EVs charging and discharging model consisted of EV users behaviour

Abstract

This paper proposes a new approach for forecasting the coordinated Electric Vehicles (EVs) charging and discharging that minimizes the EVs charging cost, based on the day-ahead electricity price (DAEP) subject to the EVs state of charge (SOC) limits, the EVs maximum power charger, the EVs batteries full charging at the end of the charging period. Besides, the EVs initial state of charge (SOC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) has been calculated based on the EVs daily driving mileage, while Latin Hypercube Sampling (LHS) has been applied to deal with the EVs arrival, departure time and SOC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> uncertainties. The proposed optimal strategy enables EVs users to make a profit of 14.79€ while they need 2.17€ to charge their EVs in the uncoordinated scenario. Furthermore, the comparison between the real and the estimated results show that the charging cost based on the real SOC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> values is 2.88% and 27% higher than the charging cost based on the estimated SOC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> values for coordinated and uncoordinated scenarios respectively.