發(fā)布時(shí)間：2017-12-28 03:27

  本文關(guān)鍵詞：考慮負(fù)荷和分布式電源不確定性的配電網(wǎng)重構(gòu)方法研究　出處：《湖南大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 分布式電源 不確定性 配電網(wǎng)重構(gòu) 自適應(yīng)粒子群算法 場(chǎng)景法

【摘要】：分布式發(fā)電作為新能源發(fā)電并網(wǎng)的一種形式,以其環(huán)保、靈活高效等優(yōu)點(diǎn)得到了廣泛應(yīng)用,給電網(wǎng)帶來諸多便利,而同時(shí)不可避免地給電網(wǎng)帶來了復(fù)雜性和不確定性。分布式電源的接入使原有的配電網(wǎng)潮流分布、網(wǎng)損以及電壓穩(wěn)定發(fā)生變化。因此,為保證電網(wǎng)安全、穩(wěn)定運(yùn)行,配電網(wǎng)中的不確定性因素將不得不被納入考慮范圍。而配電網(wǎng)重構(gòu)是保證電網(wǎng)安全、穩(wěn)定運(yùn)行的重要技術(shù)手段�；诖�,本文在考慮負(fù)荷和分布式電源的不確定性情況下,對(duì)智能配電網(wǎng)重構(gòu)方法進(jìn)行研究。在對(duì)傳統(tǒng)配電網(wǎng)前推回代潮流計(jì)算的基礎(chǔ)上,采用節(jié)點(diǎn)分層的策略,將分布式電源分成PQ、PI、PV、PQ(V)四種節(jié)點(diǎn)類型進(jìn)行處理,建立了相應(yīng)的配電網(wǎng)潮流計(jì)算模型,給出了含DG的節(jié)點(diǎn)分層前推回代潮流計(jì)算方法,并對(duì)含各種類型DG的配電網(wǎng)潮流進(jìn)行仿真分析。提出了一種基于自適應(yīng)粒子群算法的智能配電網(wǎng)重構(gòu)方法。采用非線性慣性權(quán)重,確保算法能夠有效地處理配電網(wǎng)重構(gòu)方面的非線性優(yōu)化問題。建立了以網(wǎng)損最小為目標(biāo)的配電網(wǎng)重構(gòu)模型,采用整數(shù)編碼策略,并提出開關(guān)環(huán)路矩陣和送端矩陣來判斷不可行解,縮減不可行解的比例,以提升搜索效率；同時(shí)根據(jù)位置特點(diǎn)對(duì)位置更新公式進(jìn)行了改進(jìn)。通過仿真驗(yàn)證所提智能配電網(wǎng)重構(gòu)方法的有效性。提出基于場(chǎng)景法的考慮不確定性因素的智能配電網(wǎng)重構(gòu)方法。采用輪盤賭機(jī)制和Weibul概率分布函數(shù),分別對(duì)負(fù)荷和風(fēng)能變化進(jìn)行預(yù)測(cè)并生成隨機(jī)場(chǎng)景,將不確定性問題轉(zhuǎn)化為各自確定的場(chǎng)景內(nèi)的問題。對(duì)每個(gè)確定的場(chǎng)景,利用改進(jìn)的自適應(yīng)粒子群優(yōu)化算法實(shí)現(xiàn)網(wǎng)絡(luò)重構(gòu),并通過期望值的方法實(shí)現(xiàn)場(chǎng)景聚合,來確定最終的重構(gòu)方案。通過仿真驗(yàn)證了場(chǎng)景法處理考慮不確定性的配電網(wǎng)重構(gòu)的可行性,并分析不確定性對(duì)配電網(wǎng)重構(gòu)的影響。
[Abstract]:Distributed generation, as a form of grid connected new energy generation, has been widely used for its advantages of environmental protection, flexibility and efficiency, and brings convenience to the power grid. At the same time, it inevitably brings complexity and uncertainty to the power grid. The distribution of power flow, network loss and voltage stability of the distribution network changes with the access of distributed power supply. Therefore, in order to ensure the safe and stable operation of the power grid, the uncertain factors in the distribution network will have to be taken into consideration. The distribution network reconfiguration is an important technical means to ensure the safe and stable operation of the power grid. Based on this, this paper studies the method of intelligent distribution network reconfiguration considering the uncertainty of load and distributed power supply. Based on the flow calculation of the traditional distribution network power generation, using the node hierarchical strategy, distributed power is divided into PQ, PI, PV, PQ (V) four kinds of node types, the corresponding power flow calculation model is established, the node layer containing DG and backward power flow calculation method is given the trend and distribution network with various types of DG simulation analysis. An intelligent distribution network reconfiguration method based on Adaptive Particle Swarm Optimization (PSO) is proposed. The nonlinear inertia weight is adopted to ensure that the algorithm can effectively deal with the nonlinear optimization problems of distribution network reconfiguration. To establish the model of distribution network reconfiguration with the minimum network loss as the target, using integer encoding strategy, and puts forward the switch matrix and the loop matrix to determine the sending end of infeasible solution, reducing the proportion of feasible solutions to improve the search efficiency; at the same time according to the characteristics of the improved location location update formula for. The effectiveness of the proposed method of intelligent distribution network reconfiguration is verified by simulation. An intelligent distribution network reconfiguration method based on scene method is proposed, which considers uncertain factors. Based on roulette mechanism and Weibul probability distribution function, load and wind energy changes are forecasted and random scenes are generated, and the uncertain problems are transformed into the problems in each identified scenario. For each deterministic scenario, the improved adaptive particle swarm optimization algorithm is used to reconstruct the network, and the scene aggregation is achieved through the expected value method to determine the final reconstruction plan. The feasibility of the scene method to deal with the uncertainty of distribution network reconfiguration is verified by simulation, and the influence of uncertainty on the distribution network reconfiguration is analyzed.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM711
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本文編號(hào)：1344398