Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub

José F. C. Castro (), Augusto C. Venerando, Pedro A. C. Rosas, Rafael C. Neto, Leonardo R. Limongi, Fernando L. Xavier, Wesley M. Rhoden, Newmar Spader, Adriano P. Simões, Nicolau K. L. Dantas, Antônio V. M. L. Filho, Luiz C. P. Silva and Pérolla Rodrigues
Additional contact information
José F. C. Castro: Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
Augusto C. Venerando: School of Electrical and Computer Engineering, State University of Campinas—UNICAMP, Campinas 13083-852, SP, Brazil
Pedro A. C. Rosas: Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
Rafael C. Neto: Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
Leonardo R. Limongi: Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
Fernando L. Xavier: Department of Electrical Engineering and Power Systems, Federal University of Pernambuco—UFPE, Recife 50670-901, PE, Brazil
Wesley M. Rhoden: EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil
Newmar Spader: EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil
Adriano P. Simões: EDP Energias do Brasil, São Paulo 05069-900, SP, Brazil
Nicolau K. L. Dantas: Institute of Technology Edson Mororó Moura—ITEMM, Belo Jardim 55150-550, PE, Brazil
Antônio V. M. L. Filho: Institute of Technology Edson Mororó Moura—ITEMM, Belo Jardim 55150-550, PE, Brazil
Luiz C. P. Silva: School of Electrical and Computer Engineering, State University of Campinas—UNICAMP, Campinas 13083-852, SP, Brazil
Pérolla Rodrigues: Electrical Engineering Department, IATI—Advanced Institute of Technology and Innovation, Recife 50751-310, PE, Brazil

Energies, 2024, vol. 17, issue 13, 1-21

Abstract: 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.

Keywords: electric mobility; EV charging hub; forecasting energy consumption; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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