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Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

Author

Listed:
  • Simin Peng

    (School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China)

  • Liyang Zhu

    (School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China)

  • Zhenlan Dou

    (State Grid Shanghai Integrated Energy Service Co., Ltd., Shanghai 200023, China)

  • Dandan Liu

    (School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China)

  • Ruixin Yang

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

Abstract

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

Suggested Citation

  • Simin Peng & Liyang Zhu & Zhenlan Dou & Dandan Liu & Ruixin Yang & Michael Pecht, 2023. "Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources," Energies, MDPI, vol. 16(9), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3899-:d:1139698
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    Citations

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    Cited by:

    1. Peng, Simin & Miao, Yifan & Xiong, Rui & Bai, Jiawei & Cheng, Mengzeng & Pecht, Michael, 2024. "State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter," Applied Energy, Elsevier, vol. 360(C).
    2. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
    3. Djalma M. Falcão & Sun Tao & Glauco N. Taranto & Thiago J. Masseran A. Parreiras & Murilo E. C. Bento & Dany H. Huanca & Hugo Muzitano & Paulo Esmeraldo & Pedro Lima & Lillian Monteath & Roberto Brand, 2024. "Case Studies of Battery Energy Storage System Applications in the Brazilian Transmission System," Energies, MDPI, vol. 17(22), pages 1-16, November.
    4. Ahmed A. Alguhi & Majed A. Alotaibi & Essam A. Al-Ammar, 2023. "Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks," Sustainability, MDPI, vol. 16(1), pages 1-23, December.
    5. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Yuan, Caichenran & Cang, Junjie & Zhang, Kai & Pecht, Michael, 2024. "Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model," Energy, Elsevier, vol. 298(C).

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