• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2019, Vol. 40 ›› Issue (5): 20-27.doi: 10.3969/j.issn.1000-7229.2019.05.003

• 储能系统关键技术 ·栏目主持 李建林教授级高级工程师· • 上一篇    下一篇

基于改进K-means聚类的电力市场下分布式储能系统经济性调控模型

尹渠凯1, 米增强1,贾雨龙1,范辉2   

  1. 1.分布式储能与微网河北省重点实验室(华北电力大学), 河北省保定市071003;2.国网河北省电力有限公司, 石家庄市050022
  • 出版日期:2019-05-01
  • 作者简介:尹渠凯(1993),男,硕士研究生,主要研究方向为分布式储能系统建模; 米增强(1960),男,教授,主要研究方向为新能源电力系统、柔性负荷聚合及调控技术; 贾雨龙(1989),男,博士研究生,通信作者,主要研究方向为分布式储能系统运行、柔性负荷聚合技术; 范辉(1969),男,高级工程师,主要研究方向为电力系统分析与控制、电网保护与仿真。
  • 基金资助:
    国家电网公司科技项目资助(KJGW2018-014);中央高校基本科研业务费专项资金资助(2018QN075)

Economy Regulation Method for Distributed Energy Storage in Distribution Network According to K-means Clustering

YIN Qukai1, MI Zengqiang1, JIA Yulong1, FAN Hui2   

  1. 1.Hebei Key Laboratory of Distributed Energy Storage and Microgrid(North China Electric Power University), Baoding 071003, Hebei Province, China;2. State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050022, China
  • Online:2019-05-01
  • Supported by:
    This work is supported by State Grid Corporation of China Research Program(No. KJGW2018-014)and Fundamental Research Funds for Central Universities(No. 2018QN075).

摘要: 随着电力市场的发展和电储能技术经济性的不断提升,在配电网中大量接入分布式储能系统已成为一种发展趋势。为了提高分布式储能技术参与电网运行的经济性,解决其高成本与低收益之间的矛盾,建立了一种基于K-means聚类的电力市场下分布式储能系统优化调控模型。该模型能够平衡运行过程中分布式储能系统个体之间寿命损耗的差异,降低总运行成本,并提高运行过程中分布式储能系统总体的调控潜力。以电力市场下参数不同的500个储能系统1天的运行过程为例进行验证,试验结果证明了所提调控模型的可行性和有效性,为分布式储能技术参与电网运行提供了新思路。

关键词: 储能系统, K-means聚类, 聚合建模, 可调度潜力

Abstract: With the development of power market and the improvement of energy storage technology, it has become a trend to access a large number of distributed energy storage systems in distribution networks. In order to improve the economy of distributed energy storage participating in power grid operation and solve the contradiction between high cost and low profit, this paper establishes an optimal regulation model of distributed energy storage on the basis of improved K-means clustering in power market. The model can balance the difference of life loss between distributed energy storage individuals, reduce the total operating cost and improve the scheduling potentials of distributed energy storage during operation. Finally, 500 energy storage systems with different parameters in the electricity market are taken as  examples to verify the feasibility and validity of the proposed regulation model, which provides a new idea for distributed energy storage to participate in the operation of power grid.

Key words: energy storage system, K-means clustering, aggregation modeling, scheduling potential

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