A GBDT-BCO Technique based Cost Reduction and Energy Management between Electric Vehicle and Electricity Distribution System.

This work presents an energy management system for the performance of electric vehicle charging station (EVCS) based on the distribution system (DS) with a hybrid approach. The proposed hybrid strategy is the consolidation of the gradient boosting decision tree (GBDT) algorithm and the border collie optimization (BCO), thus it is called GBDT-BCO. The major contribution of this manuscript is cost reduction and energy management interaction among electric vehicles and distribution system. The proposed system consists of electric vehicles (EV) with bi-directional energy exchange through charging and grid-to-vehicle and vehicle-to-household operating modes, energy storage systems (ESS) through peak cutting, photovoltaic (PV) favors the sale of energy to the grid, which are deemed the energy management system (EMS). Power is predicted by GBDT approach. For diminishing the operating cost of power, the proposed system is incorporated with photovoltaic, including energy storage systems, and the cost is reduced by optimizing the BCO. System stability is improved through the use of time-based predictions (e.g. usage time). The proposed GBDT-BCO approach reduces the cost of the system and maintains the needed power output during the integration of EVCS with distribution system. The efficiency of GBDT-BCO approach is likened to existing approaches and assessed with dissimilar test cases and time periods. The experimental results show that the proposed GBDT-BCO approach offers best solutions likened to existing process and increases the advantages of the distribution system for the entire cases assumed. The efficiency of the proposed and existing techniques is also analyzed. GA’s efficiency achieved is 47%, PSO reaches 56%, GA-PSO reaches 77%, and the proposed technique reaches 89%. [ABSTRACT FROM AUTHOR]