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

Jehum Cho () and Anthony Papavasiliou ()
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Jehum Cho: Université catholique de Louvain, LIDAM/CORE, Belgium
Anthony Papavasiliou: Université catholique de Louvain, LIDAM/CORE, Belgium

No 3244, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: 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.

Keywords: Multi-area reserve sizing; chance constraints; probabilistic constraints; mixed-integer programming (search for similar items in EconPapers)
Pages: 14
Date: 2023-07-01
Note: In: IEEE Transactions on Power Systems, 2023
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3244

DOI: 10.1109/TPWRS.2023.3279692

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