This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler–Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, operating independently or alongside the main grid to facilitate power flow management among interconnected sources and different loads locally. This review paper aims to offer a comprehensive overview of the different control strategies proposed in the literature to control microgrids with electric vehicle charging stations. The surveyed research is primarily categorized according to the employed control algorithms, although distinctions are also made based on defined microgrid architecture, utilization of specific power sources, and charging stations configurations. Additionally, this paper identifies research gaps in the current research. These gaps encompass the use of oversimplified models for charging stations and/or renewable sources operation, limited simulation time periods, or lack of experimental testing of proposed approaches. In the light of these identified shortcomings, this manuscript presents recommendations for guiding future research.

With an increasing number of electric vehicles (EVs), their owners’ involvement in the control of electric power systems and their market seems to be the only option for stable operation of future power networks. However, these people usually have little knowledge about power systems’ operation and follow just their interests. Therefore, this paper deals with the decision-making process of EV drivers at the charging station. The paper presents the stated preference survey used to collect the responses to hypothetical scenarios, where respondents chose between three alternatives, namely slow charging, fast charging, and vehicle-to-grid services. The survey also contained questions about respondents’ socio-demographic characteristics, as gender, age, etc. The decision-making prediction models for each socio-demographic characteristic were created using the acquired data. The paper presents the estimated parameters of the attributes affecting the respondents’ choices for the models that allow models’ simple implementation. Knowing these models and the customers’ composition, the operators of the charging stations or the distribution networks could better estimate EV owners’ behavior and so their expected power demand. Moreover, operators could more effectively implement incentives for their customers and affect the customers’ behavior in a way that is suitable for better operation of their power systems.

Recent events in Europe show the necessity of speeding up the transition to local renewable energy sources utilization within countries’ power systems. Moreover, the share of electric vehicles is gradually increasing within the Europe market. To utilize renewable energy sources with intermittent character to supply power for this increasing demand for electric vehicle charging, the vehicle-to-grid concept must be applied. However, for this concept to be successful, it should be beneficial for electric vehicle drivers and charging station operators. As a result, the focus of this research is on developing a charging/discharging control algorithm that adheres to the above assumptions. The use of the proposed control mechanism in case studies demonstrates that if electric vehicle drivers declare their preferences truthfully, the social welfare of all participants can be maximized. To prevent one of the drivers from trying to disrupt the social welfare concept in their favor by reporting false preferences, the incentive mechanism is proposed as well. The case studies show that the incentive mechanism results in the increased cost of the participant trying to disrupt the social welfare while decreasing the cost of all the others. Therefore, truth-telling and thus maximizing the social welfare of all participants in the system is an optimal strategy for each of them.