發(fā)布時間：2018-05-24 08:45

  本文選題：概率潮流計算 + 拉丁超立方算法　； 參考：《蘭州交通大學》2017年碩士論文

【摘要】：隨著化石能源的逐漸枯竭以及全球和國家低碳環(huán)保的政策需要,風力發(fā)電、太陽能發(fā)電等清潔低碳能源在新能源比例中越來越重。但是由于風能和太陽能具有較大隨機性和間歇性,這些分布式能源的出力方式會導致隨機性變化,特別是在大規(guī)模的新能源并網以后,例如發(fā)電機輸出功率波動、負荷功率變化等對電網的安全運行造成的影響更加明顯,這些存在問題對傳統的潮流計算提出了新的挑戰(zhàn)。傳統潮流電力系統分析長期以來建立在確定性潮流計算的基礎之上,但是在實際網路中,系統的參數,網絡的拓撲結構、母線的負荷等都是不確定的值,這就需要考慮通過大量的計算,耗費時間成本。而且這很難反應整個體統的總體狀況,傳統的確定性潮流計算已無法滿足上述問題,因此考慮隨機因素影響的概率潮流計算法得到了發(fā)展和應用。研究概率潮流計算的前提是分析概率潮流所用到的算法,因此本文先分別分析了蒙特卡羅法,拉丁超立算法及其優(yōu)缺點。在分析拉丁超立方算法的基礎上,對該算法中排序步驟進行改進。提出一種基于隨機行走原理的拉丁超立方算法,并將該方法應用在概率問題求解中,仿真結果證明了該算法所具有的優(yōu)越性。以下是論文完成的主要內容:(1)本文針對目前常見概率算法進行研究分析,重點闡述目前算法存在的缺陷和不足,最后指出所改進方法對于解決所存在問題的必要性。(2)針對闡述拉丁超立方算法中存在的缺點,將隨機行走算法引入要改進的算法中。本文通過MATLAB軟件進行編程,以蒙特卡羅法為參考值,利用測試函數在加入含風電的IEEE-14節(jié)點和IEEE-118系統的條件下,比較基于隨機行走算法的拉丁超立方算法(Random Walk Latin hypercube Sampling,RWLHS)和基于施密特正交化法的拉丁超立方算法(Gram-Schmidt Latin hypercube Sampling,GSLHS)兩種算法,從而得出RWLHS算法的有效性。(3)其次將本方法引用到含光伏電場的電力系統中,建立了負荷、發(fā)電機和光伏電場的模型,通過改進的方法分析系統節(jié)點加入分布能源前后節(jié)點電壓的變化和支路潮流的變化。
[Abstract]:With the depletion of fossil energy and the need of global and national low-carbon environmental protection policies, clean and low-carbon energy sources, such as wind power and solar power, are becoming more and more important in the proportion of new energy sources. But because wind and solar are more random and intermittent, the way these distributed sources of energy are produced can lead to random changes, especially after large-scale new sources of energy are connected to the grid, such as generator output power fluctuations. The influence of load power change on the safe operation of power grid is more obvious. These problems pose a new challenge to the traditional power flow calculation. The traditional power flow analysis is based on the deterministic power flow calculation for a long time, but in the actual network, the parameters of the system, the topological structure of the network and the load of the bus are all uncertain values. This requires consideration of time-consuming costs through a large number of calculations. And it is very difficult to reflect the overall situation of the whole system. The traditional deterministic power flow calculation can no longer meet the above problems, so the probabilistic power flow calculation method considering the influence of random factors has been developed and applied. The premise of studying the calculation of probabilistic power flow is to analyze the algorithms used in probabilistic power flow, so this paper first analyzes the Monte Carlo method, Latin superposition algorithm and its merits and demerits respectively. Based on the analysis of the Latin hypercube algorithm, the sorting steps in the algorithm are improved. A Latin hypercube algorithm based on random walk principle is proposed and applied to probabilistic problem solving. The simulation results show the superiority of the algorithm. The following is the main content of this paper: (1) this paper studies and analyzes the common probability algorithms, focusing on the shortcomings and shortcomings of the current algorithms. Finally, the necessity of the improved method for solving the existing problems is pointed out. (2) aiming at the shortcomings of the Latin hypercube algorithm, the random walk algorithm is introduced into the improved algorithm. In this paper, the MATLAB software is used to program, the Monte Carlo method is used as the reference value, and the test function is used under the condition of adding the IEEE-14 node and the IEEE-118 system with wind power. The Latin hypercube algorithm based on random walk algorithm, Random Walk Latin hypercube sampling RWLHS, and the Latin hypercube algorithm Gram-Schmidt Latin hypercube sampling GSLHSbased on Schmidt orthogonalization are compared. The validity of RWLHS algorithm is obtained. Secondly, the method is applied to the power system with photovoltaic electric field, and the model of load, generator and photovoltaic electric field is established. The changes of node voltage and branch power flow before and after adding distributed energy are analyzed by the improved method.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM744

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本文編號：1928429