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Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study

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  • Pattanun Chanpiwat

    (Department of Graduate Studies, Command and General Staff College, Royal Thai Army, 820/1 Rama V Rd., Nakhon-Chai-Si Road, Dusit, Bangkok 10300, Thailand
    Department of Civil Engineering, Chulachomklao Royal Military Academy, Nakhon Nayok 26001, Thailand)

  • Fabricio Oliveira

    (Department Mathematics and Systems Analysis, School of Science, Aalto University, FI-00076 Espoo, Finland)

  • Steven A. Gabriel

    (Department Mathematics and Systems Analysis, School of Science, Aalto University, FI-00076 Espoo, Finland
    Applied Mathematics & Statistics, and Scientific Computation Program, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
    Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway)

Abstract

This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing.

Suggested Citation

  • Pattanun Chanpiwat & Fabricio Oliveira & Steven A. Gabriel, 2025. "Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study," Energies, MDPI, vol. 18(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3560-:d:1695845
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    References listed on IDEAS

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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    2. Puranen, Pietari & Kosonen, Antti & Ahola, Jero, 2021. "Techno-economic viability of energy storage concepts combined with a residential solar photovoltaic system: A case study from Finland," Applied Energy, Elsevier, vol. 298(C).
    3. Denholm, Paul & Nunemaker, Jacob & Gagnon, Pieter & Cole, Wesley, 2020. "The potential for battery energy storage to provide peaking capacity in the United States," Renewable Energy, Elsevier, vol. 151(C), pages 1269-1277.
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