本文選題：分布式電源 + 粒子群算法��； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟的高速發(fā)展,對電能的需求也隨之日益增大。但是由于傳統(tǒng)的發(fā)電方式并不利于能源可持續(xù)發(fā)展,所以分布式發(fā)電顯得越來越重要。分布式發(fā)電不單能充分利用清潔新能源,而且分布式發(fā)電具有投資小、機動靈活、適應(yīng)性強的優(yōu)點。分布式電源的種類、接入位置以及接入的容量對電網(wǎng)的潮流、電壓質(zhì)量以及電網(wǎng)的各方面經(jīng)濟費用都有一定的影響。對分布式電源種類選擇,定容和選址的合理規(guī)劃,能夠改善電力系統(tǒng)的網(wǎng)損,用戶側(cè)的電壓質(zhì)量以及各種電網(wǎng)建造費用。因此分布式電源對配電網(wǎng)規(guī)劃有著極其重要意義。本文首先介紹了分布式電源基本概念,并且介紹了分布式電源接入配電網(wǎng)以后,對配電網(wǎng)電壓穩(wěn)定、網(wǎng)損、繼電保護(hù)以及系統(tǒng)的可靠性都有一定的影響。綜合以上的影響,對分布式電源的接入容量和位置提出了要求。在優(yōu)化之前,本文對傳統(tǒng)潮流算法進(jìn)行了改良,使其能夠適應(yīng)弱環(huán)和分布式電源的配電網(wǎng)情況,也能夠更加方便的計算優(yōu)化的結(jié)果。需要本文充分考慮了分布式電源對電力系統(tǒng)可靠性以及經(jīng)濟性的影響,基于電壓,電流以及分布式電源容量的限制條件,建立了分布式電源固定投資,網(wǎng)損以及電壓穩(wěn)定指標(biāo)的多目標(biāo)函數(shù)。借助該目標(biāo)函數(shù),能夠?qū)Ψ植际诫娫吹亩ㄈ莺瓦x址方案進(jìn)行合理的評估。通過對各類分布式電源定容和選址算法的研究,粒子群算法具有易于實現(xiàn),高效率等諸多特性,利于此類問題的解決。但是由于本文充分考慮了電力系統(tǒng)的經(jīng)濟性和可靠性,所提出的多目標(biāo)函數(shù)較一般目標(biāo)函數(shù)更為復(fù)雜,使用標(biāo)準(zhǔn)的粒子算法容易陷入局部收斂的陷阱。所以本文融入了遺傳算法的算子和退火算法思想,利用遺傳算法的變異和交叉算子以及退火思想,解決了在實現(xiàn)多目標(biāo)函數(shù)時候,容易陷入局部收斂的情況。改進(jìn)后的粒子群算法兼具遺傳算法、粒子群算法以及退火算法的優(yōu)點。本文從兩方面入手分析,一方面通過比較未接入分布式電源,標(biāo)準(zhǔn)粒子群算法的優(yōu)化方案以及改進(jìn)粒子群算法三種情況,論證了改進(jìn)后的粒子群算法的優(yōu)良性。另一方面,通過改進(jìn)后的粒子群算法與其他文獻(xiàn)的改進(jìn)算法的比較,論證本文改進(jìn)后的粒子群算法的優(yōu)良性。
[Abstract]:With the rapid development of economy, the demand for electric energy is increasing day by day. But because traditional power generation is not conducive to sustainable development of energy, distributed generation is becoming more and more important. Distributed generation not only makes full use of clean new energy, but also has the advantages of small investment, flexible mobility and strong adaptability. The type, location and capacity of distributed generation have a certain influence on the power flow, voltage quality and the economic cost of the power network. The selection of the type of distributed power supply, the reasonable planning of fixed capacity and location can improve the network loss of power system, the voltage quality of user side and the construction cost of various power network. So distributed generation is of great significance to distribution network planning. In this paper, the basic concept of distributed generation is introduced, and the influence of distributed generation on voltage stability, network loss, relay protection and system reliability is introduced. Combined with the above effects, the access capacity and location of distributed power generation are required. Before the optimization, the traditional power flow algorithm is improved to adapt to the distribution network of weak loop and distributed generation, and the results of optimization can be calculated more conveniently. In this paper, the influence of distributed power supply on the reliability and economy of power system is fully considered. Based on the limitation of voltage, current and capacity of distributed power supply, the fixed investment of distributed power supply is established. Multiobjective function of network loss and voltage stability index. With the help of the objective function, the fixed volume and location scheme of distributed power generation can be evaluated reasonably. Based on the research of fixed volume and location algorithm of distributed power supply, particle swarm optimization has many characteristics, such as easy to implement, high efficiency and so on, which is helpful to solve this kind of problems. However, since the economy and reliability of power system are fully considered in this paper, the proposed multi-objective function is more complex than the general objective function, and the standard particle algorithm is easy to fall into the trap of local convergence. So this paper integrates the operator of genetic algorithm and the idea of annealing algorithm, using the mutation and crossover operator of genetic algorithm and the idea of annealing, to solve the problem that the multi-objective function is easy to fall into local convergence. The improved particle swarm optimization algorithm has the advantages of genetic algorithm, particle swarm optimization algorithm and annealing algorithm. This paper analyzes from two aspects. On the one hand, by comparing the unconnected distributed power supply, the optimization scheme of standard particle swarm optimization algorithm and the improved particle swarm optimization algorithm, the paper proves the superiority of the improved particle swarm optimization algorithm. On the other hand, through the comparison between the improved PSO algorithm and the improved PSO algorithm in other literatures, the improved PSO algorithm is proved to be superior.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715

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本文編號：1977412