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Exact Mixed-Integer Programming Approach for Chance-Constrained Multi-Area Reserve Sizing
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DOI：10.1109/TPWRS.2023.3279692.png)
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Abstract
En 中文
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:
Chance constraints
mixed-integer programming
multi-area reserve sizing
probabilistic constraints
Journal
IF:
7.2
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1.1W
Citations:
5.0W