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Key Features of Electric Vehicle Diffusion and Its Impact on the Korean Power Market

Dongnyok Shim (), Seung Wan Kim (), Jörn Altmann (), Yong Tae Yoon () and Jin Gyo Kim ()
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Dongnyok Shim: Korea Information Society Development Institute (KISDI), 18 Jeongtong-ro, Deoksan-myeon, Jincheon-gun, Chungchengbuk-do 27872, Korea
Seung Wan Kim: Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Yong Tae Yoon: Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Jin Gyo Kim: Graduate School of Business, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

Sustainability, 2018, vol. 10, issue 6, 1-18

Abstract: The market share of electric vehicles is growing and the interest in these vehicles is rapidly increasing in industrialized countries. In the light of these circumstances, this study provides an integrated policy-making package, which includes key features for electric vehicle diffusion and its impact on the Korean power market. This research is based on a quantitative analysis with the following steps: (1) it analyzes drivers’ preferences for electric or traditional internal combustion engine (ICE) vehicles with respect to key automobile attributes and these key attributes indicate what policy makers should focus on; (2) it forecasts the achievable level of market share of electric vehicles in relation to improvements in their key attributes; and (3) it evaluates the impact of electric vehicle diffusion on the Korean power market based on an achievable level of market share with different charging demand profiles. Our results reveal the market share of electric vehicles can increase to around 40% of the total market share if the key features of electric vehicles reach a similar level to those of traditional vehicles. In this estimation, an increase in the power market’s system generation costs will reach around 10% of the cost in the baseline scenario, which differs slightly depending on charging demand profiles.

Keywords: electric vehicle diffusion; demand forecasting; mixed logit model; power market simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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