Environmental benefits lie in halting direct air pollution and reducing greenhouse gas emissions. In contrast to thermal vehicles, electric vehicles (EV) have zero tailpipe emissions, but their contribution in reducing global air pollution is highly dependent on the energy source they have been charged with. Thus, the energy system depicted in this paper is a photovoltaic (PV)-powered EV charging station based on a DC microgrid and includes stationary storage and public grid connection as power source backups. The goal is to identify the preliminary requirements and feasibility conditions for PV-powered EV charging stations leading to PV benefits growth. Simulation results of different scenarios prove that slow charging with long park time could increase PV benefits for EVs and may reduce the charging price, therefore, EV users should be more willing to stay at charging stations. Whereas, for fast charging, EV users should accept the high charging price since it depends on the public energy grid. Energy system distribution and EV’s energy distribution are well presented.

Satisfying the increased power demand of electric vehicles (EVs) charged by clean energy sources will become an important aspect that impacts the sustainability and the carbon emissions of the smart grid. A photovoltaic (PV)-powered charging station (PVCS) formed by PV modules and a stationary storage system with a public grid connection can provide cost-efficient and reliable charging strategies for EV batteries. Moreover, the utilization of vehicle-to-grid (V2G) service is a promising solution, as EVs spend most of their time idle in charging stations. As a result, V2G services have the potential to provide advantages to both public grid operators and EV users. In this paper, an energy management algorithm of a PVCS formulated with mixed-integer linear programming is presented to minimize the total energy cost of the participation of EV users in V2G service. Simulation results demonstrate that the proposed optimization method satisfies EV user demands while providing V2G service and highlights the benefits of the V2G service where the determined costs of the proposed algorithm perform significantly better compared to the baseline scenario (simulation without optimization).

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Electric vehicles (EVs) are expanding quickly and widely, and, therefore, EVs can participate in reducing direct greenhouse gas emissions. The intelligent infrastructure for recharging EVs, which is microgrid-based, includes photovoltaic (PV) sources, stationary storage, and a grid connection as power sources. In this article, the energy cost optimization problem is studied, taking into account the intermittent arrival and departure of EVs. A mixed-integer linear programming is formulated as an optimization problem in a real-time operation to minimize the total energy cost, taking into consideration the physical limitations of the system. The interaction with the human-machine interface provides EV data in real-time operation, and the prediction only communicates the PV prediction profile provided by the national meteorological institute in France. The optimization is executed at each EV arrival, with the actualized data in the DC microgrid. Simulation and real-time experimental results of different meteorological conditions show that the EV user demands are satisfied, proving the feasibility of the proposed optimization problem for real-time power management.