Abstract
The increasing electric vehicle fleet requires an upgrade and expansion of the available charging infrastructure. The uncontrolled charging cycles greatly affect the electric grid, and for this reason, renewable energy sources and battery storage are getting incorporated into a hybrid charging station solution. Adding a renewable source and a battery to the charging station can help to “buffer” the power required from the grid, thus avoiding peaks and related grid constraints. To date, the origin of the energy coming from the battery has not been traced. In this paper, a solution of the hybrid electric vehicle charging station coupled with the small-scale photovoltaic system and battery energy storage is proposed to eliminate the adverse effects of uncontrolled electric vehicle charging, with the exact calculation of renewable energy share coming from energy stored in the battery. The methodology for the multicriteria optimization of the charging/discharging schedule of a battery and electric vehicle charging level is based on multi-attribute utility theory. The optimization criteria include the minimization of charging costs, maximization of renewable energy (from both the solar plant and the battery), and the minimization of battery degradation. The problem is solved using a genetic algorithm optimization procedure adapted to the multicriteria optimization function. The methodology is tested on an illustrative example, and it is proven that the decision-maker’s preferences greatly affects the choice of the optimal strategy and the optimal battery capacity.
Highlights
Environmental pollution, climate change, and global energy policy lead to the accelerating growth of electric vehicles (EV) [1,2,3]
As one of the desired criteria is the maximization of energy from renewable sources, it is necessary to develop a methodology for monitoring the origin of energy
Because the battery power can be divided into different sources, EV power contains four components (Equation (7)): power from the PV panel (PS ), power from the public grid (PG ), battery power originating from the PV panel (PBS ), and battery power originating from the grid (PBG )
Summary
Environmental pollution, climate change, and global energy policy lead to the accelerating growth of electric vehicles (EV) [1,2,3]. In [22,23], a genetic algorithm was employed to solve the optimization model for the design of an EV fast-charging station, optimizing the number of chargers, installed power of renewable energies and storage, and minimizing the imported power from the grid. Another disadvantage of the current research methodologies is the single-criterion optimization instead of the multicriteria approach Different factors, such as operational cost, customer satisfaction, load loss, and profit for charging station owners, are considered only in the objective function. The simultaneous optimization of charging/discharging BES and EV charging power; the implementation of energy tracking methodology that monitors the origin of energy from and to the battery; the multiobjective optimization of different conflicting criteria This problem is solved using the genetic algorithm optimization procedure adapted to the multicriteria optimization function.
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