Presented at the 6th International Symposium on Innovative Approaches in Smart Technologies (ISAS-WINTER-2022), Online, Turkey, Dec 08, 2022

SETSCI Conference Proceedings, 2022, 14, Page (s): 71-75 , https://doi.org/10.36287/setsci.5.2.016

Published Date: 22 December 2022    | 3026     16

Although numerous amounts of research have been done to find alternate sources of energy consumption and its applications. But in times of energy crisis and global warming urgency, it is needed to pace towards methods of less dependency on fossil fuels in the meantime fulfilling the on-grid demand. With increasing improvements in the EV sector, it is to be noted the effect they will have on Distributed Grids (DG) to cope with such high demand and as a consumer the relative effect of electricity tariff. In this paper, we will design a smart algorithm for V2G implementation such that the user will bear the minimum cost of charging and benefit from reselling at a higher price. It will work with different situational based cases to best decide upon multiple factors which include the SoC of the battery, User profile, and Tariff profile for multiple DISCO. As not much data is present to fully understand the user and system behavior, this paper uses Artificial Neural Networks (ANN) to develop and train scenarios for cases mentioned. Strategy used in this paper is flexible to modify after implementation in an EV and after a sufficient amount of data is collected. It will reprogram for better accuracies and energy management. Simulations are conducted in Simulink environment.  

Keywords - Electric Vehicles, Artificial Neural Network (ANN), Tariff, Energy management, MATLAB, Reinforcement Learning

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