The incorporation of electric vehicles into the transportation system is imperative in order to mitigate the environmental impact of fossil fuel use. This requires establishing methods for deploying the charging infrastructure in an optimal way. In this paper, an optimization model is developed to identify both the number of stations to be deployed and their respective locations that minimize the total cost by utilizing Genetic Algorithms. This is implemented by combining these components into a linear objective function aiming to minimize the overall cost of deploying the charging network and maximize service quality to users by minimizing the average travel distance between demand spots and stations. Several numerical and practical considerations have been analyzed to provide an in-depth study and a deeper understanding of the model’s capabilities. The optimization is done through commercial software that is appropriately parametrized to adjust to the specific problem. The model is simple yet effective in solving a variety of problem structures, optimization goals and constraints. Further, the quality of the solution seems to be marginally affected by the shape and size of the problem area, as well as the number of demand spots, and this may be considered one of the strengths of the algorithm. The model responds expectedly to variations in the charging demand levels and can effectively run at different levels of grid discretization.

E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids. According to the literature, leaving the charging process uncontrolled could hinder some of the present challenges in the power system. The development of a suitable charging management system is required to address different stakeholders’ needs in the electro-mobility value chain. This paper focuses on the design of such a system, the TwinEV module, that offers high-value services to electric vehicles (EV) users. This module is based on a Smart Charging Tool (SCT), aiming to deliver a more user-central and cooperative approach to the EV charging processes. The methodology of the SCT tool, as well as the supportive optimization algorithm, are explained thoroughly. The architecture and the web applications of TwinEV module are analyzed. Finally, the deployment and testing results are presented.

Urban systems are increasingly challenged by the complex interplay of social, economic, environmental, and infrastructural dynamics. Traditional methods typically focus on isolated impacts, failing to adequately account for the interconnectedness of urban subsystems. This paper addresses this research gap by leveraging system dynamics as a robust modeling and simulation methodology for exploring urban developmental patterns and their long-term implications. The approach is contextualized within the sustainable development discourse, emphasizing systemic feedback relationships among transport, population, economy, and environment subsystems. We demonstrate the method's efficacy through an in-depth case study of Mandra, Greece, a city facing typical urban challenges such as congestion, economic stagnation, and environmental degradation. The developed simulation model establishes clear causal links among key variables, enabling scenario testing and long-term forecasting of policy impacts. Results from diverse scenarios illustrate how various urban development policies might affect system sustainability over extended periods. The model proves valuable for urban planners by simplifying complex urban dynamics into actionable insights, thus significantly enhancing informed decision-making. Ultimately, this study underscores the potential of system dynamics to bridge urban complexity with practical policymaking, offering an innovative analytical framework to guide cities toward sustainable futures.