本文選題：風電場　切入點：風速相關性　出處：《華北電力大學》2014年碩士論文　論文類型：學位論文

【摘要】：鑒于風電的隨機性與相關性等不確定性因素，如何在保證安全和可靠供電的前提下更加合理地利用風能，是一個亟待解決且十分必要的問題。電力系統(tǒng)的最優(yōu)潮流為上述問題提供了有效的解決途徑。針對有大型風電場接入的電力系統(tǒng)的潮流優(yōu)化問題，研究與探討了風電場分組等值建模、具有相關性的風電場風速建模、含風電場電力系統(tǒng)的最優(yōu)潮流模型與計算，提出了新的建模算法，為含風電場電力系統(tǒng)的潮流優(yōu)化研究提供了新的思路。 考慮風電場內(nèi)機組間尾流的相互影響，，提出了一種計及尾流效應的風電場等值建模方法。該方法定義了“尾流影響因子”以表征機組間尾流影響的程度，并以此作為風電場內(nèi)風電機組的分組依據(jù)。對風電機組進行分組及合并等值后，得到風電場的多機等值模型。算例結果顯示，與傳統(tǒng)等值方法相比，該模型更加準確地體現(xiàn)了風電場的功率輸出特性。 針對風電場間風速具有相關性的特點，提出了基于Copula函數(shù)的相關性風速建模方法。該方法利用Copula函數(shù)構建多風電場間風速的聯(lián)合概率分布，進而生成具有相關性的風速分布樣本空間，根據(jù)風電機組的出力特性可得到各風電場出力。算例表明，模型有效地描述了風電場間具有相關性的風速，得到的多元風速樣本可用于含風電場集群的電力系統(tǒng)的潮流優(yōu)化分析。 傳統(tǒng)的最優(yōu)潮流模型與算法多以確定性模型作為前提，然而風電的接入為電力系統(tǒng)帶來了不確定性因素�？紤]到這一問題，介紹了基于機會約束規(guī)劃的最優(yōu)潮流模型，并利用基于隨機模擬技術的粒子群優(yōu)化算法作為求解方法。 風電場的接入勢必對電力系統(tǒng)的可用輸電能力（ATC）產(chǎn)生影響，在上述模型的基礎上，建立了基于最優(yōu)潮流的含風電場電力系統(tǒng)的ATC計算模型，并對模型進行了求解。IEEE30節(jié)點和IEEE118節(jié)點測試系統(tǒng)算例表明，風速的隨機性和相關性、風電場所在的節(jié)點位置、機會約束規(guī)劃的置信水平等因素都對系統(tǒng)的可用輸電能力有一定影響，為含風電場電力系統(tǒng)的規(guī)劃與運行提供了參考和依據(jù)。
[Abstract]:In view of the uncertainties such as the randomness and correlation of wind power, how to make more rational use of wind energy on the premise of ensuring safe and reliable power supply, The optimal power flow of power system provides an effective way to solve the above problems. This paper studies and discusses wind farm grouping equivalent modeling, wind speed modeling with correlation, optimal power flow model and calculation of wind farm power system, and puts forward a new modeling algorithm. It provides a new idea for the study of power flow optimization in power system with wind farm. Considering the interaction between units in wind farm, a wind farm equivalent modeling method considering wake effect is proposed. In this method, the "wake influence factor" is defined to characterize the degree of wakes influence between units. After grouping and merging the wind turbine units, the multi-machine equivalent model of wind farm is obtained. The results show that compared with the traditional equivalent method, the wind power unit is divided into two groups. The model more accurately reflects the power output characteristics of the wind farm. According to the correlation of wind speed between wind farms, a method of wind speed modeling based on Copula function is proposed, which uses Copula function to construct the joint probability distribution of wind speed between multi-wind farms. Then the wind velocity distribution sample space with correlation is generated, and the wind farm output force can be obtained according to the wind turbine output characteristics. Numerical examples show that the model can effectively describe the wind speed with correlation between wind farms. The multivariate wind speed samples obtained can be used for power flow optimization analysis of power systems with wind farm clusters. Most of the traditional optimal power flow models and algorithms are based on deterministic models, but the access of wind power brings uncertainty to power system. Considering this problem, an optimal power flow model based on opportunistic constrained programming is introduced. The particle swarm optimization algorithm based on stochastic simulation technology is used as the solution. The connection of wind farm is bound to have an effect on the available transmission capacity of power system. Based on the above model, the ATC calculation model of power system with wind farm based on optimal power flow is established. An example of the test system for solving the. IEEE30 and IEEE118 nodes shows that the wind speed is randomness and correlation, and the location of the node in which the wind farm is located. The confidence level of the chance constrained programming has a certain influence on the available transmission capacity of the system, which provides a reference and basis for the planning and operation of the power system with wind farm.
【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】：TM614

【參考文獻】

相關期刊論文 前10條

1 嚴干貴;李鴻博;穆鋼;崔楊;劉玉;;基于等效風速的風電場等值建模[J];東北電力大學學報;2011年03期

2 嚴干貴;王茂春;穆鋼;崔楊;周志強;戴武昌;;雙饋異步風力發(fā)電機組聯(lián)網(wǎng)運行建模及其無功靜態(tài)調(diào)節(jié)能力研究[J];電工技術學報;2008年07期

3 江岳文;陳沖;溫步瀛;;含風電場的電力系統(tǒng)機組組合問題隨機模擬粒子群算法[J];電工技術學報;2009年06期

4 黃梅;萬航羽;;在動態(tài)仿真中風電場模型的簡化[J];電工技術學報;2009年09期

5 米增強;蘇勛文;楊奇遜;王毅;吳濤;;風電場動態(tài)等值模型的多機表征方法[J];電工技術學報;2010年05期

6 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

7 米增強;蘇勛文;余洋;王毅;吳濤;;雙饋機組風電場動態(tài)等效模型研究[J];電力系統(tǒng)自動化;2010年17期

8 范榮奇;陳金富;段獻忠;李慧杰;姚美齊;;風速相關性對概率潮流計算的影響分析[J];電力系統(tǒng)自動化;2011年04期

9 曹娜;于群;;風速波動情況下并網(wǎng)風電場內(nèi)風電機組分組方法[J];電力系統(tǒng)自動化;2012年02期

10 俞俊霞,趙波;基于改進粒子群優(yōu)化算法的最優(yōu)潮流計算[J];電力系統(tǒng)及其自動化學報;2005年04期

本文編號：1637694