Multi-Objective Quantum Power System Redispatch

TL;DR

This paper reformulates the power system redispatch problem as a multi-objective QUBO, enabling quantum computing approaches to optimize energy costs efficiently, including large-scale instances with temporal constraints.

Contribution

It introduces a novel penalty method and an alpha-expansion algorithm for large-scale, constrained redispatch optimization within the QUBO framework, suitable for quantum annealing.

Findings

Validated on German power system data

Demonstrated scalability to large problem instances

Successfully used D-Wave quantum annealer for solutions

Abstract

The rising energy production costs and the increasing reliance on volatile renewable sources have driven the need for more efficient power system redispatch strategies. In this work, we re-interpret the redispatch problem as a multi-objective combinatorial optimization task within the Quadratic Unconstrained Binary Optimization (QUBO) framework, suitable for adiabatic quantum computing. Our contributions include a novel normalized unbalanced penalty method that integrates inequality constraints via a quadratic Taylor expansion and an alpha-expansion algorithm that allows us to address large-scale redispatch instances and to integrate temporal adjacent state switching constraints directly into the algorithm. Our experiments are conducted on open data of the German power system. Our results, obtained via numerical simulation and from an actual D-Wave Advantage quantum annealer, validate the viability of our formulation and demonstrate that our algorithm scales to large problem instances.