Optimized Probabilistic PHEVs Demand Management in the Context of Energy Hubs

This paper develops a new framework to coordinate the charging process of plug-in hybrid electric vehicles (PHEVs) in the context of energy hubs. Attempts are focused not only on the impact evaluation of PHEVs on the system technical performance, but also customers' preferences on the charging patterns. PHEVs coordinator agent (PCA) is proposed to run a multiobjective optimization (MO) framework for the optimal charging patterns of PHEVs from the vehicle owners' and system operator's viewpoint. Wind energy utilization for PHEVs charging, energy costs at the hub input layer, and customers' convenience are all taken into account as the optimization objectives. Due to its promising performance in dealing with MO problems, the multiobjective particle swarm optimization method is employed to evaluate the optimization problem. The 2-point estimation method is used to model the existence uncertainties. The proposed framework is applied to the modified IEEE 34-node test system, and the obtained results demonstrate the efficiency and applicability of the proposed approach in real-world scenarios.