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Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

Author

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  • Qingyuan Yan

    (College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Zhaoyi Wang

    (College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Ling Xing

    (College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Chenchen Zhu

    (State Grid Taizhou Electric Power Co., Ltd., Taizhou 225300, China)

Abstract

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Suggested Citation

  • Qingyuan Yan & Zhaoyi Wang & Ling Xing & Chenchen Zhu, 2024. "Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System," Sustainability, MDPI, vol. 16(19), pages 1-44, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8497-:d:1488993
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    References listed on IDEAS

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    1. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    2. Wang, Bangyan & Wang, Xiuli & Zhu, Zongyao & Wu, Xiong, 2023. "Siting and sizing of energy storage for renewable generation utilization with multi-stage dispatch under uncertainty: A tri-level model and decomposition approach," Applied Energy, Elsevier, vol. 344(C).
    3. Zhang, Gang & Wen, Jiaxing & Xie, Tuo & Zhang, Kaoshe & Jia, Rong, 2023. "Bi-layer economic scheduling for integrated energy system based on source-load coordinated carbon reduction," Energy, Elsevier, vol. 280(C).
    4. Wen, Yan & Pan, Su & Li, Xinxin & Li, Zibo & Wen, Wuzhenghong, 2024. "Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach," Energy, Elsevier, vol. 299(C).
    5. Ebrie, Awol Seid & Kim, Young Jin, 2024. "Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid," Renewable Energy, Elsevier, vol. 230(C).
    6. Wu, Fuzhang & Yang, Jun & Li, Bin & Crisostomi, Emanuele & Rafiq, Hogir & Rashed, Ghamgeen Izat, 2024. "Uncertain scheduling potential of charging stations under multi-attribute uncertain charging decisions of electric vehicles," Applied Energy, Elsevier, vol. 374(C).
    7. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    8. Nayak, Dhyaan Sandeep & Misra, Shamik, 2024. "An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty," Energy, Elsevier, vol. 290(C).
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    Cited by:

    1. Lorenzo Becchi & Elisa Belloni & Marco Bindi & Matteo Intravaia & Francesco Grasso & Gabriele Maria Lozito & Maria Cristina Piccirilli, 2024. "A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems," Sustainability, MDPI, vol. 16(23), pages 1-21, November.

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