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文章摘要
基于历史数据的多微网能源系统优化配置
Optimal configuration of multi-microgrid energy system based on historical data
Received:July 07, 2022  Revised:July 07, 2022
DOI:10.19753/j.issn1001-1390.2025.05.020
中文关键词: 多微网配置  能源枢纽  光伏预测  历史数据
英文关键词: multi-microgrid configuration, energy hub, PV forecast, historical data
基金项目:国家电网公司科技项目(SGJBZJ00YKJS1700828)
Author NameAffiliationE-mail
Xiu Chunnan* State Grid Jibei Electric Power Co.,Ltd. xiuchunnan@163.com 
Ren Dajiang State Grid Jibei Electric Power Co.,Ltd. xiuchunnan@163.com 
Liu Qiang State Grid Jibei Electric Power Co.,Ltd. xiuchunnan@163.com 
Wang Mengen State Grid Jibei Electric Power Co.,Ltd. xiuchunnan@163.com 
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中文摘要:
      针对国内分布式能源利用率不高,各能源微网间交互弱,设备配置不合理的问题,文中建立了基于历史数据的多微网综合能源调度优化配置模型。对微网中的热电联产、光伏等设备进行数学建模;收集地区历史年度风光出力数据,基于历史数据利用BP神经网络对次年光伏出力进行预测,以保证微网设备容量配置的合理性,并考虑了光伏预测误差对结果准确性的影响;以安装成本、维护成本等为目标函数进行优化分析,设置微网独立运行和联合运行两种运行方式进行对比分析。算例分析表明,所提基于历史数据的多微网综合能源调度优化配置模型能有效降低微网总运行成本、合理化微网设备配置,提高分布式能源利用率。
英文摘要:
      Aiming at the problems of low utilization rate of distributed energy in China, weak interaction between energy microgrids, and unreasonable equipment configuration, a multi-microgrid integrated energy scheduling optimization configuration model based on historical data is established in this paper. The mathematical modeling is carried out for the cogeneration, photovoltaic (PV) and other equipment in the micro-grid. It collects the historical annual wind and solar output data of the region, and uses the BP neural network to predict the photovoltaic output of the next year based on the historical data to ensure the reasonableness of capacity configuration of the micro-grid equipment, as well as considers the influence of photovoltaic prediction error on the accuracy of the results. Taking the installation cost, maintenance cost, etc. as the objective function to carry out the optimization analysis, it sets up the independent operation and joint operation of the micro-grid for comparative analysis. The case analysis shows that the proposed multi-microgrid integrated energy scheduling optimization configuration model based on historical data can effectively reduce the total operating cost of the micro-grid, rationalize the micro-grid equipment configuration, and improve the utilization rate of distributed energy.
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