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Optimal Prosumer Storage Management in Renewable Energy Communities Under Demand Response

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  • Gianni Bianchini

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Marco Casini

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

  • Milad Gholami

    (Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy)

Abstract

This paper deals with the optimal scheduling of prosumers equipped with energy storage facilities within renewable energy communities, and proposes a novel strategy for optimizing storage usage within a price–volume demand response framework. The problem is formulated as a scalable, low-complexity mixed-integer linear program. Furthermore, a heuristic procedure is introduced to ensure redistribution of demand response rewards among participants according to their contribution to achieving demand–response goals. The proposed approach is designed to enhance the benefits for prosumers operating within a community compared to running independently.

Suggested Citation

  • Gianni Bianchini & Marco Casini & Milad Gholami, 2025. "Optimal Prosumer Storage Management in Renewable Energy Communities Under Demand Response," Energies, MDPI, vol. 18(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4904-:d:1749994
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    References listed on IDEAS

    as
    1. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    2. Hussain, Jawad & Huang, Qi & Li, Jian & Hussain, Fazal & Mirjat, Baqir Ali & Zhang, Zhenyuan & Ahmed, Syed Adrees, 2024. "A fully decentralized demand response and prosumer peer-to-peer trading for secure and efficient energy management of community microgrid," Energy, Elsevier, vol. 312(C).
    3. Bianchini, Gianni & Casini, Marco & Gholami, Milad, 2025. "Optimal operation of renewable energy communities under demand response programs," Energy, Elsevier, vol. 326(C).
    4. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Georghiou, George E., 2021. "Social arrangements, technical designs and impacts of energy communities: A review," Renewable Energy, Elsevier, vol. 169(C), pages 1138-1156.
    5. Daiva Stanelyte & Neringa Radziukyniene & Virginijus Radziukynas, 2022. "Overview of Demand-Response Services: A Review," Energies, MDPI, vol. 15(5), pages 1-31, February.
    6. Wang, Zhuo & Hou, Hui & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Xie, Changjun, 2024. "Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach," Applied Energy, Elsevier, vol. 357(C).
    7. Zanvettor, Giovanni Gino & Fochesato, Marta & Casini, Marco & Lygeros, John & Vicino, Antonio, 2024. "A stochastic approach for EV charging stations in demand response programs," Applied Energy, Elsevier, vol. 373(C).
    8. Bochun Zhan & Changsen Feng & Zhemin Lin & Xiaoyu Shao & Fushuan Wen, 2023. "Peer-to-Peer Energy Trading among Prosumers with Voltage Regulation Services Provision," Energies, MDPI, vol. 16(14), pages 1-22, July.
    9. Rogério Rocha & Ricardo Silva & João Mello & Sérgio Faria & Fábio Retorta & Clara Gouveia & José Villar, 2023. "A Three-Stage Model to Manage Energy Communities, Share Benefits and Provide Local Grid Services," Energies, MDPI, vol. 16(3), pages 1-24, January.
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