A Novel Renewable Powered Stand-alone Electric Vehicle Parking Lot Model

Abstract

The depletion of fossil fuels, gradually increasing environmental pollution and global warming lead to the electrification of the transport sector because the transport sector is one of the main reasons for the rising environmental concerns. However, the increasing number of electric vehicles (EVs) entering the power grid upsurges the electricity demand from the grid as it requires a massive amount of electricity to fulfill its charging needs. The existing electric grid and EV infrastructure is not capable enough to support increasing penetration of EVs, therefore the anticipated increase in the EV count brings new challenges regarding the EV charging needs. Most of the existing works have shared a common drawback of charging EVs by electricity provided from the electric grid. Thus, the primary purpose of utilizing EVs as a solution to decrease pollution is emitted due to the shifting of carbon emission from transportation to the electricity generation sector. This necessitates the usage of renewable energy resources, for instance, solar and wind energy to charge the electric vehicles to attain the environmental and economic benefits of EVs. Although renewable energy seems to be a promising solution due to its instability it may lead to an insufficient energy supply and cause an incomplete or interrupted charging of EVs. To address the aforementioned problems, an integer linear programming-based smart charge management system for the profit maximization of stand-alone electric vehicle parking lot has been developed in this research work. This research work focuses on two smart strategies i.e. the allocation of renewable energy resources to the EV parking lot to minimize the service level objective violations while reducing the total renewable energy expenditure and carbon emission and to increase the number of EVs being charged at each time slot. At first, multiple deep learning techniques have been used to predict the tail distribution/complementary cumulative distribution function of all the renewable energy sources at each time step in the next period. Secondly, the demand of each EV area is calculated by assuming the different battery capacities of all the EVs entering the parking lot. To reduce the service level objective (SLO) violations the problem is articulated in such a way that the EVs with the same service level objective is assigned to the same EV area, and each EV area is being supplied by the renewable energy generators that are producing the same amount of energy required by the EV area with the same probability as the SLO level at each time slot. The energy storagex system is incorporated in the parking lot that stores the excess energy that is later being used to charge EVs in case of insufficient renewable energy. The experimental results show that our proposed charging scheme has satisfied the electric vehicle demand for all the areas with minimum service level objective violations and minimum possible power consumption cost and carbon emission. The results show that due to higher prediction accuracy the results obtained from predicted data have a similar effect on cost and carbon emission as actual data of renewable power generators.