A hybrid heuristic-based multi-objective strategy for optimized charging and discharging in plug-in EV energy management.

The paper will deal with the problem of optimal charging and discharging coordination of Plug-in Electric Vehicles in a local multi-energy system that will be conducted under uncertain conditions. The current practices are usually based on simplified assumptions and fail to coordinate various energy sources and resources effectively, which restricts their usage in real-life conditions. In order to eliminate these constraints, a hybrid optimization model that involves Rider Optimization and Osprey Optimization integration is suggested. The model developed will take into account both the economic and environmental goals, such as the cost of operation, profitability of the system, and minimization of carbon emission. The lack of certainty in the behavior and generation of the renewable is factored in by using probabilistic modeling, which allows the system to be more realistic. The suggested algorithm constraints global exploration and local exploitation to optimally search the energy scheduling strategies. The outcomes of the simulation display the assumption that the proposed method can deliver the enhanced performance of the operational level as it successfully organizes the activities of distributed energy resources and electric vehicle charging. The model offers computationally effective and flexible framework to be used in practical energy management applications in local multi-energy systems.