## 1 Introduction
Moreover, in another study [14] network theory for managing EVs is embedded in a complex energy hub (involving AC and DC, as well as thermal and cooling parts) aimed at determining optimal bidding through risk measures. In another study [15], EV charging is a random variable in a robust optimization approach based on MILP. Other papers focus on EV controllable charge. In one study [16], the operation of microgrid components, including EVs, is determined by solving a non-linear programming problem with the objectives of minimum cost, emissions, and load variation, comparing a standard particle swarm optimization algorithm with advanced ones. In another study [17], maximum revenue and maximum EV requirement satisfaction are compared as objectives for EV management in a smart city environment. In another study [18], a model based on Poisson distribution of EV arrivals is embedded in a robust optimization approach. Finally, a sequential energy management strategy procedure involving either smart charging or immediate charge in a DC microgrid, including only EV stations, is described in a previous study [19]. A performance comparison of EV charge and V2G in day-ahead operation planning for minimum grid exchange in a microgrid is proposed in a previous study [20], considering different EV models and drawing suitable indicators. In another study [21], the effects of an EV management strategy exploiting load variation in a microgrid for placing charge and discharge actions is investigated. In another study [22], the optimization of a microgrid with or without EVs able to perform V2G and demand response is carried out considering operational costs and EV battery degradation costs. With regard to V2G approaches, in a previous study [23] a minimum-cost objective is posed, considering reserve constraints in a MILP environment with EV priority according to forecast use, whereas in another study [24] a function including costs, battery degradation, and energy (not supplied estimation) is considered, involving a transformation of AC load flow relations and a solution with a modified bat algorithm. In another study [25], the influence of V2G in a multi-energy microgrid with different EV users is considered by accounting for EV aggregators. In another study [26] the effect of uncertainty is shown by stochastic scenarios of EV and load in a microgrid optimization procedure with AC load flow decoupling, reporting sensibilities on parameter variation. In one study [27] a Monte-Carlo selection of best results of optimal operation is carried out by means of proper fitness functions, exploiting a GA solution. In another study [28], two different approaches are exploited in the presence of accurate or inaccurate EV usage forecasting, involving optimization versus multi-agent auctions. In one study [29] a multi-objective procedure, including cost and power fluctuations, is proposed, accounting for different EV operation modes and drawing V2G incentives using a multi-stage memetic algorithm to find a solution. The energy management for EV integration in a workplace microgrid with a multi-agent optimization scheme is described in a previous study [30]. Photovoltaic (PV) powered EV station programming is dealt with in a previous study [31], modeling different attitudes to smart charging of EVs. In another study [32], a DC microgrid with PV and EV charging stations is proposed, including cost and energy circulation of batteries, and solved with non-dominated sorting genetic algorithm (NSGA-II). In another study [33], an optimization model, including cost and an EV preference indicator, is considered for a system including PV, storage, and EVs with V2G. In another study [34], optimized control of an EV station integrated with PV and storage is proposed, involving contracts with EVs, battery degradation, and unsupplied demand costs with AC load flow. Stochastic dynamic programming for an EV station, including PV, storage, and fuel cells, is reported in one study [35], comparing this with deterministic programming.