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.
In this paper the potential for conflict in the Middle East due to the depleting of regional water reserves is explored and the implications for regional policy making, in the light of other tensions are investigated. We use System Dynamics to develop a model for water resources management and explore via simulation issues that emerge as important, in order to highlight required policies. This analysis validates part of the existing thinking and policies in place and signifies further the need for transboundary cooperation and emphasis on areas that may not be viewed traditionally as key priorities (e.g. demand management, as opposed to continuity of supply).
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