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On Optimal Battery Sizing for Households Participating in Demand-Side Management Schemes

Author

Listed:
  • Matthias Pilz

    (School of Computer Science & Mathematics at Kingston University London, Kingston upon Thames KT1 2EE, UK)

  • Omar Ellabban

    (Iberdrola Innovation Middle East, Qatar Science & Technology Park, Doha 210177, Qatar)

  • Luluwah Al-Fagih

    (School of Computer Science & Mathematics at Kingston University London, Kingston upon Thames KT1 2EE, UK
    Division of Engineering Management & Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar)

Abstract

The smart grid with its two-way communication and bi-directional power layers is a cornerstone in the combat against global warming. It allows for the large-scale adoption of distributed (individually-owned) renewable energy resources such as solar photovoltaic systems. Their intermittency poses a threat to the stability of the grid, which can be addressed by the introduction of energy storage systems. Determining the optimal capacity of a battery has been an active area of research in recent years. In this research, an in-depth analysis of the relation between optimal capacity and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme. The scheme is based on a non-cooperative dynamic game approach in which participants compete for the lowest electricity bill by scheduling their energy storage systems. The results are evaluated based on self-consumption, the peak-to-average ratio of the aggregated load and potential cost reductions. Furthermore, the difference between individually-owned batteries and a centralised community energy storage system serving the whole community is investigated.

Suggested Citation

  • Matthias Pilz & Omar Ellabban & Luluwah Al-Fagih, 2019. "On Optimal Battery Sizing for Households Participating in Demand-Side Management Schemes," Energies, MDPI, vol. 12(18), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3419-:d:264355
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    References listed on IDEAS

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    Cited by:

    1. Paweł Kelm & Irena Wasiak & Rozmysław Mieński & Andrzej Wędzik & Michał Szypowski & Ryszard Pawełek & Krzysztof Szaniawski, 2022. "Hardware-in-the-Loop Validation of an Energy Management System for LV Distribution Networks with Renewable Energy Sources," Energies, MDPI, vol. 15(7), pages 1-18, April.
    2. Ji-Won Lee & Mun-Kyeom Kim & Hyung-Joon Kim, 2021. "A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy," Energies, MDPI, vol. 14(3), pages 1-21, January.
    3. Juha Koskela & Antti Mutanen & Pertti Järventausta, 2020. "Using Load Forecasting to Control Domestic Battery Energy Storage Systems," Energies, MDPI, vol. 13(15), pages 1-20, August.
    4. Gurupraanesh Raman & Yang Yang & Jimmy Chih-Hsien Peng, 2024. "The social factors shaping community microgrid operation," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    5. Yeon Ju Baik & Ye Gu Kang, 2022. "Distributed ESS Capacity Decision for Home Appliances and Economic Analysis," Energies, MDPI, vol. 15(15), pages 1-16, July.
    6. Lim, Kai Zhuo & Lim, Kang Hui & Wee, Xian Bin & Li, Yinan & Wang, Xiaonan, 2020. "Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling," Applied Energy, Elsevier, vol. 269(C).
    7. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    8. Sofiane Kichou & Nikolaos Skandalos & Petr Wolf, 2020. "Evaluation of Photovoltaic and Battery Storage Effects on the Load Matching Indicators Based on Real Monitored Data," Energies, MDPI, vol. 13(11), pages 1-20, May.

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