V2G模式下基于SaDE-BBO算法的有源配电网优化

为了解决大规模电动汽车入网难以实现个体调度以及集群调度存在“维数灾”的问题,建立基于车辆到电网(vehicle-to-grid,V2G)模式的有源配电网分层分区优化运行模型。其中,上层优化模型对电动汽车集控中心(electric vehicle agent,EVA)进行调度,优化各区域EVA的充放电功率并作为下层优化模型的输入;下层优化模型调整各调压方式。在优化算法方面,提出一种自适应差分进化-生物地理学优化(self-adaptive differential evolution-biogeography-based optimization,SaDE-BBO)算法,并在改进的IEEE 33节点配电系统中进行仿真分析。结果表明：在不同充电控制策略下,V2G模式与各调压方式的协调互动在降低各区域EVA运营成本、平抑负荷波动以及保证有源配电网的安全和经济运行方面优势显著,与其他优化算法相比,SaDE-BBO算法具有更优质的解和更好的收敛性。

Optimization of active distribution network based on SaDE-BBO algorithm in V2G mode

In order to solve the problem of difficulty in achieving individual scheduling for large-scale electric vehicles entering the grid and the existence of "dimensionality disaster" in cluster scheduling,a hierarchical and partitioned optimization operation model for active distribution network based on vehicle-to-grid(V2G) mode is established. The upper level optimization model schedules the electric vehicle agent (EVA) of electric vehicles, optimizes the charging and discharging power of EVA in each region,and serves as input for the lower level optimization model. Lower level optimization model adjusts various voltage regulation methods. In terms of optimization algorithm,a self adaptive differential evolution biogeography based optimization (SaDE-BBO) algorithm is proposed and simulated in the improved IEEE 33-node distribution system. The results show that under different charging control strategies,the coordinated interaction between V2G mode and various voltage regulation methods has significant advantages in reducing EVA operating costs in various regions,suppressing load fluctuations,and ensuring the safe and economic operation of active distribution networks. Compared with other optimization algorithms,the SaDE-BBO algorithm has better solutions and convergence.