Quantum Computing Approach for Energy Optimization in a Prosumer Community

TL;DR

This paper explores a quantum computing method to optimize energy costs in prosumer communities, transforming the problem for quantum algorithms and demonstrating potential speedups through simulations.

Contribution

It introduces a quantum approach for energy optimization in prosumer communities, including problem reformulation and experimental validation using QAOA.

Findings

Quantum approach can effectively model the prosumer problem.

Simulation results show potential speedup with increasing problem size.

Solution quality varies with number of constraints and qubits.

Abstract

This paper presents a quantum approach for the formulation and solution of the prosumer problem, i.e., the problem of minimizing the energy cost incurred by a number of users in an energy community, while addressing the constraints given by the balance of energy and the user requirements. As the problem is NP-complete, a hybrid quantum/classical algorithm could help to acquire a significant speedup, which is particularly useful when the problem size is large. This work describes the steps through which the problem can be transformed, reformulated and given as an input to Quantum Approximate Optimization Algorithm (QAOA), and reports some experimental results, in terms of the quality of the solution and time to achieve it, obtained with a quantum simulator, when varying the number of constraints and, correspondingly, the number of qubits.