end

test/src/regression.jl

@@ -1,19 +0,0 @@
-# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using UnitCommitment, HiGHS, JuMP
-
-function regression_test()
-    @testset "GitHub Issue #57" begin
-        instance = UnitCommitment.read(fixture("issue-0057.json.gz"))
-        model = UnitCommitment.build_model(
-            instance = instance,
-            optimizer = HiGHS.Optimizer,
-        )
-        JuMP.set_silent(model)
-        UnitCommitment.optimize!(model)
-        solution = UnitCommitment.solution(model)
-        @test solution["Thermal production (MW)"]["gen_524d4c85"][1] == 90.0
-    end
-end