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An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

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  • Azadeh Ahkamiraad

    (Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Yong Wang

    (Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA)

Abstract

Current power grids in many countries are not fully prepared for high electric vehicle (EV) penetration, and there is evidence that the construction of additional grid capacity is constantly outpaced by EV diffusion. If this situation continues, then it will compromise grid reliability and cause problems such as system overload, voltage and frequency fluctuations, and power losses. This is especially true for densely populated areas where the grid capacity is already strained with existing old infrastructure. The objective of this research is to identify the zip-code level electricity consumption that is associated with large-scale EV adoption in New York City, one of the most densely populated areas in the United States (U.S.). We fuse the Fisher and Pry diffusion model and Rogers model within the agent-based simulation to forecast zip-code level EV diffusion and the required energy capacity to satisfy the charging demand. The research outcomes will assist policy makers and grid operators in making better planning decisions on the locations and timing of investments during the transition to smarter grids and greener transportation.

Suggested Citation

  • Azadeh Ahkamiraad & Yong Wang, 2018. "An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City," Energies, MDPI, vol. 11(3), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:640-:d:136143
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    References listed on IDEAS

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    1. Adepetu, Adedamola & Keshav, Srinivasan & Arya, Vijay, 2016. "An agent-based electric vehicle ecosystem model: San Francisco case study," Transport Policy, Elsevier, vol. 46(C), pages 109-122.
    2. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    3. Susanne Linder, 2011. "Spatial diffusion of electric vehicles in the German metropolitan region of Stuttgart," ERSA conference papers ersa11p557, European Regional Science Association.
    4. Eising, Jan Willem & van Onna, Tom & Alkemade, Floortje, 2014. "Towards smart grids: Identifying the risks that arise from the integration of energy and transport supply chains," Applied Energy, Elsevier, vol. 123(C), pages 448-455.
    5. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    6. Noori, Mehdi & Tatari, Omer, 2016. "Development of an agent-based model for regional market penetration projections of electric vehicles in the United States," Energy, Elsevier, vol. 96(C), pages 215-230.
    7. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    8. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    9. Bale, Catherine S.E. & McCullen, Nicholas J. & Foxon, Timothy J. & Rucklidge, Alastair M. & Gale, William F., 2013. "Harnessing social networks for promoting adoption of energy technologies in the domestic sector," Energy Policy, Elsevier, vol. 63(C), pages 833-844.
    10. Silvia, Chris & Krause, Rachel M., 2016. "Assessing the impact of policy interventions on the adoption of plug-in electric vehicles: An agent-based model," Energy Policy, Elsevier, vol. 96(C), pages 105-118.
    11. Daziano, Ricardo A. & Chiew, Esther, 2012. "Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation," Energy Policy, Elsevier, vol. 51(C), pages 876-894.
    12. Brouwer, Anne Sjoerd & Kuramochi, Takeshi & van den Broek, Machteld & Faaij, André, 2013. "Fulfilling the electricity demand of electric vehicles in the long term future: An evaluation of centralized and decentralized power supply systems," Applied Energy, Elsevier, vol. 107(C), pages 33-51.
    13. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    14. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    15. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    16. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    17. Bertotti, M.L. & Brunner, J. & Modanese, G., 2016. "The Bass diffusion model on networks with correlations and inhomogeneous advertising," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 55-63.
    18. Saarenpää, Jukka & Kolehmainen, Mikko & Niska, Harri, 2013. "Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption," Applied Energy, Elsevier, vol. 107(C), pages 456-464.
    19. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    20. Römer, Benedikt & Reichhart, Philipp & Kranz, Johann & Picot, Arnold, 2012. "The role of smart metering and decentralized electricity storage for smart grids: The importance of positive externalities," Energy Policy, Elsevier, vol. 50(C), pages 486-495.
    21. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
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