【摘要】：大規(guī)模風(fēng)電并網(wǎng)在推動電力行業(yè)快速發(fā)展,提高國家經(jīng)濟發(fā)展水平的同時,也對電網(wǎng)的規(guī)劃和電力系統(tǒng)的正常運行造成了極大的影響,尤其是風(fēng)電自身的不確定性等特性所引起的一系列不容忽視的電能質(zhì)量問題,其中電壓波動和閃變是風(fēng)電所造成的主要負(fù)面影響。為了保證規(guī)劃后的電網(wǎng)其電能質(zhì)量符合國家的有關(guān)標(biāo)準(zhǔn),這就需要在電網(wǎng)規(guī)劃中考慮風(fēng)電場接入后產(chǎn)生的電能質(zhì)量問題。電網(wǎng)規(guī)劃已經(jīng)被證明是一個具有多目標(biāo)、非線性等特點的相對復(fù)雜優(yōu)化問題,對其求解存在一定的困難�？紤]到傳統(tǒng)優(yōu)化算法的目標(biāo)權(quán)重人為選擇以及模擬二進(jìn)制交叉算子的全局搜索性能較弱等問題,本文提出將正態(tài)分布交叉(NDX)算子引入到NSGA2算法中。一方面NSGA2算法利用快速非支配排序,擁擠度以及精英策略篩選出能夠使優(yōu)化方案的各目標(biāo)函數(shù)值都比較大(或者比較小)的Pareto最優(yōu)解集,克服傳統(tǒng)多目標(biāo)優(yōu)化算法的缺點;另一方面NDX算子借助正態(tài)分布和離散重組操作來提高對解空間的探索和開發(fā)能力,進(jìn)一步擴大搜索空間。將這二者結(jié)合可以提高算法的全局搜索性能以及收斂性,從而獲得高質(zhì)量且分布均勻的Pareto最優(yōu)解。由于風(fēng)電場并網(wǎng)點處的閃變值與在此處電網(wǎng)的等效阻抗有著直接的關(guān)系,也就和電網(wǎng)的網(wǎng)絡(luò)架構(gòu)有關(guān),本文在考慮風(fēng)電并網(wǎng)所引起的電能質(zhì)量的基礎(chǔ)上,構(gòu)建了以線路總長度以及PCC處閃變值為目標(biāo)的含風(fēng)電場多目標(biāo)電網(wǎng)規(guī)劃模型�？紤]到風(fēng)電以及負(fù)荷預(yù)測自身的不確定性,借助基于蒙特卡羅的直流概率潮流計算方法以及機會約束規(guī)劃方法來判斷規(guī)劃方案是否違反支路功率約束,以校驗優(yōu)化方案的可靠性。為了驗證本文所提模型以及方法的有效性,以IEEE-6節(jié)點以及修改后的IEEE-24節(jié)點系統(tǒng)為例,采用傳統(tǒng)遺傳算法以及兩種不同NSGA2算法進(jìn)行規(guī)劃優(yōu)化,對比結(jié)果表明NSGA2算法在處理多目標(biāo)規(guī)劃優(yōu)化問題中要比傳統(tǒng)遺傳算法表現(xiàn)出很大的優(yōu)越性,而相比經(jīng)典NSGA2算法,本文所提算法又具有較高的決策效率,其優(yōu)化得到的含風(fēng)電場電網(wǎng)的規(guī)劃方案,在保證經(jīng)濟性的同時,也使風(fēng)機并網(wǎng)點的電能質(zhì)量達(dá)到更高的品質(zhì)。
[Abstract]:Large-scale wind power grid connection not only promotes the rapid development of the power industry and improves the level of national economic development, but also has a great impact on the planning of the power grid and the normal operation of the power system. In particular, a series of power quality problems caused by the uncertainty of wind power itself can not be ignored, among which voltage fluctuation and flicker are the main negative effects caused by wind power. In order to ensure that the power quality of the planned power grid conforms to the relevant national standards, it is necessary to consider the power quality problems caused by the access of wind farms in the power grid planning. Power grid planning has been proved to be a relatively complex optimization problem with multi-objective, nonlinear and other characteristics, and it is difficult to solve it. Considering the artificial selection of the target weight of the traditional optimization algorithm and the weak global search performance of the simulated binary cross operator, this paper proposes to introduce the normal distribution cross (NDX) operator into the NSGA2 algorithm. On the one hand, NSGA2 algorithm uses fast non-dominant sorting, congestion and elite strategy to select the Pareto optimal solution set which can make the objective function value of the optimization scheme relatively large (or relatively small), so as to overcome the shortcomings of the traditional multi-objective optimization algorithm. On the other hand, NDX operator improves the ability to explore and develop the solution space by means of normal distribution and discrete recombination operation, and further expands the search space. By combining the two methods, the global search performance and convergence of the algorithm can be improved, and the Pareto optimal solution with high quality and uniform distribution can be obtained. Because the flicker value at the parallel dot of the wind farm is directly related to the equivalent impedance of the power grid here, and it is also related to the network structure of the power grid, this paper considers the power quality caused by the wind power grid connection. A multi-objective power grid planning model with wind farm is constructed, which aims at the total length of the line and the flicker value at PCC. Considering the uncertainty of wind power and load forecasting itself, the DC probabilistic power flow calculation method based on Monte Carlo and the opportunity constrained programming method are used to judge whether the planning scheme violates the branch power constraint. To verify the reliability of the optimization scheme. In order to verify the effectiveness of the model and method proposed in this paper, taking the IEEE-6 node and the modified IEEE-24 node system as examples, the traditional genetic algorithm and two different NSGA2 algorithms are used for planning optimization. The comparison results show that NSGA2 algorithm is superior to the traditional genetic algorithm in dealing with multi-objective programming optimization problems, and compared with the classical NSGA2 algorithm, the proposed algorithm has higher decision efficiency. The optimized planning scheme of wind farm power grid not only ensures the economy, but also makes the power quality of fan parallel network reach higher quality.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM715

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本文編號：2484380