考虑风电场相关性的含风电电力系统随机潮流分析

Analysis on Probabilistic Load Flow in Power Gird Integrated With Wind Farms Considering Correlativity Among Different Wind Farms

地理位置接近的不同风电场的风速具有较强的相关性,这将会影响含风电电力系统随机潮流分析评估的结果。首先根据多风电场的风速历史数据建立了考虑不同风电场相关性的风速离散化联合概率模型,在此基础上结合半不变量和级数展开方法,提出考虑风电场相关性的含风电电力系统随机潮流分析评估模型,得到各节点电压的分布和越限概率等指标,同时该模型也可以考虑风电场有功无功出力的相关性。对某实际电力系统的算例验证结果表明,所提算法较不考虑风电场相关性的随机潮流结果的精度提高了很多;与蒙特卡洛方法计算结果相比误差较小,而计算速度大大提高。该算法可以快速有效地得到含风电电力系统的随机潮流评估指标,并针对所得结果提出相应的无功补偿方案,具有较好的工程应用价值。

Different wind farms close in geographical location possess strong correlativity among wind speeds and it influences the analysis and evaluation results of probabilistic load flow in power grid integrated with wind farms. Firstly, based on historical data of wind speed of multi wind farms a discretized joint probability model of wind speed, in which the correlativity among different wind farms is taken into account, is established; on this basis, combining with semi invariant and series expansion method and considering the correlativity among different wind farms a model to analyze and evaluate probabilistic load flow in power grid integrated with wind farms is proposed to obtain such indices as the distribution of nodal voltages and the out-of-limit probability of branch power flow, meanwhile in the proposed model the correlativity among active and reactive outputs of wind farms can be considered. Taking a certain power grid as the case, the verification results show that the evaluation results of probabilistic load flow by the proposed method are much more accurate than the evaluation results, in which the correlativity among wind farms are not taken into account; comparing with the results by Monte Carlo method, there is smaller computation error in the obtained results while the computation speed is increased greatly. Using the proposed algorithm, the evaluation indices of probabilistic load flow in power grid integrated with wind farms can be obtained rapidly and effectively, and the reactive compensation scheme corresponding to the obtained out-of-limit probability can be put forward.