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文章摘要
考虑电压权重的配电网分布式电源优化配置
Optimal allocation of distributed generation in distribution network considering voltage weighting factor
Received:December 10, 2018  Revised:December 10, 2018
DOI:10.19753/j.issn1001-1390.2020.08.007
中文关键词: 配电网  分布式电源  电压权重因子  NSGA-II算法  灰靶决策
英文关键词: distribution  network, distributed  generation, voltage  weighting factors, non-dominated  sorting genetic  algorithm-II, grey  target decision
基金项目:
Author NameAffiliationE-mail
Wang Shao* State Key Laboratory of Power Transmission Equipment System Security and New Technology,Chongqing University wangshao100@163.com 
Deng Xianfang State Key Laboratory of Power Transmission Equipment System Security and New Technology,Chongqing University e305642@163.com 
Xie Qingyang Yunnan Power Grid Co., Ltd. Electric Power Research Institute Kuenming 515073275@qq.com 
Zhang chengyu State Key Laboratory of Power Transmission Equipment System Security and New Technology,Chongqing University 2285483928@qq.com 
Su Shi Yunnan Power Grid Co., Ltd. Electric Power Research Institute Kuenming 472732908@qq.com 
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中文摘要:
      为更合理配置分布式电源(Distributed Generation,DG),提出考虑电压权重因子的配电网DG优化配置模型。该模型以电压总偏差和包括DG投资运行维护费、网损费、购电费的总费用为目标函数。通过在该模型的电压总偏差目标函数中引入电压权重因子来反映DG配置对各节点电压的影响和反映对用户电压质量需求的评判,从而使优化过程有利于较大电压偏差的节点或具有电压评分较高的节点的电压幅值更容易接近其期望值。基于拉丁超立方抽样得到DG和负荷的状态,采用NSGA-II算法和基于信息熵赋权的灰靶决策算法得到最优方案。仿真算例表明文中所提方法的可行性和有效性。
英文摘要:
      In order to configure distributed generation (DG) more reasonably, an optimal DG configuration model for distribution network considering voltage weighting factor is proposed. The objective functions of the model are the total voltage deviation and the total cost including DG investment cost, operation and maintenance cost, network loss cost and power purchase cost. By introducing the voltage weighting factors into the total voltage deviation objective function of the model, the influence of DG configuration on the voltage of each node and the evaluation of the voltage quality requirements of users are reflected. The voltage amplitude of the node with larger voltage deviation or with higher voltage score is easier to approach its expectation. DG and load status are obtained by Latin hypercube sampling. NSGA-II algorithm and grey target decision algorithm based on entropy weight are used to get the optimal solution. The simulation example shows the feasibility and effectiveness of the proposed method.
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