基于改进遗传算法的配电网无功规划优化
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摘要
配电网无功优化可以有效地降低网络有功损耗,并提高系统的电压合格率,从而降低网络运行费用,提高供电质量。目前对于无功优化主要有两个方面的研究方向:
     一个突破方面就是使规划中所建立的数学模型尽量反映实际情况,即目标函数和多种约束条件接近电力系统运行情况;另一个是算法的研究,主要解决求解时间长,容易限于局部最优及易产生“维数灾”而无法进行大规模优化计算的问题。
     配电网潮流是配电网无功优化的基础和工具,其计算速度和收敛性将直接影响优化的效果。通过对配网潮流进行研究,采用了改进的快速分解法和前推回推法求解潮流分布。通过配网29节点、33节点和69节点算例,对两种算法的收敛性和计算效率进行数值分析,并给出收敛机理证明。
     本文提出了考虑电能损耗和无功补偿投资的综合目标函数,并计及不同负荷下的运行方式,以求获得较优的规划方案。对于电压越界点,采用惩罚函数予以解决。补偿点选为配电网的中压侧(10KV)。无功补偿方式不仅考虑并联电容器的补偿,对于系统中的电压越限点,还考虑增设电抗器的补偿方式,提出了电容器和电抗器的补偿原则。对优化问题的求解采用遗传算法,应用灵敏度分析的方法应用到无功优化方法中,在计算中大大降低了无功配置的搜索空间。并对常规遗传算法进行改进:种群数的选取采用初始种群和正常种群规模相结合,遗传运算采用自适应交叉率与变异率。通过改进,遗传算法的全局搜索能力增强,搜索时间大大缩短,算法稳定性得以提高,同时保留了常规算法的各种优点。
     根据上述算法编制了实用软件,并对配网33节点、69节点和银川城市配电网进行了验算,通过与常规遗传算法比较,证明本文模型和算法的实用性、可靠性和较优性。
The reactive power optimization of a radial distribution system can efficiently minimize the real power losses and improve voltage level of it. so that the yearly running charge will be reduced and the quality of power energy can be increased. There are two directions to research in the reactive power optimization. One is that the mathematical model used in reactive power planning is in line with realistic situation, namely objective function and its constraints are in accordance with realistic situation. The other is that the solving methods are modified to deal with the problem of local optimum and computational burden.
    Power flow method of a distribution system is the base and tool of reactive power optimization, whose convergence and computation speed is very important to the efficiency of optimization. This paper programs two different typical methods, and makes a comparison and analyses of them through 29-node, 33-node and 69-node feeder.
    In this paper, we present the objective function considering energy loss and investment of reactive compensation equipment at diverse load levels, Penalty function is considered to deal with variables violating the constraints. The mathematical model is established which takes all practical constraints and reactive power regulation measures into considered. The candidate locations for capacitor are situated in the end of lOKv line, a genetic algorithm with sensitivity analysis is designed to limit the number of candidate locations for capacitor. At the same time, in order to decrease the searching time of GA , self-adaptive crossover rate and mutation rate are introduced, in order to reducing the search space for the optimization algorithm we consider a big population size in the beginning several generations in order to cover more space, a small population size is selected later in order to increase the convergence speed..
    Based on the proposed mathematical model and algorithm, a computer program is developed. 33-node , 69-node feeder and Yinchuan radial distribution system are studied. All the results given in the paper show that the genetic algorithm with sensitivity analysis is stable and fast and suitable for practical application medium-scale power system.
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