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计及分时电价下用户需求响应的分布式储能多目标优化运行
引用本文:王守相,张善涛,王凯,黄碧斌. 计及分时电价下用户需求响应的分布式储能多目标优化运行[J]. 电力自动化设备, 2020, 40(1): 125-132
作者姓名:王守相  张善涛  王凯  黄碧斌
作者单位:天津大学 智能电网教育部重点实验室,天津 300072,天津大学 智能电网教育部重点实验室,天津 300072,天津大学 智能电网教育部重点实验室,天津 300072,国网能源研究院,北京 102209
基金项目:国家电网公司科技项目(SGERIxnyKJ[2017]95)
摘    要:以配电网网损和电压偏差最小为优化目标,计及分时电价下的用户需求响应,建立了与用户互动的分布式储能多目标优化运行模型,将电价变动引起的用户需求响应与分布式储能动态结合,探究了分布式储能在优化运行方面提升配电网运行水平的作用。采用改进的遗传算法对所建立的模型进行求解,证明了计及分时电价下负荷需求响应的分布式储能多目标优化运行模型能够有效降低配电网网损,减小线路电压偏差,同时也验证了所提出的改进遗传算法具有进化速度快、求解结果优的特点。

关 键 词:分布式储能  配电网  分时电价  需求响应  遗传算法  优化

Multi-objective optimal operation of distributed energy storage considering user demand response under time-of-use price
WANG Shouxiang,ZHANG Shantao,WANG Kai and HUANG Bibin. Multi-objective optimal operation of distributed energy storage considering user demand response under time-of-use price[J]. Electric Power Automation Equipment, 2020, 40(1): 125-132
Authors:WANG Shouxiang  ZHANG Shantao  WANG Kai  HUANG Bibin
Affiliation:Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China,Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China,Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China and State Grid Energy Research Institute, Beijing 102209, China
Abstract:The minimum of distribution network loss and voltage deviation is taken as the optimization objective, and a multi-objective optimal model of distributed energy storage is established considering user demand response under time-of-use price. Distributed energy storage is explored to improve the operation level of distribution network by the dynamic combination of user demand response under time-of-use price. An improved genetic algorithm is presented to solve the optimal model. The test results prove that the multi-objective optimization operation of distributed energy storage considering user demand response under time-of-use price can effectively reduce the distribution network loss and the voltage deviation. It also shows that the improved genetic algorithm has the characteristics of faster evolution speed and better solution results.
Keywords:distributed energy storage   distribution network   time-of-use price   demand response   genetic algorithms   optimization
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