In a smart city, it is vital to provide a clean and green environment by curbing air pollution and greenhouse gas emissions (GHGs) from transportation. As a recent action from many governments aiming to minimize transportation’s pollution upon the climate, new plans have been announced to ban cars with gas engines throughout the world. Therefore, it is anticipated that the presence of electric vehicles (EVs) will grow very fast globally. Consequently, the necessity to establish electric vehicle supply equipment (EVSE) in the smart city through public charging stations is growing incrementally year by year. However, the EV charging process via EVSE which is primarily connected to the power grid will put high pressure upon the centralized power grid, especially during peak demand periods. Increasing the power production of power grid will increase the environmental impact. Therefore, it is fundamental for the smart city to be equipped with a modern power grid to cope with the traditional power grid’s drawbacks. In this thesis, we conduct an in-depth analysis of the problem of EVs’ interaction with the modern power grid in a smart city to manage and control EV charging and discharging processes. We also present various approaches and mechanisms toward identifying and investigating these challenges and requirements to manage the power demand. We propose novel solutions, namely Decentralized-EVSE (D-EVSE), for EVs’ charging and discharging processes based on Renewable Energy Sources (RESs) and an energy storage system. We present two algorithms to manage the interaction between EVs and D-EVSE while maximizing EV drivers’ satisfaction in terms of reducing the waiting time for charging or discharging services and minimizing the stress placed on D-EVSE. We propose an optimization model based on Game Theory (GT) to manage the interaction between EVs and D-EVSE. We name this the decentralized-GT (D-GT) model. This model aims to find the optimal solution for EVs and D-EVSE based on the concept of win-win. We design a decentralized profit maximization algorithm to help D-EVSE take profit from the electricity price variation during the day when selling or buying electricity respectively to EVs or from the grid or EVs as discharging processes. We implement different scenarios to these models and show through analytical and simulation results that our proposed models help to minimize the D-EVSE stress level, increase the D-EVSE sustainability, maximize the D-EVSE profit, as well as maximize EV drivers’ satisfaction and reduce EVs’ waiting time.