Publication

Abstract : This paper proposes an Artificial Neural Network based Energy Management System for the economic operation of a Photovoltaic powered Electric Vehicle Charging Station with battery backup and Vehicle to Grid support. The Charging Station Battery and Parking Lot EVs support the EVCS to operate economically by charging during off-peak hours and discharging during peak hours and also augment the reliability of the EVCS by serving as a backup supply during Stand-Alone mode. The proposed method reduces the operating cost of the PV powered EVCS while ensuring that proper power balance is maintained between the various components: PV, Electric Vehicles, CSB, the PLV ready for V2G operation and the grid. This method relies on the information from real time data such as the connection status of the EV and PLV, State of Charge of the EV, CSB and PLV, the available PV power and the Time of Use tariff based on which the resources are allocated intelligently. For implementing this control action, a Supervised Feed Forward ANN model is developed which is trained by means of a set of data using various algorithms and it is observed that the Bayesian Regularisation algorithm outperformed the other algorithms in terms of prediction accuracy. With the proposed ANN-based EMS the energy utilized by the EVCS from the grid exhibited a reduction of 28% as compared to the system without EMS throughout the course of a simulation lasting 24 hours. © 2023 Taylor & Francis Group, LLC.