计及风速与负荷相关性的配电网重构研究

发布时间：2018-09-04 08:27

【摘要】：配电网重构是配电自动化研究的重要组成部分，是提高系统经济性和安全性的重要方式。近年来，随着分布式发电的迅速发展，风力发电作为一种重要发电形式，在配电网中的渗透率也逐渐提高。大力发展风力发电是未来电力系统发展的必然趋势。风速与负荷均受多种气候因素影响，随时间变化，二者具有一定相关性，并非独立随机变量。因此，如何在配电网重构中有效的计及风速与负荷相关性的影响，用联合分布描述二者的相关性，，建立更精确的数学模型具有重要研究意义。为克服传统相关性模型存在的不足，本文提出了一种基于Copula理论建立风速与负荷相关性模型的方法。由风速、负荷历史样本数据，得到各自的边缘分布。运用极大似然估计法，估计备选Copula函数中的参数。基于Copula理论，根据最短距离法的判定准则，从备选函数中选择一个最优的Copula函数来描述风速与负荷之间的相关结构。对加拿大萨斯喀彻温地区风速数据和IEEE-RTS年度负荷时序曲线样本进行建模，算例表明：Copula函数能够精确模拟样本风速与负荷，很好的解决风速与负荷相关性问题。根据分布式电源对配电网潮流的影响以及其在配电网潮流计算中的节点类型，基于经验分布函数，研究了含风力发电与负荷随机性及其相关性的配电网潮流随机模型。利用建立的风速与负荷相关性模型，根据Copula函数抽样方法，产生一定规模的风速与负荷序列。以蒙特卡洛模拟为基础，采用一种计及风速与负荷相关性的蒙特卡洛配电网随机潮流算法计算配电网随机潮流。对IEEE33和PGE69节点配电网进行随机潮流计算，结果表明：该算法能够有效的计及风速与负荷相关性对配电网随机潮流的影响。以配电网有功损耗期望值最小为目标函数，研究了含风力发电的配电网重构数学模型，同时计及了风速-负荷随机性和相关性的影响。提出了配电网重构的改进遗传算法，可避免遗传操作中产生的大量不可行解。根据计及风速与负荷相关性的蒙特卡洛配电网随机潮流，采用一种计及二者相关性的配电网重构算法进行配电网重构。对IEEE33和PGE69节点配电网进行重构，结果表明：该算法能够有效降低配电网有功损耗，全面计及风速与负荷相关性对配电网重构的影响。
[Abstract]:Distribution network reconfiguration is an important part of distribution automation research and an important way to improve system economy and security. In recent years, with the rapid development of distributed generation, wind power generation as an important form of power generation, the permeability in the distribution network gradually increased. The development of wind power generation is the inevitable trend of power system development in the future. Wind speed and load are affected by many climatic factors, but they are not independent random variables. Therefore, it is important to study how to take into account the influence of wind speed and load in the reconfiguration of distribution network, to describe the correlation between wind speed and load by joint distribution, and to establish a more accurate mathematical model. In order to overcome the shortcomings of traditional correlation model, a method of establishing wind speed and load correlation model based on Copula theory is proposed in this paper. From the wind speed and load history sample data, each edge distribution is obtained. The maximum likelihood estimation method is used to estimate the parameters in the alternative Copula function. Based on the Copula theory and the decision criterion of the shortest distance method, an optimal Copula function is selected from the alternative function to describe the correlation structure between wind speed and load. The data of wind speed in Saskatchewan, Canada, and the sample of IEEE-RTS annual load time series curve are modeled. The example shows that the function of "1: Copula" can accurately simulate the wind speed and load of the sample and solve the problem of correlation between wind speed and load. According to the influence of distributed generation on distribution network power flow and its node type in power flow calculation of distribution network, based on empirical distribution function, the stochastic model of distribution network power flow with the randomness of wind power generation and load and its correlation is studied. According to the Copula function sampling method, a certain scale wind speed and load series is generated by using the established wind speed and load correlation model. Based on Monte Carlo simulation, a Monte Carlo stochastic power flow algorithm considering the correlation between wind speed and load is used to calculate the stochastic power flow of distribution network. The results of stochastic power flow calculation for IEEE33 and PGE69 node distribution networks show that the algorithm can effectively take into account the influence of wind speed and load on stochastic power flow in distribution networks. Taking the minimum expected value of active power loss in distribution network as objective function, the mathematical model of distribution network reconfiguration with wind power generation is studied, and the effects of wind speed and load randomness and correlation are taken into account. An improved genetic algorithm for reconfiguration of distribution network is proposed, which can avoid a large number of infeasible solutions generated in genetic operations. According to the stochastic power flow of Monte Carlo distribution network considering the correlation between wind speed and load, a distribution network reconfiguration algorithm considering the correlation between them is adopted. The results of reconfiguration of IEEE33 and PGE69 nodes show that the algorithm can effectively reduce the active power loss of distribution network, and take into account the influence of wind speed and load on the reconfiguration of distribution network.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614;TM76

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