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A Computationally Efficient Optimization Method for Battery Storage in Grid-connected Microgrids Based on a Power Exchanging Process

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  • Ping Liu

    (School of Electric Power, South China University of Technology, Guangzhou 510241, China)

  • Zexiang Cai

    (School of Electric Power, South China University of Technology, Guangzhou 510241, China)

  • Peng Xie

    (School of Electric Power, South China University of Technology, Guangzhou 510241, China)

  • Xiaohua Li

    (School of Electric Power, South China University of Technology, Guangzhou 510241, China)

  • Yongjun Zhang

    (School of Electric Power, South China University of Technology, Guangzhou 510241, China)

Abstract

Battery storage (BS) sizing problems for grid-connected microgrids (GC μ Gs) commonly use stochastic scenarios to represent uncertain natures of renewable energy and load demand in the GC μ G. Though taking a large number of stochastic scenarios into consideration can deliver a relatively accurate optimal result, it can also highly deteriorate the computational efficiency of the sizing problem. To make an accuracy-efficiency trade-off, a computationally efficient optimization method to optimize the BS capacities based on the power exchanging process of the GC μ G is proposed in this paper. According to the imbalanced power of the GC μ G, this paper investigates the power exchanging process between the GC μ G, BS and external grid. Motivated by the BS dynamics, a forward/backward sweep-based energy management scheme is proposed based on the power exchanging process. A heuristic two-level optimization model is developed with sizing BS as the upper-level problem and optimizing the operational cost of the GC μ G as the lower-level problem. The lower-level problem is solved by the proposed energy management scheme and the objective function of the upper-level is minimized by the pattern search (PS) algorithm. To validate the accuracy and computational efficiency of the proposed method, the numerical results are compared with the mixed integer linear programming (MILP) method. The comparison shows that the proposed method shares similar accuracy but is much more time-efficient than the MILP method.

Suggested Citation

  • Ping Liu & Zexiang Cai & Peng Xie & Xiaohua Li & Yongjun Zhang, 2019. "A Computationally Efficient Optimization Method for Battery Storage in Grid-connected Microgrids Based on a Power Exchanging Process," Energies, MDPI, vol. 12(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1512-:d:224882
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    References listed on IDEAS

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    Cited by:

    1. Xiaodong Yu & Xia Dong & Shaopeng Pang & Luanai Zhou & Hongzhi Zang, 2019. "Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation," Energies, MDPI, vol. 12(24), pages 1-21, December.
    2. Marcin Szott & Szymon Wermiński & Marcin Jarnut & Jacek Kaniewski & Grzegorz Benysek, 2021. "Battery Energy Storage System for Emergency Supply and Improved Reliability of Power Networks," Energies, MDPI, vol. 14(3), pages 1-21, January.
    3. Daniel Reich & Giovanna Oriti, 2021. "Rightsizing the Design of a Hybrid Microgrid," Energies, MDPI, vol. 14(14), pages 1-22, July.

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