Inverse Chance Constrained Optimal Power Flow

TL;DR

This paper introduces an inverse chance constrained optimal power flow method that determines the maximum feasible security level for power systems, using a novel iterative algorithm and revealing complex feasibility boundaries.

Contribution

It transforms the security level into a decision variable and develops a Newton-Raphson-like algorithm to efficiently find the highest feasible security level in CC-OPF.

Findings

Revealed complex feasibility boundaries for security levels.

Demonstrated the effectiveness of the inverse CC-OPF approach.

Highlighted the importance of coordinating security levels across multiple constraints.

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 the inverse CC-OPF that seeks the highest feasible security level supported by the system. To efficiently solve this problem, 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.