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考虑概率潮流的分布式电源优化配置
张忠伟1, 王金玉1, 张建波1, 杨洋2
1.东北石油大学 电气信息工程学院, 黑龙江 大庆 163318;2.中国石油集团电能公司 中油电能电力技术服务公司, 黑龙江 大庆 163453
摘要:
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。
关键词:  分布式电源  电动汽车  基于成功历史的自适应差分进化  迹变换  径向基神经网络
DOI:10.11835/j.issn.1000-582X.2018.12.010
分类号:TM712
基金项目:国家自然科学基金资助项目(51574087)。
Optimal configuration of distributed generation considering probability power flow
ZHANG Zhongwei1, WANG Jinyu1, ZHANG Jianbo1, YANG Yang2
1.School of Electrical Engineering & Information, Northeast Petroleum University, Daqing 163318, Heilongjiang, P. R. China;2.Electric Technical Services Company, China Petroleum Electric Energy Co., Ltd, Daqing 163453, Heilongjiang, P. R. China
Abstract:
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.
Key words:  distributed power  electric vehicle  adaptive differential evolution based on success history  unscented transformation  RBF neural network
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