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Optimal Scheduling Strategy for Distribution Network with Mobile Energy Storage System and Offline Control PVs to Minimize the Solar Energy Curtailment

Author

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  • San Kim

    (School of Electrical Engineering, Anam Campus, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea)

  • Jinyeong Lee

    (Electricity Policy Research Center, Korea Electrotechnology Research Institute (KERI), Uiwang 16029, Republic of Korea)

Abstract

As offline control photovoltaic (PV) plants are not equipped with online communication and remote control systems, they cannot adjust their power in real-time. Therefore, in a distribution network saturated with offline control PVs, the distribution system operator (DSO) should schedule the distributed energy resources (DERs) considering the uncertainty of renewable energy to prevent curtailment due to overvoltage. This paper presents a day-ahead network operation strategy using a mobile energy storage system (MESS) and offline control PVs to minimize power curtailment. The MESS model efficiently considers the transportation time and power loss of the MESS, and models various operating modes, such as the charging, discharging, idle, and moving modes. The optimization problem is formulated based on mixed-integer linear programming (MILP) considering the spatial and temporal operation constraints of MESSs and is performed using chanced constrained optimal power flow (CC-OPF). The upper limits for offline control PVs are set based on the probabilistic approach, thus mitigating overvoltage due to forecasting errors. The proposed operation strategy was tested in the IEEE 33-node distribution system coupled with a 15-node transportation system. The test results show the effectiveness of the proposed method for minimizing curtailment in offline control PVs.

Suggested Citation

  • San Kim & Jinyeong Lee, 2024. "Optimal Scheduling Strategy for Distribution Network with Mobile Energy Storage System and Offline Control PVs to Minimize the Solar Energy Curtailment," Energies, MDPI, vol. 17(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2234-:d:1389225
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    References listed on IDEAS

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    2. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    3. Ji-Won Cha & Sung-Kwan Joo, 2021. "Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)," Energies, MDPI, vol. 14(21), pages 1-19, October.
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