## 1 Introduction
### Literature Review  
Recent studies have aimed to design microgrids for EV charging. The authors of [11] have proposed mixed-integer linear programming (MILP) for an EV charging station integrated into a DC microgrid to determine the optimal operation planning. They have focused on optimizing the daily operational costs based on forecasting PV production and EV operation. A hybrid optimization problem for energy storage management has been proposed in [12] to minimize the EV charging cost in a PV-integrated EV charging station using time-of-use wholesale electricity pricing. The authors in [13] have presented meta-heuristic methods, such as binary particle swarm optimization and binary grey wolf optimization. They have studied an optimal charging coordination strategy for a random arrival of plug-in EVs. A MILP optimization has been proposed in [14] to minimize the microgrid operation cost by having an aggregated EV charging station for an islanded microgrid and in [15] to minimize the energy generation cost and load shedding considering various constraints in a microgrid that integrates battery EV charging stations. A heuristic operation problem has been proposed in [2] for a commercial building microgrid that integrates EVs and a PV system to study a strategy to acquire data in real-time rather than forecasting EV charging demand or PV production. A genetic algorithm optimization has been studied in [16] for the multi-criteria optimization problem to minimize the charging costs of the EV, maximize the use of PV and the storage device and minimize the degradation of the storage device. A MILP optimization has been proposed in [17] to solve the day-ahead optimization problem and to find the optimal scheduling and operation of a prosumer who owns renewable energy sources and a plugged-in EV. They have used a feed-forward artificial neural network for the weather prediction module in the energy management system. Linear programming and quadratic programming optimization problems have been addressed in [18] to minimize the total operating costs for building a microgrid that integrates a heterogeneous fleet of EVs. A multi-objective scheduling optimization problem based on genetic algorithms has been presented in [19] for microgrids including EVs to reduce grid loss and charging costs considering various constraints of the microgrid sources and EV charging characteristics. The authors in [20] have presented an optimal model for an energy management strategy in a real microgrid, which integrates a PV system with storage devices, smart buildings and a plug-in EV. They have minimized the total costs of energy consumption by reducing the power supplied from the grid. A robust optimization has been described in [21] and compared with stochastic optimization to minimize the economic and environmental costs of a microgrid, which integrates PV and EVs. They have proposed a mathematical model to study the uncertainty of EV charging behavior and PV power. A model predictive control has been depicted in [22] using a smart charging strategy that takes into account the future EV charging demand. Their goal is to reduce the peak energy demand for an EV parking lot with PV sources. A multi-objective evolutionary particle swarm optimization problem has been presented in [23] to minimize the costs and the overloading for high demands of grid energy for EV scheduling based on a day-ahead scenario. A novel convex quadratic objective function has been proposed in [24] to minimize the power loss of a microgrid in a two-stage optimization method with different penetration levels of plug-in hybrid EVs, studying the behavior of the plug-in hybrid EVs. The authors of [25] have proposed a stochastic planning model as a convex programming problem to optimize the component sizes by minimizing the total cost of the EV charging station considering the uncertainties of PV production, EV charging demand, and different constraints.