考虑概率潮流的分布式电源优化配置

为了解决电动汽车和分布式电源并网为电力系统带来的较强的随机性、间歇性和相关性的问题，以分布式电源和电动汽车的概率模型为基础，建立了以分布式电源总费用、供电可靠性和有功网损为目标函数的优化配置模型，将概率潮流计算嵌入基于成功历史的自适应参数差分进化算法求解目标函数。采用无迹变换利用输入随机变量的均值和协方差近似描述系统状态变量统计特性，直接方便地处理具有不确定性的随机变量。然后利用径向基神经网络求解功率方程，避免了计算雅可比矩阵和偏导，减少了算法运行时间。最后采用基于成功历史的自适应参数差分进化法并行计算多目标函数。通过IEEE33节点配电系统进行仿真，验证了该方法的有效性和高效性，节约了规划成本。

Optimal configuration of distributed generation considering probability power flow

In order to solve the problem caused by paralleling operation of electric vehicles and distributed power grids, which brings strong randomness, intermittent and correlation to power system. Based on the probabilistic model of distributed power supply and electric vehicle, an optimal configuration model taking distributed power supply total cost, power supply reliability and active network loss as the objective function was established, embeding the probabilistic power flow calculation into the adaptive parameter difference based on the success history. The evolutionary algorithm was used to solve the objective function. The unscented transformation was used to describe the statistical properties of the system state variables by using the mean and covariance of the input random variables to directly deal with the random variables with uncertainty. Then RBF neural network was used to solve the power equation, which avoided calculating the Jacobian matrix and partial derivative, reducing the running time of the algorithm. Finally, the multi-objective function was calculated in parallel using the adaptive parameter differential evolution method based on the success history. The simulation of the IEEE33 node power distribution system verified the effectiveness and efficiency of the method and the method can save planning costs.