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

Author

Listed:
  • Cho, Jehum

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Papavasiliou, Anthony

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

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.

Suggested Citation

  • Cho, Jehum & Papavasiliou, Anthony, 2023. "Exact Mixed-Integer Programming Approach for Chance-Constrained Multi-Area Reserve Sizing," LIDAM Reprints CORE 3244, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:3244
    DOI: https://doi.org/10.1109/TPWRS.2023.3279692
    Note: In: IEEE Transactions on Power Systems, 2023
    as

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