Power Flow Security Maximization via Inverse Chance Constrained Optimization

Shenglu Wang, Kairui Feng, Mengqi Xue, Yue Song

Abstract: The chance-constrained optimal power flow (CC-OPF) essentially finds the low-cost generation dispatch scheme ensuring operational constraints are met with a specified probability, termed the security level. While the security level is a crucial input parameter, how it shapes the CC-OPF feasibility boundary has not been revealed. Changing the security level from a parameter to a decision variable, this letter proposes a security maximization approach based on the chance constrained DC-OPF model, termed inverse CC-OPF (ICC-OPF), that seeks the maximum security level achievable by the system. To efficiently solve the ICC-OPF, we design a Newton-Raphson-like iteration algorithm leveraging the duality-based sensitivity analysis of an associated surrogate problem. Numerical experiments validate the proposed approach, revealing complex feasibility boundaries for security levels that underscore the importance of coordinating security levels across multiple chance constraints.

External IDs:dblp:journals/csysl/WangFXS25