计及电动汽车和光伏不确定性的主动配电网量测优化配置

大规模电动汽车及分布式电源的发电并网使得配电网态势感知结果需要考虑更多的不确定因素。在利用动态概率密度函数表征电动汽车充/放电随机性和光伏系统出力间歇性的基础之上,基于加权最小二乘法方法,建立了含网络节点注入功率不确定性的主动配电网量测(数据采集点)优化配置模型,并采用一种自适应协方差矩阵进化策略对模型进行优化求解,从而可以得到在给定系统状态量估计误差允许精度下的数据采集点最优配置方案。通过某一电网算例计算分析,结果验证了所提模型和方法的可行性和有效性,可为主动配电网安全评估提供支撑。

Optimal Meter Placement for Active Distribution Network Considering Uncertainties of Plug-in Electric Vehicles and Photovoltaic Systems

As large-scale of electric vehicle(EV)and distributed generator(DG)such as wind and solar energy is being integrated into distribution networks, the situation awareness(SA)program needs to consider more uncertainties. A new methodology of meter placement for SA in the active distribution network considering uncertainty of correlated input variables is proposed based on dynamic probability density function to describe the uncertainty of EV charging/discharging demands and photovoltaic system outputs. The SA analysis is based on state estimation method. Moreover, the covariance matrix adaptation evolution strategy is used to optimize the proposed methodology, and the algorithm can get the desired results. Simulation is conducted on an active distribution network, and the results validate the feasibility and effectiveness of the proposed methodology. It can provide theoretical support for the safety assessment of active distribution networks.