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<bold>Research background:</bold> The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger.
<bold>Purpose:</bold> The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments.
<bold>Research methodology</bold>: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles.
<bold>Results:</bold> The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations.
<bold>Novelty:</bold> The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.
</abstract>

Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger.
Purpose: The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments.
Research methodology: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles.
Results: The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations.
Novelty: The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.

European Green Deal Implications on Country Level Energy Consumption

Ph.D., Associate professor Vilnius University

Ph.D., Associate Professor Vilnius Gediminas Technical University

Faculty of Transport Engineering Department of Logistics and transport management Plytines 27

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

{"article-title":"European Green Deal Implications on Country Level Energy Consumption"}

Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions...

European Green Deal Implications on Country Level Energy Consumption

Pubblicato online: 20 dic 2022

Pagine: 97 - 122

Ricevuto: 02 lug 2022

Accettato: 11 ott 2022

DOI: https://doi.org/10.2478/foli-2022-0021

Parole chiave
electricity demand, transport forecasts, policy measures, investment, scenario modelling

© 2022 Andrius Jaržemskis, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

European Green Deal Implications on Country Level Energy Consumption

Pubblicato online: 20 dic 2022

Pagine: 97 - 122

Ricevuto: 02 lug 2022

Accettato: 11 ott 2022

DOI: https://doi.org/10.2478/foli-2022-0021

Parole chiaveelectricity demand, transport forecasts, policy measures, investment, scenario modelling

© 2022 Andrius Jaržemskis, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Parole chiave
electricity demand, transport forecasts, policy measures, investment, scenario modelling