【摘要】：相對(duì)于傳統(tǒng)能源而言,風(fēng)電具有隨機(jī)性以及波動(dòng)性。由于風(fēng)電的不可控性,對(duì)于電力系統(tǒng)而言,如何在電力系統(tǒng)規(guī)劃以及運(yùn)行層面上考慮風(fēng)電的隨機(jī)性以及波動(dòng)性成為當(dāng)今電力系統(tǒng)的熱點(diǎn)研究問題,尤其是給電力系統(tǒng)無功/電壓控制方面帶來一系列巨大的挑戰(zhàn)。由于風(fēng)電出力對(duì)電力系統(tǒng)運(yùn)行的影響具有復(fù)雜的非線性特征,傳統(tǒng)風(fēng)電場景模型難以保證風(fēng)電場景與電力系統(tǒng)優(yōu)化運(yùn)行保持一致。為此,不同于傳統(tǒng)方法的先對(duì)風(fēng)電場景聚類、再進(jìn)行無功優(yōu)化,而是建立系統(tǒng)運(yùn)行特性聚類得到系統(tǒng)運(yùn)行特性場景。由此出發(fā),本文從如下幾方面進(jìn)行研究:首先,風(fēng)電場景分成了靜態(tài)場景模型和動(dòng)態(tài)場景模型:利用非參數(shù)核密度估計(jì)的方法以及LHS的方法產(chǎn)生了風(fēng)電靜態(tài)模型;利用風(fēng)電預(yù)測的預(yù)測誤差分布以及風(fēng)電的波動(dòng)性分布,結(jié)合多元標(biāo)準(zhǔn)正態(tài)分布逆變換抽樣產(chǎn)生了風(fēng)電動(dòng)態(tài)場景。其次,在風(fēng)電靜態(tài)場景當(dāng)中考慮到K-means聚類方法難以確定聚類數(shù)的問題,通過聚類指標(biāo)得到運(yùn)行場景的最佳聚類數(shù)。將澳大利亞2個(gè)風(fēng)電場實(shí)際數(shù)據(jù)產(chǎn)生的靜態(tài)場景接入到IEEE30節(jié)點(diǎn)系統(tǒng)中,分別進(jìn)行傳統(tǒng)風(fēng)電場景分析和所提出的運(yùn)行場景分析,比較了系統(tǒng)網(wǎng)損和電壓的概率特性,驗(yàn)證了所提出的運(yùn)行場景分析方法的合理性和優(yōu)越性。其次考慮到運(yùn)行場景計(jì)算時(shí)間上的效率問題,并基于電力系統(tǒng)靈敏度的指標(biāo),提出了風(fēng)電靜態(tài)靈敏度場景,同樣接入到系統(tǒng)當(dāng)中驗(yàn)證了該模型的有效性。再者,在風(fēng)電動(dòng)態(tài)場景當(dāng)中,不同于靜態(tài)場景聚類,而是采用場景削減的方法,提出了一種動(dòng)態(tài)運(yùn)行場景模型。首先,風(fēng)電接入系統(tǒng)當(dāng)中進(jìn)行動(dòng)態(tài)無功優(yōu)化,得到控制變量樣本,其次對(duì)控制變量進(jìn)行場景削減得到風(fēng)電動(dòng)態(tài)運(yùn)行場景,最后,帶入到系統(tǒng)當(dāng)中進(jìn)行動(dòng)態(tài)無功優(yōu)化,得到動(dòng)態(tài)無功優(yōu)化的結(jié)果�；贗EEE30節(jié)點(diǎn)和愛爾蘭風(fēng)電數(shù)據(jù),比較了系統(tǒng)網(wǎng)損和電壓的概率特性,驗(yàn)證了所提出的動(dòng)態(tài)運(yùn)行場景分析方法的合理性和優(yōu)越性。
[Abstract]:Compared with traditional energy, wind power has randomness and volatility. Due to the uncontrollability of wind power, how to consider the randomness and fluctuation of wind power in power system planning and operation level has become a hot research issue in power system nowadays. In particular, it brings a series of great challenges to the reactive power / voltage control of power system. Because of the complex nonlinear characteristics of wind power generation, it is difficult for the traditional wind power scenario model to ensure that the wind power scenario is consistent with the optimal operation of the power system. Therefore, different from the traditional methods, the wind power scenarios are clustered first, then reactive power optimization is carried out. Instead, the system operation characteristic clustering is established to obtain the system operation characteristic scenarios. Firstly, the wind power scene is divided into static scene model and dynamic scenario model. The method of nonparametric kernel density estimation and LHS method are used to produce the static wind power model. Based on the prediction error distribution of wind power prediction and the fluctuation distribution of wind power, combined with the inverse sampling of multivariate standard normal distribution, the dynamic scene of wind power is generated. Secondly, considering the problem that the K-means clustering method is difficult to determine the clustering number in the static wind power scenario, the optimal cluster number of the running scenario can be obtained by clustering index. The static scene generated by the actual data of two wind farms in Australia is connected to the IEEE30 node system, and the traditional wind power scenario analysis and the proposed operation scenario analysis are carried out, respectively, and the probability characteristics of the system network loss and voltage are compared. The rationality and superiority of the proposed method are verified. Secondly, considering the efficiency of the calculation time of the running scenario, and based on the sensitivity index of the power system, the static sensitivity scenario of wind power is proposed, which is also connected to the system to verify the validity of the model. Furthermore, in the dynamic scenario of wind power, different from the static scenario clustering, a dynamic running scenario model is proposed by using the method of scene reduction. First, dynamic reactive power optimization is carried out in wind power access system, and control variable samples are obtained. Secondly, dynamic operation scenarios of wind power are obtained by scene reduction of control variables. Finally, dynamic reactive power optimization is carried out into the system. The results of dynamic reactive power optimization are obtained. Based on IEEE30 node and Irish wind power data, the probability characteristics of system loss and voltage are compared, and the rationality and superiority of the proposed dynamic operation scenario analysis method are verified.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙建偉;李禹鵬;楊增輝;嚴(yán)正;徐瀟源;馮楠;崔勇;;基于擬蒙特卡羅模擬和核密度估計(jì)的概率靜態(tài)電壓穩(wěn)定計(jì)算方法[J];電網(wǎng)技術(shù);2016年12期

