Version 1
: Received: 22 March 2024 / Approved: 25 March 2024 / Online: 25 March 2024 (08:31:26 CET)
How to cite:
Gonzalez-Saenz, J.; Becerra, V. Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market. Preprints2024, 2024031447. https://doi.org/10.20944/preprints202403.1447.v1
Gonzalez-Saenz, J.; Becerra, V. Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market. Preprints 2024, 2024031447. https://doi.org/10.20944/preprints202403.1447.v1
Gonzalez-Saenz, J.; Becerra, V. Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market. Preprints2024, 2024031447. https://doi.org/10.20944/preprints202403.1447.v1
APA Style
Gonzalez-Saenz, J., & Becerra, V. (2024). Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market. Preprints. https://doi.org/10.20944/preprints202403.1447.v1
Chicago/Turabian Style
Gonzalez-Saenz, J. and Victor Becerra. 2024 "Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market" Preprints. https://doi.org/10.20944/preprints202403.1447.v1
Abstract
This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
