Chance Constraints for Improving the Security of AC Optimal Power Flow
For power system operators, this provides a practical way to enhance grid security against uncertainty from renewables with minimal computational overhead.
The paper proposes a scalable method for AC Optimal Power Flow that improves robustness to short-term deviations in renewable generation, achieving better feasibility and cost performance with computation time comparable to a single deterministic AC OPF.
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.