文章摘要
孙磊,张璨,林振智,等.计及电动汽车充电站作为黑启动电源的网架重构优化策略[J].电力系统自动化,2015,39(14):75-81. DOI: 10.7500/AEPS20140825008.
SUN Lei,ZHANG Can,LIN Zhenzhi, et al.An Optimization Strategy for Network Reconfiguration with Charging Stations of Electric Vehicles as Black-start Power Sources[J].Automation of Electric Power Systems,2015,39(14):75-81. DOI: 10.7500/AEPS20140825008.
计及电动汽车充电站作为黑启动电源的网架重构优化策略
An Optimization Strategy for Network Reconfiguration with Charging Stations of Electric Vehicles as Black-start Power Sources
DOI:10.7500/AEPS20140825008
关键词: 电力系统恢复  电动汽车  充电站  机会约束规划  双层优化
KeyWords: power system restoration  electric vehicle  charging stations  chance-constrained programming  bi-level optimization
上网日期:2015-07-13
基金项目:国家重点基础研究发展计划(973计划)资助项目(2013CB228202);国家自然科学基金资助项目(51177145,51377005);国网浙江省电力公司科技项目(5211DF13500M)
作者单位E-mail
孙磊 浙江大学电气工程学院, 浙江省杭州市 310027  
张璨 浙江大学电气工程学院, 浙江省杭州市 310027  
林振智 浙江大学电气工程学院, 浙江省杭州市 310027 zhenzhi.lin@gmail.com 
文福拴 浙江大学电气工程学院, 浙江省杭州市 310027
文莱科技大学电机与电子工程系, 文莱斯里巴加湾 BS8675 
 
吕浩华 国网浙江省电力公司电动汽车服务分公司, 浙江省杭州市 310007  
李波 国网浙江省电力公司电动汽车服务分公司, 浙江省杭州市 310007  
摘要:
      基于电池租赁的换电池模式与大规模集中型充电站配合具有商业竞争潜力,有望得到推广。在此背景下,提出了一种计及电动汽车充电站的网架重构优化策略。首先,对充电站可用电池容量建模,得到针对给定系统停电时刻充电站可提供的启动容量。建立了基于双层优化的网架重构模型,在上层模型中,以最大化系统可用发电容量为目标确定发电机组恢复时刻; 在下层模型中,以线路充电电容之和最小为目标确定恢复路径。之后,采用机会约束规划处理相关的不确定性因素,并与双层优化模型相结合构建基于机会约束规划的双层网架重构优化模型,进而采用改进的粒子群算法求解该优化问题。最后,以修改的新英格兰10机39节点系统为例说明了所发展的模型与方法的基本特征。
Abstract:
      Supported by large-scale centralized charging stations of electric vehicles (EVs), the battery swapping mode with battery leasing is a commercially competitive way for the development of EVs. Given this background, a network reconfiguration strategy taking the EV charging stations into account is presented. First, the capacity provided by the batteries in an EV charging station is modeled, and thus the capacity for restarting the non-black-start units can be obtained whenever a power outage occurs. Then, a bi-level optimization based model for the network reconfiguration is proposed. In the upper-level optimization model, the recovery time of a generating unit is determined by maximizing the restored generation capacity, while in the lower-level the restoration path is optimized by minimizing the charging capacitance of the recovered lines. A chance-constrained programming method is employed to address the risks arising from uncertain factors during the system restoration, and then a bi-level optimization model for the network reconfiguration based on chance-constrained programming is presented. An improved particle swarm optimization algorithm is employed to solve the model developed. Finally, a modified New England 10-unit 39-bus power system is employed to demonstrate the basic characteristics of the model and method developed. This work is supported by National Basic Research Program of China (973 Program) (No. 2013CB228202), National Natural Science Foundation of China (No. 51177145, No. 51377005) and a project from State Grid Zhejiang Electric Power Company (No. 5211DF13500M).
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