Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

Francesco Lo Franco, Mattia Ricco (), Vincenzo Cirimele, Valerio Apicella, Benedetto Carambia and Gabriele Grandi
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Francesco Lo Franco: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
Mattia Ricco: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
Vincenzo Cirimele: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
Valerio Apicella: R&D and Innovation Group, Movyon s.p.a., 50013 Florence, Italy
Benedetto Carambia: R&D and Innovation Group, Movyon s.p.a., 50013 Florence, Italy
Gabriele Grandi: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy

Energies, 2023, vol. 16, issue 4, 1-27

Abstract: Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

Keywords: electric vehicles; EV power demand forecasting; charging hub; urban scenarios; machine learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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