Optimizing battery energy storage systems simplified dispatch: comparative analysis of temporal resolution, complexity and multi-market participation strategies
Manuel Barão(1), Tomás Freitas Rocha(2), Paulo Pinto(2), Adriano Carvalho(3) 1. Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal 2. MEGAJOULE S.A, Moreira, Portugal 3. Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Porto, Portugal 2026-06-27

Abstract

Battery Energy Storage Systems (BESS) economicviability depends critically on accurate price forecasting for dispatch decisions. While existing literature extensively analyses BESS operation under perfect foresight, the impact of realistic forecast uncertainty remains insufficiently quantified. This paper develops a comprehensive framework to assess forecast-driven BESS operation using a rule-based dispatch model applied to three years of Iberian electricity market data (2020, 2022, 2024). By comparing forecast-driven operation against perfect foresight benchmarks, annual revenue losses of 17 to 25% attributable to forecast uncertainty are quantified. A critical asymmetry emerges charging decisions prove economically robust to temporal forecast errors, while discharge timing errors account for up to 68% of total revenue degradation in solar-dominated markets. Analysis reveals that market structure evolution, specifically persistent midday price floors, fundamentally alters forecast sensitivity patterns. Artificial noise injection demonstrates that 2024 market conditions exhibit resilience at 70% forecast accuracy, while 2020 conditions require 95% accuracy for comparable performance. These findings indicate that pre-feasibility assessments assuming perfect foresight systematically overestimate BESS revenues by 15 to 25%, with significant implications for investment decision-making.

Key words: Battery Energy Storage Systems, electricity price forecasting, revenue loss, energy arbitrage, techno-economic assessment.

References

[1] DGEG – Direção-Geral de Energia e Geologia. Energia em Números – Edição 2025. ISBN: 978-972-8646-87-5. Lisboa: DGEG, 2024.

[2] ENTSO-E. Transparency Platform – Electricity Market Data. URL: https://transparency.entsoe.eu/. Accessed September 2025. 2024.

[3] Y. Yang et al. “Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review”. In: Renewable and Sustainable Energy Reviews 167 (Oct. 2022). ISSN: 18790690. DOI: 10.1016/j.rser.2022.112671.

[4] S. Englberger et al. “SimSES Multi-Use: A simulation tool for multiple storage system applications”. In: 2019 16th International Conference on the European Energy Market (EEM). 2019, pp. 1-5. DOI: 10.1109/EEM.2019.8916568.

[5] R. Zhang et al. “Optimization of battery energy storage system (BESS) sizing in different electricity market types considering BESS utilization mechanisms and ownerships”. In: Journal of Cleaner Production 470 (Sept. 2024). ISSN: 09596526. DOI: 10.1016/j.jclepro.2024.143317.

[6] W. Cole, V. Wesley and M. T. Vigneswaran. Cost Projections for Utility-Scale Battery Storage: 2025 Update. Tech. rep. NREL/TP-6A40-93281. National Renewable Energy Laboratory, Golden, CO, 2025. URL: https://www.nrel.gov/docs/fy25osti/93281.pdf.

[7] A. J. Hutchinson et al. “A comprehensive review of modeling approaches for grid-connected energy storage technologies”. In: Journal of Energy Storage 109 (Feb. 2025). ISSN: 2352152X. DOI: 10.1016/j.est.2024.115057.

[8] REN - Rede Eletrica Nacional. aFRR - Automatic Frequency Restoration Reserve. Accessed: 2026-01-06. 2024. URL: https://mercado.ren.pt/PT/Electr/InfoMercado/InfSistema/aFRR.

[9] European Commission. Regulation (EU) 2017/2195 establishing a guideline on electricity balancing. URL: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32017R2195. Official Journal of the European Union. 2017.

[10] Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual. 2024. URL: https://www.gurobi.com.