【摘要】：在能源短缺和環(huán)境惡化的雙重壓力下,可再生新能源的開發(fā)利用受到了廣泛的關(guān)注,其中風(fēng)能以其清潔無污染、分布廣的優(yōu)點逐漸成為頗具發(fā)展前景的新能源。但在實際應(yīng)用中,由于風(fēng)能的波動性和不確定性,風(fēng)電場的接入將改變傳統(tǒng)配電網(wǎng)的潮流分布,影響系統(tǒng)的網(wǎng)絡(luò)損耗和節(jié)點電壓水平。因此,研究考慮風(fēng)電不確定性的無功優(yōu)化對系統(tǒng)安全穩(wěn)定運行以及風(fēng)電可靠并網(wǎng)都具有重要意義。論文首先闡述了風(fēng)電的發(fā)展現(xiàn)狀以及風(fēng)電并網(wǎng)對系統(tǒng)無功優(yōu)化的影響,詳細(xì)綜述了風(fēng)電并網(wǎng)系統(tǒng)無功優(yōu)化的研究現(xiàn)狀：在介紹了三種類型風(fēng)電機(jī)組結(jié)構(gòu)的基礎(chǔ)上,分析了異步風(fēng)電機(jī)組的穩(wěn)態(tài)模型及其在潮流計算中的節(jié)點處理方法；針對異步風(fēng)電機(jī)組并網(wǎng)給配網(wǎng)無功優(yōu)化帶來的不確定性問題,提出了基于風(fēng)速預(yù)測的場景分析法,將不確定性模型轉(zhuǎn)換成多個典型的確定性場景問題；在應(yīng)用中分析了多風(fēng)電機(jī)組同時并網(wǎng)的系統(tǒng)場景劃分方法,進(jìn)而建立了全場景下的兼顧系統(tǒng)經(jīng)濟(jì)性和安全穩(wěn)定性的多目標(biāo)無功優(yōu)化模型；接著采用最大模糊滿意度準(zhǔn)則將多目標(biāo)模型轉(zhuǎn)換成單目標(biāo)模型；并提出了一種改進(jìn)的量子行為粒子群算法來對模型進(jìn)行求解,該算法引入自適應(yīng)權(quán)重系數(shù)、柯西變異算子以及收縮-擴(kuò)張系數(shù)自適應(yīng)策略對量子行為粒子群算法進(jìn)行改進(jìn),從而改善了其在求解復(fù)雜多峰函數(shù)時存在搜索速度慢以及迭代后期容易發(fā)生早熟收斂等缺陷,并采用一個標(biāo)準(zhǔn)特征函數(shù)來驗證改進(jìn)算法的有效性。以改進(jìn)的69節(jié)點輻射狀配電系統(tǒng)來進(jìn)行算例分析,結(jié)果表明：所構(gòu)建的無功優(yōu)化模型對一天中風(fēng)電出力的隨機(jī)變化具有更好的適應(yīng)性,且能很好的協(xié)調(diào)系統(tǒng)經(jīng)濟(jì)性和安全穩(wěn)定性之間的關(guān)系；通過對三種算法的結(jié)果進(jìn)行詳細(xì)比較分析,驗證了所提出的改進(jìn)量子行為粒子群算法在模型求解中的收斂性和優(yōu)越性,這也為其他形式的新能源接入電網(wǎng)提供了一定的理論依據(jù)。
[Abstract]:Under the double pressure of energy shortage and environmental deterioration, the development and utilization of renewable new energy have been paid more and more attention, among which wind energy has become a promising new energy with its advantages of clean and pollution-free and wide distribution. However, in practical applications, because of the volatility and uncertainty of wind energy, the access of wind farm will change the distribution of power flow of traditional distribution network, and affect the network loss and voltage level of the system. Therefore, the study of reactive power optimization considering the uncertainty of wind power is of great significance to the safe and stable operation of the system and reliable grid connection of wind power. Firstly, the paper describes the development of wind power and the influence of wind power grid connection on reactive power optimization of wind power system, and summarizes the research status of reactive power optimization of wind power grid connection system in detail: on the basis of introducing the structure of three types of wind turbine units, The steady-state model of asynchronous wind turbine and its node processing method in power flow calculation are analyzed, and the scene analysis method based on wind speed prediction is proposed to solve the uncertainty of reactive power optimization caused by asynchronous wind turbine grid connection. The uncertainty model is transformed into several typical deterministic scene problems, and the system scenario partition method of multi-wind turbine is analyzed in the paper. Then, the multi-objective reactive power optimization model with both system economy and safety and stability is established, and then the multi-objective model is transformed into a single-objective model by using the maximum fuzzy satisfaction degree criterion. An improved quantum behavior particle swarm optimization algorithm is proposed to solve the model. The algorithm introduces adaptive weight coefficient, Cauchy mutation operator and contract-expansion coefficient adaptive strategy to improve the quantum behavior particle swarm optimization algorithm. In order to solve the complex multimodal function, it has some defects such as slow searching speed and precocious convergence in the late iteration, and a standard characteristic function is used to verify the effectiveness of the improved algorithm. An example of an improved 69 node radiative distribution system is presented. The results show that the proposed reactive power optimization model is more adaptable to the random variation of wind power output in a day. The results of the three algorithms are compared and analyzed in detail to verify the convergence and superiority of the improved quantum behavior particle swarm optimization algorithm in the model solving. This also provides a certain theoretical basis for other forms of new energy access to the grid.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614
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本文編號：2132909