Grid Cost Allocation in Peer-to-Peer Electricity Markets: Benchmarking Classical and Quantum Optimization Approaches

TL;DR

This paper introduces a QUBO-based optimization model for grid cost allocation in P2P electricity markets, comparing quantum and classical methods on IEEE test cases, and finds classical algorithms outperform quantum approaches.

Contribution

It presents a novel QUBO model for grid cost allocation in P2P markets and evaluates quantum versus classical optimization methods, highlighting classical methods' superior performance.

Findings

Classical branch-and-cut outperforms quantum annealing and heuristics.

Quantum annealing performs poorly despite hyperparameter tuning.

The model effectively allocates grid costs but quantum approaches are less suitable.

Abstract

This paper presents a novel optimization approach for allocating grid operation costs in Peer-to-Peer (P2P) electricity markets using Quantum Computing (QC). We develop a Quadratic Unconstrained Binary Optimization (QUBO) model that matches logical power flows between producer-consumer pairs with the physical power flow to distribute grid usage costs fairly. The model is evaluated on IEEE test cases with up to 57 nodes, comparing Quantum Annealing (QA), hybrid quantum-classical algorithms, and classical optimization approaches. Our results show that while the model effectively allocates grid operation costs, QA performs poorly in comparison despite extensive hyperparameter optimization. The classical branch-and-cut method outperforms all solvers, including classical heuristics, and shows the most advantageous scaling behavior. The findings may suggest that binary least-squares optimization problems may not be suitable candidates for near-term quantum utility.