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Impact of PHEVs Penetration on Ontario’s Electricity Grid and Environmental Considerations

Author

Listed:
  • Lena Ahmadi

    (Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Eric Croiset

    (Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Ali Elkamel

    (Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Peter L. Douglas

    (Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Woramon Unbangluang

    (Chemical Engineering Department, King Mongkut's University of Technology, Thonburi, Bangkok 10140, Thailand)

  • Evgueniy Entchev

    (Energy Technology Centre, Natural Resource Canada, Ottawa, ON K1A 1M1, Canada)

Abstract

Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid through an increase in electricity demand. This paper studies effects of the wide spread adoption of PHEVs on peak and base load demands in Ontario, Canada. Long-term forecasting models of peak and base load demands and the number of light-duty vehicles sold were developed. To create proper forecasting models, both linear regression (LR) and non-linear regression (NLR) techniques were employed, considering different ranges in the demographic, climate and economic variables. The results from the LR and NLR models were compared and the most accurate one was selected. Furthermore, forecasting the effects of PHEVs penetration is done through consideration of various scenarios of penetration levels, such as mild, normal and aggressive ones. Finally, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated for electricity production planning purposes.

Suggested Citation

  • Lena Ahmadi & Eric Croiset & Ali Elkamel & Peter L. Douglas & Woramon Unbangluang & Evgueniy Entchev, 2012. "Impact of PHEVs Penetration on Ontario’s Electricity Grid and Environmental Considerations," Energies, MDPI, vol. 5(12), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:5:y:2012:i:12:p:5019-5037:d:21759
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    References listed on IDEAS

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    1. Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
    2. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    3. 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.
    4. F. Chui & A. Elkamel & R. Surit & E. Croiset & P.L. Douglas, 2009. "Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(3), pages 277-304.
    5. Amjad, Shaik & Neelakrishnan, S. & Rudramoorthy, R., 2010. "Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1104-1110, April.
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    Cited by:

    1. Lena Ahmadi & Ali Elkamel & Sabah A. Abdul-Wahab & Michael Pan & Eric Croiset & Peter L. Douglas & Evgueniy Entchev, 2015. "Multi-Period Optimization Model for Electricity Generation Planning Considering Plug-in Hybrid Electric Vehicle Penetration," Energies, MDPI, vol. 8(5), pages 1-25, May.
    2. Jean-Michel Clairand & Javier Rodríguez-García & Carlos Álvarez-Bel, 2018. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation," Energies, MDPI, vol. 11(11), pages 1-21, November.

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