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The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge's operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the static transmission expansion planning (TEP) problem is solved for real large electrical networks. Different generation and load scenarios are considered and the "N‐1" security criterion is ensured. The solution of the problem is achieved based on a constructive metaheuristic algorithm (CMA‐TEP) specially developed for large systems. The proposed method performs a gradual and parallel process of constructing solutions (expansion plans), which incorporates heuristics and procedures generally used by planning engineers. Operational information for the intact network condition and under contingencies is used during the constructive process, to identify reinforcements to be added (most attractive) to or removed (less effective) from the network being set up. Moreover, mechanisms of recombination, perturbation, selection, refinement, and depuration of solutions are also employed during the constructive process. The linear DC network model, including ohmic transmission losses, is used throughout the entire constructive process. The robustness and efficiency of the proposed CMA‐TEP method is tested in a configuration of the Brazilian network, considering two hydrological scenarios: dry and humid. Comparisons based on statistical performance indices are also carried out with specialized genetic algorithms. [ABSTRACT FROM AUTHOR]