Quantum Approximate Optimization for Real-Time Transactive Energy Market Clearing at Scale

Abstract

Clearing local transactive energy markets while enforcing non-convex AC optimal power flow constraints becomes computationally intractable as the number of distributed energy resources scales into the millions, and classical interior-point and branch-and-bound solvers cannot meet real-time deadlines at this scale. We propose a hybrid quantum-classical framework that maps the joint market-clearing and power-flow problem onto a quadratic unconstrained binary optimization form with dynamic penalty weighting and solves it with a customized Quantum Approximate Optimization Algorithm. On synthetically expanded IEEE 8500-node networks with up to 100,000 distributed energy resources, the framework exhibits empirical (N N) scaling, clears the largest network in 72.8 seconds, and maintains an optimality gap below 3.5%, compared with hours-long or non-convergent classical baselines.