本文關(guān)鍵詞：含風電電力系統(tǒng)的場景分析方法及其在隨機優(yōu)化中的應(yīng)用　出處：《武漢大學》2014年博士論文　論文類型：學位論文

  更多相關(guān)文章： 風電 場景生成 場景削減 隨機優(yōu)化 機組組合

【摘要】：風力發(fā)電具有顯著的隨機性和波動性,且不受調(diào)度。隨著我國風電并網(wǎng)比例與日俱增,風電“并網(wǎng)難”問題日益突出。在電力系統(tǒng)規(guī)劃與運行中如何充分考慮風電的隨機性和波動性,成為了當前世界范圍內(nèi)工業(yè)界和學術(shù)界普遍關(guān)心的前沿性難題。 論文首先構(gòu)建了基于場景分析方法的含大規(guī)模風電電力系統(tǒng)隨機優(yōu)化決策框架,根據(jù)是否考慮隨機變量的相關(guān)性,將風電場景細化為靜態(tài)場景和動態(tài)場景兩方面分別論述場景生成的方法,并結(jié)合各自的具體實例——大規(guī)模風電輸送通道落點優(yōu)選和隨機機組組合,深入研究了場景分析方法在隨機優(yōu)化中的應(yīng)用,取得了以下幾方面的創(chuàng)新成果： 在場景生成方面：風功率預(yù)測誤差的解析理論分布對于不同的預(yù)測手段和應(yīng)用地點尚不具有廣泛的適用性。對此,本文提出了“以隨機變量的經(jīng)驗分布作為場景生成的輸入”的思想；通過等間距抽樣法,對單個隨機變量或多個相互獨立的隨機變量的經(jīng)驗分布進行抽樣,采用柯列斯基分解或場景樹法對抽樣值進行重新排列組合,介紹了一套基于拉丁超立方抽樣的靜態(tài)場景生成方法；已有的動態(tài)場景生成方法沒有計及風電的波動特性。并且,與目前廣泛使用的風功率“點預(yù)測”手段相應(yīng)的動態(tài)場景生成方法不完備。對此,本文提出了一種考慮風功率隨機性和波動性的動態(tài)場景生成方法,采用“預(yù)測箱”統(tǒng)計風功率點預(yù)測的預(yù)測誤差經(jīng)驗分布,通過對多元正態(tài)分布協(xié)方差結(jié)構(gòu)的關(guān)鍵參數(shù)進行辨識和逆變換抽樣,使得隨機生成的動態(tài)場景既符合風電的隨機性又符合波動性。 關(guān)于風功率靜態(tài)場景在隨機優(yōu)化中的應(yīng)用方面：論文將靜態(tài)場景生成與隨機優(yōu)化方法結(jié)合應(yīng)用到我國某省接入外來大規(guī)模風電的輸送通道落點優(yōu)選問題中,設(shè)計了風電落點多目標決策的評價指標體系及其權(quán)重設(shè)置方法；通過仿真分析,本文發(fā)現(xiàn)電力系統(tǒng)的多樣化運行方式可能會影響決策模型的結(jié)果。因此,論文利用帶有概率信息的多個典型靜態(tài)場景刻畫了運行方式的多樣性,提出了一種考慮多場景的風電落點隨機優(yōu)化方法。 關(guān)于風功率動態(tài)場景在隨機優(yōu)化中的應(yīng)用：論文以隨機機組組合作為對象,通過計算發(fā)現(xiàn)：經(jīng)過經(jīng)典的場景削減方法得到的最有可能發(fā)生的場景可能忽略部分極端場景,這些極端場景雖然發(fā)生概率很低,但是一旦發(fā)生造成的影響是巨大的。鑒于此,本文從場景生成得到的原始大量場景集合中辨識出極端邊界場景,并將其引入到修正后的隨機機組組合模型中刻畫極端事件造成的損失期望,構(gòu)成了兩階段隨機機組組合方法,權(quán)衡了系統(tǒng)運行的經(jīng)濟性和可靠性。并且隨機機組組合模型經(jīng)過混合整數(shù)線性化,調(diào)用CPLEX軟件實現(xiàn)了問題的快速準確求解。
[Abstract]:Wind power generation has significant randomness and volatility, and is not scheduled. With the increasing proportion of wind power grid in China. The problem of "grid-connected difficulty" of wind power is becoming increasingly prominent. How to fully consider the randomness and volatility of wind power in power system planning and operation. Has become the world-wide industry and academia generally concerned about the forefront of the problem. Firstly, a stochastic optimization decision framework for wind power system with large-scale wind power system is constructed based on scenario analysis method, according to whether or not to consider the correlation of random variables. The wind power scene is divided into static scene and dynamic scene, and the methods of scene generation are discussed respectively, and combined with their specific examples, large-scale wind power transmission channel drop point selection and random unit combination. The application of scene analysis method in random optimization is deeply studied, and some innovative results are obtained as follows: In the aspect of scene generation, the analytic theoretical distribution of wind power prediction error is not widely applicable to different prediction methods and application sites. In this paper, the idea of "taking the empirical distribution of random variables as the input of scene generation" is put forward. The empirical distribution of a single random variable or several independent random variables is sampled by the method of equidistant sampling, and the sampling values are rearranged and combined by Kiresky decomposition or scene tree method. A static scene generation method based on Latin hypercube sampling is introduced. The existing dynamic scene generation methods do not take into account the fluctuating characteristics of wind power. Moreover, the dynamic scene generation methods corresponding to the wind power "point prediction" which are widely used at present are not complete. In this paper, a dynamic scene generation method considering the randomness and fluctuation of wind power is proposed. The prediction error empirical distribution of wind power point prediction is calculated by "forecasting box". By identifying and sampling the key parameters of multivariate normal distribution covariance structure, the randomly generated dynamic scene is not only consistent with the randomness of wind power, but also in line with the volatility of wind power. On the application of static wind power scene in random optimization: this paper combines static scene generation with stochastic optimization method to select the location of transmission channel in a province of our country. The evaluation index system and its weight setting method of multi-objective decision making of wind power drop point are designed. Through simulation analysis, it is found that the diversified operation mode of power system may affect the result of decision model. Therefore, the paper describes the diversity of operation mode by using several typical static scenes with probability information. In this paper, a stochastic optimization method for wind power drop points considering multiple scenarios is proposed. About the application of wind power dynamic scene in stochastic optimization: this paper takes the random unit as the object. It is found by calculation that the most likely scenarios obtained by the classical scenario reduction method may ignore some extreme scenarios, although the probability of these extreme scenarios is very low. In view of this, the extreme boundary scene is identified from the original set of a large number of scenarios generated by the scene. It is introduced to the modified stochastic unit commitment model to describe the loss expectation caused by extreme events, which constitutes a two-stage stochastic unit combination method. The economy and reliability of the system are weighed, and the stochastic unit model is linearized by mixed integers, and the CPLEX software is used to solve the problem quickly and accurately.
【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TM614

