Robust Optimization for Electricity Generation

TL;DR

This paper develops a robust convex optimization framework for electric power systems that accounts for uncertainties in demand and renewable energy, providing tight bounds and a convergent solution method.

Contribution

It introduces a novel robust convex relaxation approach for ACOPF under uncertainty, with a cutting-plane algorithm ensuring convergence and practical feasibility.

Findings

Robust convex relaxation yields tight lower bounds for ACOPF.

The cutting-plane method converges reliably to optimal solutions.

Experimental results validate the effectiveness of the proposed approach.

Abstract

We consider a robust optimization problem in an electric power system under uncertain demand and availability of renewable energy resources. Solving the deterministic alternating current optimal power flow (ACOPF) problem has been considered challenging since the 1960s due to its nonconvexity. Linear approximation of the AC power flow system sees pervasive use, but does not guarantee a physically feasible system configuration. In recent years, various convex relaxation schemes for the ACOPF problem have been investigated, and under some assumptions, a physically feasible solution can be recovered. Based on these convex relaxations, we construct a robust convex optimization problem with recourse to solve for optimal controllable injections (fossil fuel, nuclear, etc.) in electric power systems under uncertainty (renewable energy generation, demand fluctuation, etc.). We propose a cutting-plane method to solve this robust optimization problem, and we establish convergence and other desirable properties. Experimental results indicate that our robust convex relaxation of the ACOPF problem can provide a tight lower bound.