期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 14, 期 1, 页码 140-151出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2022.3203669
关键词
Chance constraint; distributionally robust; optimal transmission switching; renewable generation
This work aims to develop a robust optimal transmission switching (OTS) framework to alleviate grid congestion and reduce renewable curtailment. A two-stage distributionally robust chance-constrained (DRCC) problem is formulated to ensure limited constraint violations under any uncertainty distribution within an ambiguity set. Moment-based and distance-based ambiguity sets are utilized to obtain scalable mixed-integer linear program (MILP) formulations. Numerical experiments on test systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of constraint violations and renewable curtailment reduction. The moment-based MILP approach is computationally efficient and suitable for real-time grid operations.
Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based ($\infty$-Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations.
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