Exact Mixed-Integer Programming Approach for Chance-Constrained Multi-Area Reserve Sizing

An exact algorithm is developed for the chance-constrained multi-area reserve sizing problem in the presence of transmission network constraints. The problem can be cast as a two-stage stochastic mixed integer linear program using sample approximation. Due to the complicated structure of the problem...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on power systems Ročník 39; číslo 2; s. 3310 - 3323
Hlavní autori: Cho, Jehum, Papavasiliou, Anthony
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Chance constraints
Constraints
Integer algorithms
Integer programming
Mathematical models
Mixed integer
mixed-integer programming
multi-area reserve sizing
Optimization
probabilistic constraints
Probabilistic logic
Real-time systems
Sizing
Systems operation
Time-frequency analysis
ISSN:0885-8950, 1558-0679
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:An exact algorithm is developed for the chance-constrained multi-area reserve sizing problem in the presence of transmission network constraints. The problem can be cast as a two-stage stochastic mixed integer linear program using sample approximation. Due to the complicated structure of the problem, existing methods attempt to find a feasible solution based on heuristics. Existing mixed-integer algorithms that can be applied directly to a two-stage stochastic program can only address small-scale problems that are not practical. We have found a minimal description of the projection of our problem onto the space of the first-stage variables. This enables us to directly apply more general Integer Programming techniques for mixing sets, that arise in chance-constrained problems. Combining the advantages of the minimal projection and the strengthening reformulation from IP techniques, our method can tackle real-world problems. We specifically consider a case study of the 10-zone Nordic network with 100,000 scenarios where the optimal solution can be found in approximately 5 minutes.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2023.3279692