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SETSCI - Volume 5(2) (2022)
ISAS-WINTER-2022 - 6th International Symposium on Innovative Approaches in Smart Technologies, Online, Turkey, Dec 08, 2022

Smart (V2G) Power Transfer on Scenario Based Strategies Trained Through Artificial Neural Networks for Maximum Tariff Saving and Energy Management in MATLAB Simulink
Shariq Ahmed Shamsi1*, Omer Cihan Kivanc2
1Istanbul Okan University, Istanbul, Turkey
2Istanbul Okan University, Istanbul, Turkey
* Corresponding author:
Published Date: 2022-12-22   |   Page (s): 71-75   |    74     5

ABSTRACT 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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