IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i3p640-d136143.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/3/640/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/3/640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Susanne Linder, 2011. "Spatial diffusion of electric vehicles in the German metropolitan region of Stuttgart," ERSA conference papers ersa11p557, European Regional Science Association.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leonardo Bitencourt & Tiago Abud & Rachel Santos & Bruno Borba, 2021. "Bass Diffusion Model Adaptation Considering Public Policies to Improve Electric Vehicle Sales—A Brazilian Case Study," Energies, MDPI, vol. 14(17), pages 1-19, September.
    2. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Alderete Peralta, Ali & Balta-Ozkan, Nazmiye & Longhurst, Philip, 2022. "Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach," Applied Energy, Elsevier, vol. 305(C).
    3. Liu, Junbei & Zhuge, Chengxiang & Tang, Justin Hayse Chiwing G. & Meng, Meng & Zhang, Jie, 2022. "A spatial agent-based joint model of electric vehicle and vehicle-to-grid adoption: A case of Beijing," Applied Energy, Elsevier, vol. 310(C).
    4. Mo Chen & Rudy X. J. Liu & Chaochao Liu, 2021. "How to Improve the Market Penetration of New Energy Vehicles in China: An Agent-Based Model with a Three-Level Variables Structure," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    5. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    6. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    7. Hidayatno, Akhmad & Jafino, Bramka Arga & Setiawan, Andri D. & Purwanto, Widodo Wahyu, 2020. "When and why does transition fail? A model-based identification of adoption barriers and policy vulnerabilities for transition to natural gas vehicles," Energy Policy, Elsevier, vol. 138(C).
    8. Heymann, Fabian & Miranda, Vladimiro & Soares, Filipe Joel & Duenas, Pablo & Perez Arriaga, Ignacio & Prata, Ricardo, 2019. "Orchestrating incentive designs to reduce adverse system-level effects of large-scale EV/PV adoption – The case of Portugal," Applied Energy, Elsevier, vol. 256(C).
    9. Wolinetz, Michael & Axsen, Jonn, 2017. "How policy can build the plug-in electric vehicle market: Insights from the REspondent-based Preference And Constraints (REPAC) model," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 238-250.
    10. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    11. Krupa, Joseph S. & Rizzo, Donna M. & Eppstein, Margaret J. & Brad Lanute, D. & Gaalema, Diann E. & Lakkaraju, Kiran & Warrender, Christina E., 2014. "Analysis of a consumer survey on plug-in hybrid electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 14-31.
    12. 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.
    13. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.
    14. Noori, Mehdi & Zhao, Yang & Onat, Nuri C. & Gardner, Stephanie & Tatari, Omer, 2016. "Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: Analysis of regional net revenue and emissions savings," Applied Energy, Elsevier, vol. 168(C), pages 146-158.
    15. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
    16. Harrison, Gillian & Thiel, Christian, 2017. "An exploratory policy analysis of electric vehicle sales competition and sensitivity to infrastructure in Europe," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 165-178.
    17. Sen, Burak & Noori, Mehdi & Tatari, Omer, 2017. "Will Corporate Average Fuel Economy (CAFE) Standard help? Modeling CAFE's impact on market share of electric vehicles," Energy Policy, Elsevier, vol. 109(C), pages 279-287.
    18. 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.
    19. 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.
    20. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:640-:d:136143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.