含光伏电站和蓄电池储能系统的主动配电系统状态估计

间歇性和随机波动性的分布式新能源大规模接入，储能系统与电动汽车的随机充放电以及智能量测装置的量测误差等使得主动配电系统的态势感知面临诸多挑战。考虑到光照强度、温度及电池荷电状态等影响因素，文中搭建了含光伏发电电源、配电网和蓄电池储能负荷的主动配电系统状态估计模型。首先，基于光伏五参数模型和电池内阻模型，推导出光伏系统和蓄电池储能系统的量测函数及雅可比矩阵。然后，结合配电网的状态估计模型，将电气量和非电气量统一标幺化后，对主动配电系统进行状态估计及不良数据处理。最后，在IEEE 33节点系统下进行了仿真验证。仿真结果表明，与光伏系统和蓄电池储能系统作为PQ节点的模型相比，所提方法扩展了与光伏系统和蓄电池储能系统相关的状态估计和不良数据处理能力，且提高了配电网的状态估计精度。

State Estimation for Active Distribution Systems Incorporating Photovoltaic Plant and Battery Energy Storage System

The large-scale integration of distributed energy resources with intermittent and stochastic volatility, the random charging and discharging of energy storage system and electric vehicles, and measurement errors of the intelligent measuring devices make the situational awareness of active distribution systems face some challenges. Considering the influencing factors such as the solar irradiance, the temperature, and the state of charge(SOC)of batteries, a state estimation model for the active distribution systems is developed including integrated photovoltaic power generation systems, the distribution network, and battery energy storage systems. Firstly, based on the photovoltaic five-parameter model and the battery internal resistance model, the measurement functions and the Jacobian matrix of the photovoltaic systems and battery energy storage systems are derived. Then, combined with the state estimation model of the distribution network, the electrical quantity and non-electrical quantity are uniformly standardized, and the state estimation and bad data processing of the active distribution systems are performed. Finally, the simulation is carried out in IEEE 33-node system. The simulation results show that, compared with the conventional model which only takes the photovoltaic system and the battery energy storage system as PQ nodes, the proposed method significantly expands the scopes and capabilities of state estimation and bad data processing in the photovoltaic system and battery energy storage system, and improves the accuracy of the state estimation of the distribution network.