Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration

Yuqi Zhou, Hao Zhu, Grani A. Hanasusanto

Research output: Contribution to journalArticlepeer-review


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 (∞-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.

Original languageEnglish (US)
Pages (from-to)140-151
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Issue number1
StatePublished - Jan 1 2023
Externally publishedYes


  • Chance constraint
  • distributionally robust
  • optimal transmission switching
  • renewable generation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment


Dive into the research topics of 'Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration'. Together they form a unique fingerprint.

Cite this