文章摘要
李扬,韦钢,马钰,等.含电动汽车和分布式电源的主动配电网动态重构[J].电力系统自动化,2018,42(5):102-110. DOI: 10.7500/AEPS20170926006.
LI Yang,WEI Gang,MA Yu, et al.Dynamic Reconfiguration of Active Distribution Network Considering Electric Vehicles and Distributed Generations[J].Automation of Electric Power Systems,2018,42(5):102-110. DOI: 10.7500/AEPS20170926006.
含电动汽车和分布式电源的主动配电网动态重构
Dynamic Reconfiguration of Active Distribution Network Considering Electric Vehicles and Distributed Generations
DOI:10.7500/AEPS20170926006
关键词: 分布式电源  电动汽车  不确定性  区间分析  动态重构  克隆选择算法
KeyWords: distributed generation  electric vehicle  uncertainty  interval analysis  dynamic reconfiguration  clonal selection algorithm
上网日期:2018-01-23
基金项目:上海绿色能源并网工程技术研究项目(13DZ2251900)
作者单位E-mail
李扬 上海电力学院电气工程学院, 上海市 200090  
韦钢 上海电力学院电气工程学院, 上海市 200090 wg5815@sohu.com 
马钰 上海电力学院电气工程学院, 上海市 200090  
李牧 国网嘉兴供电公司, 浙江省嘉兴市 314000  
何炜 上海电力学院电气工程学院, 上海市 200090  
李光宏 上海电力学院电气工程学院, 上海市 200090  
摘要:
      分布式电源和大量电动汽车充电功率的间歇性和随机性使配电网重构面临新的挑战。在此背景下,提出了一种基于区间数方法的含电动汽车和分布式电源的动态重构策略。文中引入区间数以描述分布式电源出力和电动汽车充电负荷的不确定性,以区间数描述最小化网损为目标函数,提出依据动态降损参数的动态时段划分,建立配电网动态重构的数学模型,在利用Krawczyk-Moore区间迭代法对潮流方程进行求解的同时引入仿射乘除运算代替区间乘除运算,改善了区间运算过于保守的问题。最后,结合邻域搜索以及克隆选择算法提出了求解上述模型的优化方法,以实现考虑多种不确定因素下主动配电网动态重构。算例分析证明所提方法相较于传统人工智能算法具有优越性。
Abstract:
      The intermittent and randomness of distributed generations and charging power of electric vehicles bring new challenges to the distribution network reconfiguration. In this context, an interval number method based dynamic reconfiguration strategy considering electric vehicles and distributed generation is proposed. This paper introduces the uncertainty of the distribution power supply and the charging load of electric vehicles. The objective function to minimize network loss by interval number is proposed. Meanwhile, the dynamic time division of dynamic loss parameters is come up with and a mathematical model of dynamic reconfiguration of distribution network is established. The flow equation is solved by using Krawczyk-Moore interval iteration method as well as an affine multiplication and division operation is introduced to replace the interval multiplication, which help to solve the problem of interval arithmetic. Finally, the optimization method for the model above is proposed by combining neighborhood search and clonal selection algorithm, in order to realize dynamic reconfiguration of active distribution network with multiple uncertainties. The analysis of the example shows that the proposed method is superior to the traditional artificial intelligence algorithm.
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