LAPSE:2023.9896v1
Published Article

LAPSE:2023.9896v1
Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System
February 27, 2023
Abstract
Electric vehicle (EV) charging may impose a substantial power demand on existing low voltage (LV) and medium voltage (MV) networks, which are usually not prepared for high power demands in short time intervals. The influx of E-mobility may require an increase in grid reinforcements, but these can be reduced and optimized by a combination of new technologies, tools, and strategies, such as the deployment of solar PV generation integrated with aggregated energy storage systems. One of the challenges in the implementation of charging infrastructures in public stations is coupling the projected sizes of energy demand and power requirements in each location for each charger. This paper describes a method to estimate projected values for energy consumption and power demand in EV fast charging stations (CS). The proposed ideas were applied in a concept facility located in Campinas, Brazil, in a structure equipped with two 50 kW DC Fast Chargers, local 12.5 kW/13.2 kWp PV generation (to reduce energy impacts to the grid), and a 100 kW/200 kWh storage system, using electrochemical batteries (to minimize peak power requirements).
Electric vehicle (EV) charging may impose a substantial power demand on existing low voltage (LV) and medium voltage (MV) networks, which are usually not prepared for high power demands in short time intervals. The influx of E-mobility may require an increase in grid reinforcements, but these can be reduced and optimized by a combination of new technologies, tools, and strategies, such as the deployment of solar PV generation integrated with aggregated energy storage systems. One of the challenges in the implementation of charging infrastructures in public stations is coupling the projected sizes of energy demand and power requirements in each location for each charger. This paper describes a method to estimate projected values for energy consumption and power demand in EV fast charging stations (CS). The proposed ideas were applied in a concept facility located in Campinas, Brazil, in a structure equipped with two 50 kW DC Fast Chargers, local 12.5 kW/13.2 kWp PV generation (to reduce energy impacts to the grid), and a 100 kW/200 kWh storage system, using electrochemical batteries (to minimize peak power requirements).
Record ID
Keywords
consumption forecasting, electric vehicles, energy projections, EV charging stations
Subject
Suggested Citation
Castro JFC, Marques DC, Tavares L, Dantas NKL, Fernandes AL, Tuo J, de Medeiros LHA, Rosas P. Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System. (2023). LAPSE:2023.9896v1
Author Affiliations
Castro JFC: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil [ORCID]
Marques DC: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil
Tavares L: Advanced Institute of Technology and Innovation (IATI), Recife 50751-310, PE, Brazil [ORCID]
Dantas NKL: Institute of Technology Edson Mororó Moura (ITEMM), Recife 51020-280, PE, Brazil
Fernandes AL: CPFL Energy, Campinas 13087-397, SP, Brazil
Tuo J: CPFL Energy, Campinas 13087-397, SP, Brazil
de Medeiros LHA: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil
Rosas P: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil [ORCID]
Marques DC: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil
Tavares L: Advanced Institute of Technology and Innovation (IATI), Recife 50751-310, PE, Brazil [ORCID]
Dantas NKL: Institute of Technology Edson Mororó Moura (ITEMM), Recife 51020-280, PE, Brazil
Fernandes AL: CPFL Energy, Campinas 13087-397, SP, Brazil
Tuo J: CPFL Energy, Campinas 13087-397, SP, Brazil
de Medeiros LHA: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil
Rosas P: Electrical Engineering Department, Federal University of Pernambuco (UFPE), Recife 50670-901, PE, Brazil [ORCID]
Journal Name
Energies
Volume
15
Issue
17
First Page
6106
Year
2022
Publication Date
2022-08-23
ISSN
1996-1073
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Original Submission
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PII: en15176106, Publication Type: Journal Article
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LAPSE:2023.9896v1
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https://doi.org/10.3390/en15176106
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Feb 27, 2023
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