2023 Quantum Computing Applications in Chemical and Biochemical Engineering Workshop

Quantum Computing Applications in Day-Ahead Market Optimisation

Authors

Brown, S. F. - Presenter, University of Sheffield
Wyatt-Jones, J. A., The University of Sheffield
Ejeh, J. O., The University of Sheffield
Quantum computing (QC) has attracted a great deal of research over recent years. This has been due to advances in hardware, software, solution algorithms, as well as its potential to show exponential speed-ups compared to equivalent classical computing. Although QC is still at its early stage, three key areas in chemical engineering have been identified to provide an initial advantage – computational chemistry, machine learning and optimisation (Ajageka and You, 2019).

An increasing number of chemical engineering problems can be posed as optimisation problems. One such problem relates to the design and scheduling of electrical energy storage (ESS) devices within power systems. ESS technologies have been shown to improve power system reliability, flexibility and security (Arghandeh et al., 2014; Ma et al., 2018), especially with the increasing reliance on intermittent renewable energy sources owing to their greenhouse gas emission reduction potential. This applies both in behind-the-meter and front-of-meter applications. The design and scheduling problem for the ESS device is important owing to high costs combined with the range of revenue streams – energy arbitrage, ancillary services, etc – such devices can have access to.

A range of existing optimisation models have been developed, suited to classical computers, which determine the optimal battery actions given a set of price signals for each revenue stream. However, current optimisation models are unsuitable for use on quantum processors. Hence, in this work we propose a reformulation of a mixed integer linear programming (MILP) model (Ejeh et al., 2022) for day-ahead market optimisation problem in a UK market, to a quantum unconstrained binary optimisation (QUBO) model. Using the Qiskit toolkit (Qiskit contributors, 2023), the reformulated QUBO is solved using the quantum approximate optimisation algorithm (QAOA) with the minimum eigen optimiser. The results obtained from the MILP and QUBO models are analysed highlighting the performance, challenges and potential improvements for QC over current classical computing-based optimization approaches.