计及电动汽车与温控负荷需求响应的分层能源系统优化调度

针对电动汽车(electric vehicle,EV)大规模接入电网对电力系统带来的影响,构建了一种基于电动汽车及温控负荷需求响应的分层能源系统管理框架。受到激励的电动汽车集群(electric vehicles, EVs)和温控负荷集群(temperature-controlled load clusters, TCLs)能够快速响应负荷聚合商的调度策略,以此减少大量柔性负荷并网对电网产生的冲击。在基于卷积神经网络和长短期记忆网络混合模型对负荷进行预测的基础上,假设负荷聚合商可通过调度可控柔性负荷来减小实际负荷与预测负荷的误差,并根据制定的负荷调度策略与电力运营商之间进行点对点(peer to peer, P2P)电力交易,运用分布式优化方法求解双方可获得的最大利益。对于P2P交易以后剩余的能源需求,建立了系统运行成本、碳排放和风能溢出的多目标优化模型,采用集中优化的二代非支配排序遗传算法(non dominated sorting genetic algorithm-II, NSGA-Ⅱ)求解该模型的帕累托前沿,并在IEEE 30节点系统进行了算例验证。仿真结果表明,在所提出的能源优化调度策略下既能满足电动汽车和温控负荷的功率需求,也给电力系统带来了良好的经济效益和环境效益。

Optimal Scheduling of Hierarchical Energy Systems with Electric Vehicles and Temperature-Controlled Load Demand Response

A hierarchical energy system management framework based on the demand response of electric vehicles (EVs) and temperature-controlled loads (TCLs) is developed to address the impact of large-scale EVs on the power system. Stimulated EV clusters and TCL clusters can quickly respond to the scheduling strategies of load aggregators to reduce the impact on the grid caused by the large number of flexible loads connected to the grid. Firstly, a hybrid model of convolutional neural network and long-and short-term memory network is used to predict each part of the load, and the load aggregator dispatches controllable flexible loads to maximize the fit of the predicted load profile. The load aggregator performs peer to peer (P2P) power trading with the power operator according to the current scheduling strategy and applies distributed optimization to solve the maximum benefit for both parties. For the remaining energy demand after local energy trading, a multi-objective optimization model for system operating cost, carbon emission, and wind energy spillover is considered. The Pareto frontier of this model is solved using NSGA-II with centralized optimization and verified by arithmetic cases in the IEEE 30-node system. The simulation results show that the proposed optimal energy dispatch strategy can not only meet the power requirements of EVs and TCLs, but also bring good economic and environmental benefits to the power system.