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Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks

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  • Gupta, Rahul
  • Sossan, Fabrizio

Abstract

This work proposes a method for optimal planning (sizing and siting) energy storage systems (ESSs) in power distribution grids while considering the option of curtailing photo-voltaic (PV) generation. More specifically, for a given PV generation capacity to install, this method evaluates whether curtailing PV generation might be more economical than installing ESS. Indeed, while curtailing excess PV generation might be considered a last resort to avoid grid violations during operations, it is typically neglected in the planning phase. The proposed method accounts for the constraints of the power grid (i.e., nodal voltages, lines, and substation transformer limits) modeled by linearized power flow equations to keep the problem formulation tractable. The planning problem minimizes the net investment costs of the ESSs, and the imported and exported electricity costs considering a planning horizon of 20 years. The results are presented for a medium voltage (MV) distribution grid with different levels of installed capacity of PV generation, reflecting future scenarios of PV generation development. The sensitivity of the ESSs’ sizes and investment costs to the electricity prices accounting for variable levels of PV production in the global generation mix is also investigated.

Suggested Citation

  • Gupta, Rahul & Sossan, Fabrizio, 2023. "Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks," Applied Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003197
    DOI: 10.1016/j.apenergy.2023.120955
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    References listed on IDEAS

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    1. Hemmati, Reza & Saboori, Hedayat & Jirdehi, Mehdi Ahmadi, 2017. "Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources," Energy, Elsevier, vol. 133(C), pages 380-387.
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    5. Shabbir, Noman & Kütt, Lauri & Astapov, Victor & Daniel, Kamran & Jawad, Muhammad & Husev, Oleksandr & Rosin, Argo & Martins, João, 2024. "Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management," Applied Energy, Elsevier, vol. 372(C).
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    8. Zhipeng Jing & Lipo Gao & Yu Mu & Dong Liang, 2024. "Flexibility-Constrained Energy Storage System Placement for Flexible Interconnected Distribution Networks," Sustainability, MDPI, vol. 16(20), pages 1-17, October.

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