Alternating Methods for Large-Scale AC Optimal Power Flow with Unit Commitment

TL;DR

This paper introduces a decomposition and penalty-based method for solving large-scale, detailed security-constrained AC unit commitment problems efficiently, achieving near-optimal solutions within strict time limits.

Contribution

It proposes a novel decomposition scheme and heuristics for large-scale AC unit commitment with detailed grid features, improving solution quality and computational efficiency.

Findings

Achieved an average optimality gap of 1.33% on large test cases.

Demonstrated near-optimal solutions within strict time constraints.

Validated approach on real-like power grid data from DOE competition.

Abstract

Security-constrained unit commitment with alternating current optimal power flow (SCUC-ACOPF) is a central problem in power grid operations that optimizes commitment and dispatch of generators under a physically accurate power transmission model while encouraging robustness against component failures. SCUC-ACOPF requires solving large-scale problems that involve multiple time periods and networks with thousands of buses within strict time limits. In this work, we study a detailed SCUC-ACOPF model with a rich set of features of modern power grids, including price-sensitive load, reserve products, transformer controls, and energy-limited devices. We propose a decomposition scheme and a penalty alternating direction method to find high-quality solutions to this model. Our methodology leverages spatial and temporal decomposition, separating the problem into a set of mixed-integer linear programs for each bus and a set of continuous nonlinear programs for each time period. To improve the performance of the algorithm, we introduce a variety of heuristics, including restrictions of temporal linking constraints, a second-order cone relaxation, and a contingency screening algorithm. We quantify the quality of feasible solutions through a dual bound from a convex second-order cone program. To evaluate our algorithm, we use large-scale test cases from Challenge 3 of the U.S. Department of Energy's Grid Optimization Competition that resemble real power grid data under a variety of operating conditions and decision horizons. The experiments yield feasible solutions with an average optimality gap of 1.33%, demonstrating that this approach generates near-optimal solutions within stringent time limits.