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그린딜 정책에 따른 유럽자동차 산업재편의 EV 에너지 융합방안

EV Energy Convergence Plan for Reshaping the European Automobile Industry According to the Green Deal Policy

  • 서대성 (성결대학교 파이데이학부)
  • 투고 : 2021.04.05
  • 심사 : 2021.06.20
  • 발행 : 2021.06.28

초록

이 논문은 그린 딜로 인한 전기 에너지 수요가 급증을 될 수 있기 때문에 이를 제시하고자 한다. 그러나 미래의 전기자동차와 많은 전기 에너지의 조달은 여전히 화석 연료에 의존한다. 이에 IT 산업의 중요성이 부각되고 수소-전기차의 수요와 연관 산업으로 그 수요가 증가하게 된다. 본 연구의 방법은 IT 산업의 전기 에너지 수요보다 미래 차세대 동력으로 전기차의 충전과 연관성을 조사하였다. 이는 실증적 회귀 분석을 통해 경제 성장에 따른 산업용 전기와 가정용 에너지를 성장에 따른 PPP의 상관관계를 도출하였다. 그 결과 전기차와 차세대 전기차를 포함한 변화량은 GDP 대비 구매력 변화 국가의 1/3에서 유의미한 것으로 나타났다. 이는 전기차의 수요가 있는 32개국 중 12개 국가(이탈리아, 캐나다, 스위스, 폴란드, 슬로베니아, 독일, 슬로바키아, 핀란드, 스웨덴, 체코, 에스토니아, 덴마크)가 더 많은 전기 에너지에 더 민감하기에 전체 구매력에 영향을 미치게 된다. IT-전기 에너지원의 미사용 전력 낭비를 방지하고, 수소전기 충전-보존함으로써, 향후 성장을 위한 수급에 국가의 IT 산업 보존 완충 시설대비가 필수불가결하다.

The paper dealt with the fact that the green deal took place when the demand for electrical energy surged. However, the procurement of electric vehicles and much of the electric energy of the future still depends on fossil fuels. Accordingly, the importance of the IT industry is highlighted, and the demand for hydrogen-electric vehicles and related industries increases. The method of this study investigated the relevance of EV charging as a future next-generation power source rather than the electric energy demand of the IT industry. This study derives the correlation between industrial electricity and household energy PPP according to economic growth through empirical regression analysis. As the result, it was found that the amount of change, including electric and next-generation electric vehicles, was significant for on thirds of the countries in the change in purchasing power compared to GDP. This affects overall purchasing power as twelve out of thirty two countries with EV demand (Italy, Canada, Switzerland, Poland, Slovenia, Germany, Slovakia, Finland, Sweden, Czech Republic, Estonia, Denmark) are more sensitive to electric energy. This is related to the charging of EVs or hydrogen as the next-generation power of the future rather than the electric energy demand of the IT industry. By preventing waste of unused electricity of IT-electric energy sources and charging-preserving hydrogen electricity, it seems indispensable to prepare for the national IT power conservation buffer facility for supply and demand in future growth.

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참고문헌

  1. T. M. Rusche. (2015). Regulation of renewable electricity in the internal electricity market, Cambridge Studies in European Law and Policy, 9-38. Cambridge: Cambridge University Press. DOI : 10.1017/CBO9781316285817.003
  2. R. Berkowitz & D. Aurbach. (2019). There's a lithium battery in your future. MRS Bulletin, 44(12), 918-919. DOI : 10.1557/mrs.2019.297
  3. D. Teece. (2018). Tesla and the Reshaping of the Auto Industry. Management and Organization Review, 14(3), 501-512. DOI : 10.1017/mor.2018.33
  4. B. Lapeyre & E. Quinet. (2017). A Simple GDP-based Model for Public Investments at Risk. Journal of Benefit-Cost Analysis, 8(1), 91-114. DOI : 10.1017/bca.2017.5
  5. M. I. Bayram & M. Devetsikiotis. (2021). Analytical Models for Emerging Energy Storage Applications, Advanced Data Analytics for Power Systems, 455-480, Lodon : Cambridge University Press. DOI : 10.1017/9781108859806.024
  6. C. Harrison, (2020). Electricity capital and accumulation strategies in the U.S. electricity system. Environment and Planning E: Nature and Space, 2514848620949098. DOI : 10.1177/2514848620949098
  7. J. Patrick & S. Eckhard & R. O. Melanie. (2019). How many fast-charging stations dowe need along European highways?, Transportation Research Part D: Transport and Environment, 73, 120-129. https://doi.org/10.1016/j.trd.2019.06.005
  8. R. Hoekstra. (2019). What Does GDP Measure (And What Not)? In Replacing GDP by 2030: (pp. 54-78). Lodon: Cambridge University Press. DOI : 10.1017/9781108608558.003
  9. R. Langarita et al. (2019). Testing European goals for the Spanish electricity system using a disaggregated CGE model. Energy, 179, 1288-1301. DOI : 10.1016/j.energy.2019.04.175
  10. Z. Csefalvay. (2019). Robotizationin Central and Eastern Europe: catching up or dependence?. European Planning Studies, 28(8), 1534-1553. DOI : 10.1080/09654313.2019.1694647.
  11. Statistical Office.(2021). Energy price (OECD). Kosis(Online). https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_2KAA607_OECD
  12. J. Frazelle. (2021). Battery day. Communications of the ACM, 64(5), 52-59. https://doi.org/10.1145/3434222
  13. A. Soman, A. Trivedi, D. Irwin, B. Kosanovic, B. McDaniel & P. Shenoy. (2020). Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids, e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems(pp. 237-241).
  14. D. S. Seo. (2019). A Study on the Autonomous Decision Right of Emotional AI based on Analysis of 4th Wave Technology Availability in the Hyper-Linkage. Journal of Converence for Information Technology, 9(8), 9-19. DOI : 10.22156/CS4SMB.2019.9.8.009.