考虑多风电场相关性的场景概率潮流计算及其在无功优化中的应用

发布时间：2018-08-21 07:15

【摘要】：随着能源危机和环境污染问题日益突出,新能源的开发利用受到了越来越多的关注。其中以风能的开发利用技术最为成熟,并且已有大规模风电场并网发电。然而,受风能资源的影响,风电场出力具有很强随机性、间歇性和互相关性特点,这使得风电并网后电力系统的潮流分布、电压稳定等各方面都受到了较大影响。本文针对相邻风电场出力的随机性和相关性特点,通过建立其场景概率模型分析风电并网后对电力系统的影响,并应用到无功优化研究中。首先,本文基于聚类分析和Copula函数提出了一种考虑多风电场相关性的场景概率潮流计算方法。聚类分析能根据数据本身的近似特征对数据进行分类,Copula函数可以建立复杂相关数据的概率模型。本文结合两者优点同时考虑毗邻风电场出力间相关性复杂多变的特点,建立了多风电场出力的场景概率模型,并利用基于拉丁超立方采样的概率潮流计算方法对系统各场景运行状态进行了概率评估。为验证方法的合理性和有效性,将澳大利亚两个实际相邻风电场接入IEEE30节点系统中进行测试分析,仿真结果表明本文所提方法和模型能够更好地描述多风电场出力间的相关性,得到更准确的概率潮流计算结果。而后,将本文所提含多风电场的场景概率潮流计算方法应用到电力系统无功优化研究中。建立了以系统网损期望、发电机无功偏差期望和节点电压偏差期望加权值最小为目标函数的概率无功优化模型,采用粒子群算法对模型进行求解,得到各风电出力场景下的最优无功控制策略。在含多风电场的IEEE30节点系统中对所建概率无功优化模型进行仿真测试,并与确定性的场景无功优化模型进行对比分析,结果表明本文所提方法能提高无功控制策略对风电出力随机变化的适应性,保障系统以最大概率运行在最优条件下。
[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.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM744;TM614

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