本文關鍵詞： 抽水蓄能 光伏發(fā)電 風力發(fā)電 免疫粒子群算法 經(jīng)濟效益 功率波動　出處：《華北電力大學》2014年碩士論文　論文類型：學位論文

【摘要】：目前,為解決化石能源短缺和能源浪費問題,新能源的開發(fā)利用就顯得尤為重要,集合風、光優(yōu)勢互補的一種新的多能互補開發(fā)方式具有較好的經(jīng)濟意義和可持續(xù)發(fā)展意義。風力發(fā)電和太陽能光伏發(fā)電具有無法準確預測、隨機性以及不穩(wěn)定性的特點,導致風力和光伏發(fā)電無法被有效地利用并引起功率輸出的波動。將風力發(fā)電、光伏發(fā)電與抽水蓄能組成風光水聯(lián)合發(fā)電系統(tǒng),很好的解決了這些問題,不僅可平滑風電和光伏發(fā)電的功率輸出,而且隨著電力市場的改革,實行了峰谷電價,也可以達到充分利用風電和光伏發(fā)電的目的。 本文首先分析了風電場、太陽能光伏電站和抽水蓄能電站的相關知識,風光發(fā)電的互補特性。提出在風光發(fā)電互補的基礎上結(jié)合抽水蓄能電站,構(gòu)建了風光水聯(lián)合系統(tǒng)的發(fā)電運行模型。針對聯(lián)合系統(tǒng)的特點,對系統(tǒng)中抽水蓄能電站的容量進行計算,達到優(yōu)化配置的目的,最終起到了降低功率波動、減少棄能的效果。為使風光水聯(lián)合發(fā)電系統(tǒng)達到經(jīng)濟效益最大化優(yōu)化調(diào)度并且平抑功率波動的目的,本文將以功率波動最小為目標的函數(shù)引入到經(jīng)濟效益最大化模型中。本文在粒子群算法的基礎上,由于其易早熟、后期搜索速度慢而且精度較低的特點,提出一種動態(tài)調(diào)整學習因子的免疫粒子群算法。該算法通過對算法速度公式中的學習因子進行改進,采用非對稱線性動態(tài)調(diào)整學習因子的方法,增強前期的全局搜索能力以及后期的局部搜索能力,快速得到最優(yōu)解。該算法在該多目標聯(lián)合優(yōu)化調(diào)度系統(tǒng)的求解中顯著提高了搜索精度,表明了模型和算法的有效性。 本文研究了基于改進免疫粒子群算法在風光水聯(lián)合運行系統(tǒng)中的應用,結(jié)果表明,這是合理利用風能和太陽能資源的有效途徑,不但提高了使用新能源的效益,同時達到了降低風電場、光伏電站輸出功率波動的目的,具有可觀的經(jīng)濟效益和社會效益。
[Abstract]:At present, in order to solve the problems of fossil energy shortage and energy waste, the development and utilization of new energy is particularly important. A new multi-energy complementary development method with complementary optical advantages has better economic and sustainable development significance. Wind power generation and solar photovoltaic power generation have the characteristics of uncertainty, randomness and instability. Wind and photovoltaic power generation can not be effectively used and cause the fluctuation of power output. Wind power generation, photovoltaic power generation and pumped storage constitute the wind water combined power generation system, which solves these problems very well. Not only the power output of wind power and photovoltaic generation can be smoothed, but also with the reform of power market, peak-valley electricity price has been implemented, and the purpose of making full use of wind power and photovoltaic power generation can also be achieved. This paper first analyzes the knowledge of wind farm, solar photovoltaic power station and pumped storage power station, and the complementary characteristics of wind power generation. According to the characteristics of the combined system, the capacity of the pumped-storage power station in the system is calculated to achieve the purpose of optimizing the configuration, and finally to reduce the power fluctuation. In order to maximize the economic benefit of the combined generation system and to stabilize the power fluctuation, In this paper, the function of minimum power fluctuation is introduced into the economic benefit maximization model. On the basis of particle swarm optimization algorithm, due to its precocity, slow search speed and low precision, This paper presents an immune particle swarm optimization algorithm which dynamically adjusts the learning factor. By improving the learning factor in the speed formula of the algorithm, an asymmetric linear dynamic adjustment method is used to adjust the learning factor. The global search ability in the early stage and the local search ability in the later stage are enhanced, and the optimal solution is obtained quickly. The algorithm improves the search accuracy significantly in the solution of the multi-objective joint optimal scheduling system, and shows the validity of the model and the algorithm. In this paper, the application of improved immune particle swarm optimization algorithm in wind and water combined operation system is studied. The results show that it is an effective way to utilize wind and solar energy resources reasonably, and not only improves the efficiency of using new energy. At the same time, it can reduce the fluctuation of output power of wind farm and photovoltaic power station, and has considerable economic and social benefits.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM61;TP18

【參考文獻】

相關期刊論文 前10條

1 劉科研;盛萬興;李運華;;基于改進免疫遺傳算法的無功優(yōu)化[J];電網(wǎng)技術;2007年13期

2 魯忠燕;鄧集祥;汪永紅;;基于免疫粒子群算法的電力系統(tǒng)無功優(yōu)化[J];電網(wǎng)技術;2008年24期

3 劉偉;彭冬;卜廣全;蘇劍;;光伏發(fā)電接入智能配電網(wǎng)后的系統(tǒng)問題綜述[J];電網(wǎng)技術;2009年19期

4 吳杰康;韓軍鋒;劉蔚;吳智華;;基于反捕食粒子群算法的電力系統(tǒng)經(jīng)濟調(diào)度方法[J];電網(wǎng)技術;2010年06期

5 劉東冉;陳樹勇;馬敏;王皓懷;侯俊賢;馬世英;;光伏發(fā)電系統(tǒng)模型綜述[J];電網(wǎng)技術;2011年08期

6 王曉蘭;李志偉;;風電-抽水蓄能電站聯(lián)合運行的多目標優(yōu)化[J];蘭州理工大學學報;2011年05期

7 于宗艷;韓連濤;;免疫粒子群優(yōu)化算法及應用[J];計算機仿真;2008年12期

8 肖曉偉;肖迪;林錦國;肖玉峰;;多目標優(yōu)化問題的研究概述[J];計算機應用研究;2011年03期

9 史震古;抽水蓄能電站綜述[J];江西水利科技;1994年03期

10 阮e，

本文編號：1511665