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A Scenario-Based Approach to Using Electric Vehicle Batteries in Virtual Power Plants: Insights from Environmental, Social, and Governance and Monte Carlo Simulations

Author

Listed:
  • Seungryong Choi

    (Graduate School of Management of Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Keuntae Cho

    (Department of Systems Management Engineering & Graduate School of Management of Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea)

Abstract

The intensified global focus on the energy transition and sustainability has increased the drive to leverage electric vehicle (EV) batteries as virtual power plant (VPP) resources. However, uncertainties and governance factors associated with this integration have not been systematically researched. This study aimed to identify and evaluate the key uncertainties surrounding the deployment of EV batteries in VPPs and propose strategic responses from an ESG perspective. We adopted a mixed-methods approach using scenario planning to identify critical uncertainties. The approach included quantitative assessments using Monte Carlo simulations and a scenario matrix to incorporate ESG elements into future projections. The findings highlighted economic value volatility (E: 13.37%), employment creation potential and sustainability (S: 10.68%), and increased transparency requirements (G: 8.60%) as the most influential uncertainty factors based on the simulation results. These variables formed the basis for selecting three core drivers for scenario construction. Four distinct scenarios were identified. By proposing tailored response strategies for each scenario, this study suggests that the long-term sustainability of EV batteries and VPP industries can be bolstered in various potential future environments. Integrating ESG factors into a scenario analysis helps decision-making in industries characterized by high uncertainty. The study offers strategies that embed ESG considerations to support the sustainability of EV batteries and VPP sectors and provides valuable insights for shaping policies, industrial strategies, and corporate ESG initiatives.

Suggested Citation

  • Seungryong Choi & Keuntae Cho, 2025. "A Scenario-Based Approach to Using Electric Vehicle Batteries in Virtual Power Plants: Insights from Environmental, Social, and Governance and Monte Carlo Simulations," Sustainability, MDPI, vol. 17(7), pages 1-34, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3224-:d:1628323
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    1. Lin Zhu & Lichun He & Peipei Shang & Yingchun Zhang & Xiaojun Ma, 2018. "Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation," Energies, MDPI, vol. 11(9), pages 1-26, September.
    2. Hon Chung Lau & Steve C. Tsai, 2023. "Global Decarbonization: Current Status and What It Will Take to Achieve Net Zero by 2050," Energies, MDPI, vol. 16(23), pages 1-24, November.
    3. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    4. Wustenhagen, Rolf & Wolsink, Maarten & Burer, Mary Jean, 2007. "Social acceptance of renewable energy innovation: An introduction to the concept," Energy Policy, Elsevier, vol. 35(5), pages 2683-2691, May.
    5. Markmann, Christoph & Darkow, Inga-Lena & von der Gracht, Heiko, 2013. "A Delphi-based risk analysis — Identifying and assessing future challenges for supply chain security in a multi-stakeholder environment," Technological Forecasting and Social Change, Elsevier, vol. 80(9), pages 1815-1833.
    6. S. S. Lavenberg & P. D. Welch, 1981. "A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations," Management Science, INFORMS, vol. 27(3), pages 322-335, March.
    7. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
    8. Fink, A. & Kosecoff, J. & Chassin, M. & Brook, R.H., 1984. "Consensus methods: Characteristics and guidelines for use," American Journal of Public Health, American Public Health Association, vol. 74(9), pages 979-983.
    9. Hawawini, Gabriel, 1978. "A mean-standard deviation exposition of the theory of the firm under uncertainty," MPRA Paper 10148, University Library of Munich, Germany.
    10. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    11. Hobday, Mike, 2000. "The project-based organisation: an ideal form for managing complex products and systems?," Research Policy, Elsevier, vol. 29(7-8), pages 871-893, August.
    12. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
    13. Alessandro Cortesi & Patrizia Tettamanzi & Fabio Corno, 2009. "Empirical evidence on internal control systems and corporate governance in Italy," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 13(1), pages 75-100, May.
    14. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    15. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.
    16. Ioannis Ioannou & George Serafeim, 2015. "The impact of corporate social responsibility on investment recommendations: Analysts' perceptions and shifting institutional logics," Strategic Management Journal, Wiley Blackwell, vol. 36(7), pages 1053-1081, July.
    17. Katrin Schmietendorf & Joachim Peinke & Oliver Kamps, 2017. "The impact of turbulent renewable energy production on power grid stability and quality," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(11), pages 1-6, November.
    18. Catalin Popescu & Eglantina Hysa & Alba Kruja & Egla Mansi, 2022. "Social Innovation, Circularity and Energy Transition for Environmental, Social and Governance (ESG) Practices—A Comprehensive Review," Energies, MDPI, vol. 15(23), pages 1-28, November.
    19. Wüstenhagen, Rolf & Menichetti, Emanuela, 2012. "Strategic choices for renewable energy investment: Conceptual framework and opportunities for further research," Energy Policy, Elsevier, vol. 40(C), pages 1-10.
    20. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    21. Jordi Surroca & Josep A. Tribó & Sandra Waddock, 2010. "Corporate responsibility and financial performance: the role of intangible resources," Strategic Management Journal, Wiley Blackwell, vol. 31(5), pages 463-490, May.
    22. Xu, Chonggang & Gertner, George Zdzislaw, 2008. "Uncertainty and sensitivity analysis for models with correlated parameters," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1563-1573.
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    Cited by:

    1. Chia-Sheng Tu & Ming-Tang Tsai, 2025. "The Optimal Energy Management of Virtual Power Plants by Considering Demand Response and Electric Vehicles," Energies, MDPI, vol. 18(17), pages 1-18, August.

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