Abstract

Balancing energy demand and supply will become an even greater challenge considering the ongoing transition from traditional fuel to electric vehicles (EV). The management of this task will heavily depend on the pace of the adoption of light-duty EVs. Electric vehicles have seen their market share increase worldwide; the same is happening in Portugal, partly because the government has kept incentives for consumers to purchase EVs, despite the COVID-19 pandemic. The consequent shift to EVs entails various challenges for the distribution network, including coping with the expected growing demand for power. This article addresses this concern by presenting a case study of an area comprising 20 municipalities in Northern Portugal, for which battery electric vehicles (BEV) sales and their impact on distribution networks are estimated within the 2030 horizon. The power required from the grid is estimated under three BEV sales growth deterministic scenarios based on a daily consumption rate resulting from the combination of long- and short-distance routes. A Monte Carlo computational simulation is run to account for uncertainty under severe EV sales growth. The analysis is carried out considering three popular BEV models in Portugal, namely the Nissan Leaf, Tesla Model 3, and Renault Zoe. Their impacts on the available power of the distribution network are calculated for peak and off-peak hours. The results suggest that the current power grid capacity will not cope with demand increases as early as 2026. The modeling approach could be replicated in other regions with adjusted parameters.

Highlights

  • European Union (EU) legislation targets are to cut CO2 emissions from cars by 37.5% by 2030 [1]

  • This paper aims to forecast the battery electric vehicles (BEV) segment development toward the 2030 horizon and its effect on the distribution power grid for an area comprising 20 municipalities in Northern Portugal

  • The preference for PHEV may be related to high prices for BEVs and the lack of sufficient charging stations in Portugal, totaling 2471 in 2020, of which 494 were fast charge (>22 kW) and 1976 were normal charge (

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Summary

A Monte Carlo Method Approach

Teresa Nogueira 1,2, * , José Magano 3,4 , Ezequiel Sousa 1 and Gustavo R. Higher Institute of Business and Tourism Sciences (ISCET), 4050-180 Porto, Portugal

Introduction
Demographics
Long-Distance Route
Short-Distance Route
Installed Power and Available Energy during Peak and Off-Peak Hours
BEV Development from 2021 to 2030
Scenario 1
Scenario 2
Scenario 3
Scenario Comparison
Scenario
The available power during peak and off-peak in scenario
Number
Global
Findings
Discussion and Conclusions
Full Text
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