考慮多風(fēng)電場相關(guān)性的場景概率潮流計算及其在無功優(yōu)化中的應(yīng)用
發(fā)布時間：2018-08-21 07:15
【摘要】：隨著能源危機(jī)和環(huán)境污染問題日益突出,新能源的開發(fā)利用受到了越來越多的關(guān)注。其中以風(fēng)能的開發(fā)利用技術(shù)最為成熟,并且已有大規(guī)模風(fēng)電場并網(wǎng)發(fā)電。然而,受風(fēng)能資源的影響,風(fēng)電場出力具有很強(qiáng)隨機(jī)性、間歇性和互相關(guān)性特點,這使得風(fēng)電并網(wǎng)后電力系統(tǒng)的潮流分布、電壓穩(wěn)定等各方面都受到了較大影響。本文針對相鄰風(fēng)電場出力的隨機(jī)性和相關(guān)性特點,通過建立其場景概率模型分析風(fēng)電并網(wǎng)后對電力系統(tǒng)的影響,并應(yīng)用到無功優(yōu)化研究中。首先,本文基于聚類分析和Copula函數(shù)提出了一種考慮多風(fēng)電場相關(guān)性的場景概率潮流計算方法。聚類分析能根據(jù)數(shù)據(jù)本身的近似特征對數(shù)據(jù)進(jìn)行分類,Copula函數(shù)可以建立復(fù)雜相關(guān)數(shù)據(jù)的概率模型。本文結(jié)合兩者優(yōu)點同時考慮毗鄰風(fēng)電場出力間相關(guān)性復(fù)雜多變的特點,建立了多風(fēng)電場出力的場景概率模型,并利用基于拉丁超立方采樣的概率潮流計算方法對系統(tǒng)各場景運行狀態(tài)進(jìn)行了概率評估。為驗證方法的合理性和有效性,將澳大利亞兩個實際相鄰風(fēng)電場接入IEEE30節(jié)點系統(tǒng)中進(jìn)行測試分析,仿真結(jié)果表明本文所提方法和模型能夠更好地描述多風(fēng)電場出力間的相關(guān)性,得到更準(zhǔn)確的概率潮流計算結(jié)果。而后,將本文所提含多風(fēng)電場的場景概率潮流計算方法應(yīng)用到電力系統(tǒng)無功優(yōu)化研究中。建立了以系統(tǒng)網(wǎng)損期望、發(fā)電機(jī)無功偏差期望和節(jié)點電壓偏差期望加權(quán)值最小為目標(biāo)函數(shù)的概率無功優(yōu)化模型,采用粒子群算法對模型進(jìn)行求解,得到各風(fēng)電出力場景下的最優(yōu)無功控制策略。在含多風(fēng)電場的IEEE30節(jié)點系統(tǒng)中對所建概率無功優(yōu)化模型進(jìn)行仿真測試,并與確定性的場景無功優(yōu)化模型進(jìn)行對比分析,結(jié)果表明本文所提方法能提高無功控制策略對風(fēng)電出力隨機(jī)變化的適應(yīng)性,保障系統(tǒng)以最大概率運行在最優(yōu)條件下。
[Abstract]:With the problem of energy crisis and environmental pollution becoming more and more prominent, more and more attention has been paid to the development and utilization of new energy. Among them, wind energy development and utilization technology is the most mature, and large-scale wind farms have been connected to grid power generation. However, due to the influence of wind energy resources, wind farm output has the characteristics of strong randomness, intermittence and interrelation, which makes the power flow distribution and voltage stability of the power system greatly affected after wind power is connected to the grid. In view of the randomness and relativity of adjacent wind farms, this paper analyzes the influence of wind power on power system by setting up its scenario probability model, and applies it to the research of reactive power optimization. Firstly, based on clustering analysis and Copula function, a scenario probabilistic power flow calculation method considering the correlation of multi-wind farms is proposed. Clustering analysis can classify the data according to the approximate characteristics of the data itself and the Copula function can establish the probability model of the complex related data. Combining the advantages of the two methods and considering the complex and changeable characteristics of the correlation between the output forces of adjacent wind farms, a scenario probability model of multi-wind farm output is established in this paper. The probabilistic power flow calculation method based on Latin hypercube sampling is used to evaluate the running state of each scenario of the system. In order to verify the rationality and validity of the method, two practical adjacent wind farms in Australia are connected to the IEEE30 node system for testing and analysis. The simulation results show that the proposed method and model can better describe the correlation between the output of multi-wind farms. A more accurate calculation result of probabilistic power flow is obtained. Then, the scenario probabilistic power flow calculation method proposed in this paper is applied to the reactive power optimization of power system. A probabilistic reactive power optimization model with the minimum weighted value of network loss expectation, generator reactive power deviation expectation and node voltage deviation expectation as objective function is established. Particle swarm optimization algorithm is used to solve the model. The optimal reactive power control strategy for each wind power output scenario is obtained. The probabilistic reactive power optimization model is simulated and tested in the IEEE30 node system with multiple wind farms, and compared with the deterministic scenario reactive power optimization model. The results show that the proposed method can improve the adaptability of reactive power control strategy to the random variation of wind power output and ensure that the system runs under the optimal conditions with the maximum probability.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM744;TM614
[Abstract]:With the problem of energy crisis and environmental pollution becoming more and more prominent, more and more attention has been paid to the development and utilization of new energy. Among them, wind energy development and utilization technology is the most mature, and large-scale wind farms have been connected to grid power generation. However, due to the influence of wind energy resources, wind farm output has the characteristics of strong randomness, intermittence and interrelation, which makes the power flow distribution and voltage stability of the power system greatly affected after wind power is connected to the grid. In view of the randomness and relativity of adjacent wind farms, this paper analyzes the influence of wind power on power system by setting up its scenario probability model, and applies it to the research of reactive power optimization. Firstly, based on clustering analysis and Copula function, a scenario probabilistic power flow calculation method considering the correlation of multi-wind farms is proposed. Clustering analysis can classify the data according to the approximate characteristics of the data itself and the Copula function can establish the probability model of the complex related data. Combining the advantages of the two methods and considering the complex and changeable characteristics of the correlation between the output forces of adjacent wind farms, a scenario probability model of multi-wind farm output is established in this paper. The probabilistic power flow calculation method based on Latin hypercube sampling is used to evaluate the running state of each scenario of the system. In order to verify the rationality and validity of the method, two practical adjacent wind farms in Australia are connected to the IEEE30 node system for testing and analysis. The simulation results show that the proposed method and model can better describe the correlation between the output of multi-wind farms. A more accurate calculation result of probabilistic power flow is obtained. Then, the scenario probabilistic power flow calculation method proposed in this paper is applied to the reactive power optimization of power system. A probabilistic reactive power optimization model with the minimum weighted value of network loss expectation, generator reactive power deviation expectation and node voltage deviation expectation as objective function is established. Particle swarm optimization algorithm is used to solve the model. The optimal reactive power control strategy for each wind power output scenario is obtained. The probabilistic reactive power optimization model is simulated and tested in the IEEE30 node system with multiple wind farms, and compared with the deterministic scenario reactive power optimization model. The results show that the proposed method can improve the adaptability of reactive power control strategy to the random variation of wind power output and ensure that the system runs under the optimal conditions with the maximum probability.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM744;TM614
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相關(guān)期刊論文 前10條
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