Electric Power ›› 2022, Vol. 55 ›› Issue (1): 46-54.DOI: 10.11930/j.issn.1004-9649.202012091

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Capacity Proportion Optimization of Wind, Solar Power and Battery Energy Storage System for Regional Power Grid Based on Source-Load Matching

LI Pai1, FANG Baomin2, QI Taiyuan2, HUANG Yuehui1, SHI Zhaodi1, WANG Lianfang2   

  1. 1. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute), Beijing 100192, China;
    2. State Grid Qinghai Electric Power Company, Xining 810008, China
  • Received:2020-12-21 Revised:2021-09-22 Online:2022-01-28 Published:2022-01-20
  • Supported by:
    This work is supported by the Science and Technology Project of SGCC (No.5419-202034054A-0-0-00)

Abstract: Capacity proportion optimization of the wind, solar power, and battery energy storage system is the basis for efficient utilization of renewable energy in a large-scale regional power grid. In this regard, an optimization method based on source-load matching was proposed to allocate the capacity proportion of the wind, solar, and battery energy storage system in a regional power grid. An optimization model was built to maximize wind and solar power generation. The optimization model considered the operational characteristics of wind and solar power and energy storage, constraints of installed capacity, annual curtailment rates, and proportions of wind and solar power generation. Moreover, the constraint of system-load matching deviation was placed to maintain the match between system power outputs and load demand. Then, with the annual output time-series of wind and solar as the input, the optimal capacity proportion of the wind, solar power, and battery energy storage system, which met the optimal source-load matching, was obtained by solving the proposed model. Finally, the effectiveness and practicability of the proposed model and method were verified by testing on an actual regional power grid in the north of China.

Key words: renewable energy, battery energy storage, capacity proportion optimization, source-load matching, time sequence simulation