Abstract
The use of electric vehicles and renewable energy sources can significantly contribute to the reduction of pollution levels, fostering a cleaner and more sustainable environment. In addition to their positive environmental impact, electric vehicles can also serve as energy storage units, enabling a bidirectional energy flow between the vehicles and the grid. Despite these promising prospects, a disjointed approach to charging infrastructure planning overlooks the synergies between the energy and transportation ecosystem. This calls for a holistic and coordinated approach to infrastructure development that harnesses the benefits emerging from the convergence of electric vehicles, renewable energy, and transport planning. This paper presents a methodology for the optimal placement of future Energy Hubs for electric vehicle charging and renewable generation. The methodology uses data from open-source datasets involving renewable energy sources, traffic density, charging patterns, road topology, and land use. The optimization problem is formulated as a multi-objective linear programming problem, with the objectives of minimizing the number of Energy Hub units, minimizing electric vehicles charging radius coverage and distance from power substations, and maximizing energy generation. The methodology is demonstrated through an application in Trondheim, Norway, which produces a Pareto front for decision-makers to evaluate the best trade-offs between objectives.
