A Surrogate-Based Asynchronous Decomposition Technique for Realistic Security-Constrained Optimal Power Flow Problems

Solving realistic security-constrained optimal power flow problems In “A surrogate-based asynchronous decomposition technique for realistic security-constrained optimal power flow problems,” we propose a new algorithm for solving a classical problem in power grid operations: the security-constrained optimal power flow, considering its nonlinearities and realistic transitions between nominal and emergency post-contingency operations. Solving security-constrained optimal power flow problems accurately is a critical function, upon which depends the reliability, security, and efficiency of power systems as well as the correct functioning of other critical infrastructure dependent on electricity. The proposed algorithm was extensively tested against many state-of-the-art approaches using realistic and real instances in the ARPA-E Grid Optimization Competition Challenge 1, where it found the best-known solution for 58% of the instances, attained an average gap of less than 0.2%, and obtained the best overall scores, thereby winning all divisions of Challenge 1 with a very strong first place. We present a decomposition approach for obtaining good feasible solutions for the security-constrained, alternating-current, optimal power flow (SC-AC-OPF) problem at an industrial scale and under real-world time and computational limits. The approach was designed while preparing and participating in ARPA-E’s Grid Optimization Competition (GOC) Challenge 1. The challenge focused on a near-real-time version of the SC-AC-OPF problem, where a base operating point is optimized, taking into account possible single-element contingencies, after which the system adapts its operating point following the response of automatic frequency droop controllers and voltage regulators. Our solution approach for this problem relies on state-of-the-art nonlinear programming algorithms, and it employs nonconvex relaxations for complementarity constraints, a specialized two-stage decomposition technique with sparse approximations of recourse terms and contingency ranking and prescreening. The paper describes and justifies our approach and outlines the features of its implementation, including functions and derivatives evaluation, warm-starting strategies, and asynchronous parallelism. We discuss the results of the independent benchmark of our approach by ARPA-E’s GOC team in Challenge 1, where it was found to consistently produce high-quality solutions across a wide range of network sizes and difficulty, and conclude by outlining future extensions of the approach. History: This paper has been accepted for the Operations Research Special Issue on Computational Advances in Short-Term Power System Operations. Funding: This work was performed under the auspices of the U.S. Department of Energy by the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.