Elektrikli araçların kısa dönemde maliyetler, sera gazı salınımları ve enerji tüketimi üzerindeki etkisinin hesaplanması üzerine bir method: Türkiye örneği

In today?s world, where sustainability of energy supply chains is questioned intensively, electric vehicles (EV) are emerging as a solution to reduce emissions and increase efficiency of the road transportation sector. Impacts of this emerging technology, such as emissions, primary energy consumption and cost to end users should be analyzed before introduction of the technology, in order to get the best benefit and avoid a case where they perform worse than conventional vehicles (CV). Studies in the literature are based on average energy mix or pre-defined generation scenarios and they aim to construct policy recommendations solely on the cost minimization objective. However, the performance of EVs depends on the sources that are used to generate the marginal electricity that charges the batteries and single objective models provide a limited analysis on the benefits of EVs. Moreover, impact analysis studies always used pre-defined scenarios for charging hours without any optimization effort to determine the best hours of charging and there are no studies that analyze Turkey, which is an important potential market for EVs.In this study, gaps above are addressed by a methodology that analyzes performance of EVs under different charging and market penetration scenarios and compares the performance with CVs. The methodology uses a bi-objective optimization model representing the electricity market to determine the efficient set of options for marginal generators that charge EVs. The model is applied by using real data from the Turkish electricity market. Results show that, electric vehicles provide an opportunity to decrease costs, emissions and primary energy consumption significantly compared to CVs in Turkey. Single objective models are shown to prevent the sector from getting the best environmental benefit and may end up in a solution where EVs perform worse than CVs. Hence, decision makers should use bi-objective models to make better use of EVs and take specifications of the marginal electricity into account before shaping EV policies.