2 楊茂;董駿城;;基于混合分布模型的風(fēng)電功率波動(dòng)特性研究[J];中國電機(jī)工程學(xué)報(bào);2016年S1期

3 明杰;向紅吉;戴朝華;陳維榮;廖國棟;;大規(guī)模風(fēng)電接入的運(yùn)行場景無功優(yōu)化評(píng)估[J];電網(wǎng)技術(shù);2016年09期

4 陳繼明;祁麗志;孫名妤;薛永端;;多場景下含風(fēng)電機(jī)組的配電網(wǎng)無功優(yōu)化的研究[J];電力系統(tǒng)保護(hù)與控制;2016年09期

5 孫田;鄒鵬;楊知方;鐘海旺;戴國華;夏清;;動(dòng)態(tài)無功優(yōu)化的多階段求解方法[J];電網(wǎng)技術(shù);2016年06期

6 謝敏;熊靖;劉明波;周尚籌;;基于Copula的多風(fēng)電場出力相關(guān)性建模及其在電網(wǎng)經(jīng)濟(jì)調(diào)度中的應(yīng)用[J];電網(wǎng)技術(shù);2016年04期

7 崔楊;徐蒙福;唐耀華;彭龍;嚴(yán)干貴;;基于集電系統(tǒng)無功靈敏度的雙饋風(fēng)電場無功控制策略[J];電網(wǎng)技術(shù);2015年09期

8 劉公博;顏文濤;張文斌;賈晨;周靜;耿光飛;;含分布式電源的配電網(wǎng)動(dòng)態(tài)無功優(yōu)化調(diào)度方法[J];電力系統(tǒng)自動(dòng)化;2015年15期

9 熊強(qiáng);陳維榮;張雪霞;戴朝華;李奇;;考慮多風(fēng)電場相關(guān)性的場景概率潮流計(jì)算[J];電網(wǎng)技術(shù);2015年08期

10 張斌;莊池杰;胡軍;陳水明;張明明;王科;曾嶸;;結(jié)合降維技術(shù)的電力負(fù)荷曲線集成聚類算法[J];中國電機(jī)工程學(xué)報(bào);2015年15期

，

本文編號(hào)：2165674