【摘要】：配電網(wǎng)重構(gòu)是配電自動化研究的重要組成部分，是提高系統(tǒng)經(jīng)濟(jì)性和安全性的重要方式。近年來，隨著分布式發(fā)電的迅速發(fā)展，風(fēng)力發(fā)電作為一種重要發(fā)電形式，在配電網(wǎng)中的滲透率也逐漸提高。大力發(fā)展風(fēng)力發(fā)電是未來電力系統(tǒng)發(fā)展的必然趨勢。 風(fēng)速與負(fù)荷均受多種氣候因素影響，隨時(shí)間變化，二者具有一定相關(guān)性，并非獨(dú)立隨機(jī)變量。因此，如何在配電網(wǎng)重構(gòu)中有效的計(jì)及風(fēng)速與負(fù)荷相關(guān)性的影響，用聯(lián)合分布描述二者的相關(guān)性，，建立更精確的數(shù)學(xué)模型具有重要研究意義。 為克服傳統(tǒng)相關(guān)性模型存在的不足，本文提出了一種基于Copula理論建立風(fēng)速與負(fù)荷相關(guān)性模型的方法。由風(fēng)速、負(fù)荷歷史樣本數(shù)據(jù)，得到各自的邊緣分布。運(yùn)用極大似然估計(jì)法，估計(jì)備選Copula函數(shù)中的參數(shù)�；贑opula理論，根據(jù)最短距離法的判定準(zhǔn)則，從備選函數(shù)中選擇一個(gè)最優(yōu)的Copula函數(shù)來描述風(fēng)速與負(fù)荷之間的相關(guān)結(jié)構(gòu)。對加拿大薩斯喀徹溫地區(qū)風(fēng)速數(shù)據(jù)和IEEE-RTS年度負(fù)荷時(shí)序曲線樣本進(jìn)行建模，算例表明：Copula函數(shù)能夠精確模擬樣本風(fēng)速與負(fù)荷，很好的解決風(fēng)速與負(fù)荷相關(guān)性問題。 根據(jù)分布式電源對配電網(wǎng)潮流的影響以及其在配電網(wǎng)潮流計(jì)算中的節(jié)點(diǎn)類型，基于經(jīng)驗(yàn)分布函數(shù)，研究了含風(fēng)力發(fā)電與負(fù)荷隨機(jī)性及其相關(guān)性的配電網(wǎng)潮流隨機(jī)模型。利用建立的風(fēng)速與負(fù)荷相關(guān)性模型，根據(jù)Copula函數(shù)抽樣方法，產(chǎn)生一定規(guī)模的風(fēng)速與負(fù)荷序列。以蒙特卡洛模擬為基礎(chǔ)，采用一種計(jì)及風(fēng)速與負(fù)荷相關(guān)性的蒙特卡洛配電網(wǎng)隨機(jī)潮流算法計(jì)算配電網(wǎng)隨機(jī)潮流。對IEEE33和PGE69節(jié)點(diǎn)配電網(wǎng)進(jìn)行隨機(jī)潮流計(jì)算，結(jié)果表明：該算法能夠有效的計(jì)及風(fēng)速與負(fù)荷相關(guān)性對配電網(wǎng)隨機(jī)潮流的影響。 以配電網(wǎng)有功損耗期望值最小為目標(biāo)函數(shù)，研究了含風(fēng)力發(fā)電的配電網(wǎng)重構(gòu)數(shù)學(xué)模型，同時(shí)計(jì)及了風(fēng)速-負(fù)荷隨機(jī)性和相關(guān)性的影響。提出了配電網(wǎng)重構(gòu)的改進(jìn)遺傳算法，可避免遺傳操作中產(chǎn)生的大量不可行解。根據(jù)計(jì)及風(fēng)速與負(fù)荷相關(guān)性的蒙特卡洛配電網(wǎng)隨機(jī)潮流，采用一種計(jì)及二者相關(guān)性的配電網(wǎng)重構(gòu)算法進(jìn)行配電網(wǎng)重構(gòu)。對IEEE33和PGE69節(jié)點(diǎn)配電網(wǎng)進(jìn)行重構(gòu)，結(jié)果表明：該算法能夠有效降低配電網(wǎng)有功損耗，全面計(jì)及風(fēng)速與負(fù)荷相關(guān)性對配電網(wǎng)重構(gòu)的影響。
[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.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614;TM76

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