【參考文獻】

相關(guān)期刊論文 前10條

1 王芝茗;蘇安龍;魯順;;基于電力平衡的遼寧電網(wǎng)接納風電能力分析[J];電力系統(tǒng)自動化;2010年03期

2 肖創(chuàng)英;汪寧渤;陟晶;丁坤;;甘肅酒泉風電出力特性分析[J];電力系統(tǒng)自動化;2010年17期

3 孫榮富;張濤;梁吉;;電網(wǎng)接納風電能力的評估及應(yīng)用[J];電力系統(tǒng)自動化;2011年04期

4 王彩霞;魯宗相;;風電功率預(yù)測信息在日前機組組合中的應(yīng)用[J];電力系統(tǒng)自動化;2011年07期

5 汪寧渤;王建東;何世恩;;酒泉風電跨區(qū)消納模式及其外送方案[J];電力系統(tǒng)自動化;2011年22期

6 王康;劉崇茹;韓民曉;蔡恒;;兼顧穩(wěn)定性與經(jīng)濟性的交直流系統(tǒng)直流落點選擇方法[J];電力系統(tǒng)自動化;2011年24期

7 張國華;張建華;楊京燕;王策;張印;段滿銀;;基于有向權(quán)重圖和復雜網(wǎng)絡(luò)理論的大型電力系統(tǒng)脆弱性評估[J];電力自動化設(shè)備;2009年04期

8 孫元章;吳俊;李國杰;;風力發(fā)電對電力系統(tǒng)的影響(英文)[J];電網(wǎng)技術(shù);2007年20期

9 聶宏展;呂盼;喬怡;姚秀萍;;基于熵權(quán)法的輸電網(wǎng)規(guī)劃方案模糊綜合評價[J];電網(wǎng)技術(shù);2009年11期

10 侯佑華;房大中;齊軍;李洪波;牛偉;楊濤;;大規(guī)模風電入網(wǎng)的有功功率波動特性分析及發(fā)電計劃仿真[J];電網(wǎng)技術(shù);2010年05期

，

本文編號：1403392