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考虑“风-光-荷-储”联合运行的配电网双层规划方法 被引量:8

Bi-Level Planning Method for Distribution Network with ‘WT-PV-Load-ESS'
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摘要 分布式电源与储能系统在配电网中的应用是当前研究的热点。提出了一种风机、光伏电源、储能系统在配电网中的双层规划模型。上层模型以投资、运维费用为优化目标,同时考虑区域的购电费用及网损费用,完成风机和光伏电源的容量配置;下层模型提出了一种储能系统削峰填谷的运行策略,基于该策略完成储能系统的选址定容工作。针对以上模型,提出以下求解方法:基于遗传算法求解上层模型,并通过K均值聚类方法对比研究输入数据序列长度对规划结果的影响,通过帕累托分析研究了投资运维费用与购电费用及网损费用的关系;使用YALMIP工具箱完成储能系统运行策略的求解,进而完成储能系统的选址定容工作。最后,通过IEEE-33节点标准配电系统仿真验证了所提模型在风机和光伏电源以及储能系统规划方面的有效性,结果显示所提储能运行策略具有良好的削峰填谷效果。 The research on distribution generation and energy storage system in distribution netw ork is a hot topic of current research. This paper proposes a bi-level planning model of ‘WT-PV-Load-ESS'in distribution netw ork. The upper model is optimized for the investment maintenance cost,and the pow er purchase and pow er losses cost are also taken into account in the allocation of PV and WT. The low er model proposes a operation strategy of ESS for peak shaving,based on w hich the sizing sitting w ork of ESS are completed. Aiming at the above model,the follow ing solutions are proposed in this paper: the upper model is solved based on genetic algorithm; the influence of the length of the input data on the planning results is compared by K-means clustering method; Pareto analysis is used to study the relationship betw een the investment maintenance cost and the pow er purchase pow er loss cost; at last,based on the estimation of ESS's capacity,the strategy of ESS is solved w ith YALM IP toolbox,then the sizing sitting w ork of ESS are completed. Finally,the effectiveness of the proposed model in the planning of ‘WT-PV-Load-ESS 'system are verified through the simulation of IEEE-33 node standard distribution system. The results show that the proposed ESS operation strategy has good effect in peak shaving.
出处 《电力建设》 北大核心 2017年第11期87-96,共10页 Electric Power Construction
基金 国家自然科学基金项目(5127713) 国网湖北省电力公司科技项目(增量配电模式下城市电网规划技术研究)~~
关键词 风机(WT) 光伏电源(PV) 储能系统(ESS) 双层优化 遗传算法 K均值聚类 wind turbine (WT) photovoltaic (PV) energy storage system (ESS) bi-level optimization genetic algorithm (GA) K-means clustering
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