Distributionally Robust Joint Chance-Constrained Optimal Power Flow using Relative Entropy

TL;DR

This paper introduces a novel data-driven, distributionally robust approach to solve chance-constrained optimal power flow problems, ensuring robustness and efficiency in power system operation under uncertainty.

Contribution

It presents an exact reformulation of distributionally robust chance constraints for CCOPF, improving over conservative approximations and providing robustness guarantees.

Findings

The proposed method outperforms existing approaches in simulations.

It offers exact reformulation of chance constraints, reducing conservativeness.

The approach guarantees out-of-sample robustness and efficiency.

Abstract

Designing robust algorithms for the optimal power flow (OPF) problem is critical for the control of large-scale power systems under uncertainty. The chance-constrained OPF (CCOPF) problem provides a natural formulation of the trade-off between the operating cost and the constraint satisfaction rate. In this work, we propose a new data-driven algorithm for the CCOPF problem, based on distributionally robust optimization (DRO). \revise{We show that the proposed reformulation of the distributionally robust chance constraints is exact, whereas other approaches in the CCOPF literature rely on conservative approximations. We establish out-of-sample robustness guarantees for the distributionally robust solution and prove that the solution is the most efficient among all approaches enjoying the same guarantees.} We apply the proposed algorithm to the the CCOPF problem and compare the performance of our approach with existing methods using simulations on IEEE benchmark power systems.