Chance Constraints for Improving the Security of AC Optimal Power Flow

TL;DR

This paper introduces a convex, chance-constrained approach to enhance the robustness and cost-effectiveness of AC Optimal Power Flow solutions under uncertain renewable generation, with efficient computation for large systems.

Contribution

It develops a scalable, convex reformulation of chance-constrained AC OPF that improves solution robustness and cost, using theoretical insights and modeling assumptions.

Findings

Improves feasibility of OPF solutions under uncertainty

Maintains computational efficiency comparable to deterministic OPF

Enhances cost performance with minimal additional computation

Abstract

This paper presents a scalable method for improving the solutions of AC Optimal Power Flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The focus of the paper is on providing solutions that are more robust to short-term deviations, and which optimize both the initial operating point and a parametrized response policy for control during fluctuations. We formulate this as a chance-constrained optimization problem. To obtain a tractable representation of the chance constraints, we introduce a number of modelling assumptions and leverage recent theoretical results to reformulate the problem as a convex, second-order cone program, which is efficiently solvable even for large instances. Our experiments demonstrate that the proposed procedure improves the feasibility and cost performance of the OPF solution, while the additional computation time is on the same magnitude as a single deterministic AC OPF calculation.