Docs: Add DOIs

README.md

@@ -20,9 +20,18 @@
 
 * **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*, *MorLatRam2013*, *DamKucRajAta2016*, *PanGua2016*), multiple piecewise-linear costs formulations (*Gar1962*, *CarArr2006*, *KnuOstWat2018*) and contingency screening methods (*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][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
 * **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
 
+[ArrCon2000]: https://doi.org/10.1109/59.871739
+[CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
+[DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
+[Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
+[KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
+[MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
+[PanGua2016]: https://doi.org/10.1287/opre.2016.1520
+[XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
+
 ## Sample Usage
 
 ```julia
@@ -37,25 +46,27 @@ import UnitCommitment:
     ShiftFactorsFormulation
 
 # Read benchmark instance
-instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
+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,
+    instance = instance,
+    optimizer = Cbc.Optimizer,
 )
 
 # Construct model (using customized formulation)
 model = UnitCommitment.build_model(
-    instance=instance,
-    optimizer=Cbc.Optimizer,
-    formulation=Formulation(
+    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,
+            isf_cutoff = 0.005,
+            lodf_cutoff = 0.001,
         ),
     ),
 )
@@ -63,7 +74,7 @@ model = UnitCommitment.build_model(
 # Modify the model (e.g. add custom constraints)
 @constraint(
     model,
-    model[:is_on]["g3",1] + model[:is_on]["g4",1] <= 1,
+    model[:is_on]["g3", 1] + model[:is_on]["g4", 1] <= 1,
 )
 
 # Solve model

docs/index.md

@@ -2,13 +2,22 @@
 
 **UnitCommitment.jl** (UC.jl) is a Julia/JuMP optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and Julia/JuMP implementations of state-of-the-art mixed-integer programming formulations.
 
-### Package Components
+## Package Components
 
 * **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
-* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature and extended to make them more challenging and realistic.
-* **Model Implementation**: The package provides a Julia/JuMP implementation of state-of-the-art formulations and solution methods for SCUC. Our goal is to keep this implementation up-to-date, as new methods are proposed in the literature.
+* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature, converted into a common data format, and extended using data-driven methods to make them more challenging and realistic.
+* **Model Implementation**: The package provides a Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
 * **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
 
+[ArrCon2000]: https://doi.org/10.1109/59.871739
+[CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
+[DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
+[Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
+[KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
+[MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
+[PanGua2016]: https://doi.org/10.1287/opre.2016.1520
+[XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
+
 ### Authors
 * **Alinson Santos Xavier** (Argonne National Laboratory)
 * **Aleksandr M. Kazachkov** (University of Florida)

src/model/formulations/ArrCon2000/structs.jl

@@ -7,7 +7,7 @@ Formulation described in:
 
     Arroyo, J. M., & Conejo, A. J. (2000). Optimal response of a thermal unit
     to an electricity spot market. IEEE Transactions on power systems, 15(3), 
-    1098-1104.
+    1098-1104. DOI: https://doi.org/10.1109/59.871739
 """
 module ArrCon2000
 

src/model/formulations/CarArr2006/structs.jl

@@ -8,6 +8,7 @@ Formulation described in:
     Carrión, M., & Arroyo, J. M. (2006). A computationally efficient
     mixed-integer linear formulation for the thermal unit commitment problem.
     IEEE Transactions on power systems, 21(3), 1371-1378.
+    DOI: https://doi.org/10.1109/TPWRS.2006.876672
 """
 module CarArr2006
 

src/model/formulations/DamKucRajAta2016/structs.jl

@@ -7,6 +7,7 @@ Formulation described in:
 
     Damcı-Kurt, P., Küçükyavuz, S., Rajan, D., & Atamtürk, A. (2016). A polyhedral
     study of production ramping. Mathematical Programming, 158(1), 175-205.
+    DOI: https://doi.org/10.1007/s10107-015-0919-9
 """
 module DamKucRajAta2016
 

src/model/formulations/Gar1962/structs.jl

@@ -8,6 +8,7 @@ Formulation described in:
     Garver, L. L. (1962). Power generation scheduling by integer
     programming-development of theory. Transactions of the American Institute
     of Electrical Engineers. Part III: Power Apparatus and Systems, 81(3), 730-734.
+    DOI: https://doi.org/10.1109/AIEEPAS.1962.4501405
 
 """
 module Gar1962

src/model/formulations/KnuOstWat2018/structs.jl

@@ -7,7 +7,7 @@ Formulation described in:
 
     Knueven, B., Ostrowski, J., & Watson, J. P. (2018). Exploiting identical
     generators in unit commitment. IEEE Transactions on Power Systems, 33(4),
-    4496-4507.
+    4496-4507. DOI: https://doi.org/10.1109/TPWRS.2017.2783850
 """
 module KnuOstWat2018
 

src/model/formulations/MorLatRam2013/structs.jl

@@ -7,7 +7,7 @@ Formulation described in:
 
     Morales-España, G., Latorre, J. M., & Ramos, A. (2013). Tight and compact
     MILP formulation for the thermal unit commitment problem. IEEE Transactions
-    on Power Systems, 28(4), 4897-4908.
+    on Power Systems, 28(4), 4897-4908. DOI: https://doi.org/10.1109/TPWRS.2013.2251373
 """
 module MorLatRam2013
 

src/model/formulations/PanGua2016/structs.jl

@@ -7,6 +7,7 @@ Formulation described in:
 
     Pan, K., & Guan, Y. (2016). Strong formulations for multistage stochastic
     self-scheduling unit commitment. Operations Research, 64(6), 1482-1498.
+    DOI: https://doi.org/10.1287/opre.2016.1520
 """
 module PanGua2016
 

src/solution/methods/XavQiuWanThi2019/structs.jl

@@ -8,6 +8,7 @@ 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