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Evaluation of Grid Level Impacts of Electric Vehicles

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  • Safiullah, Hameed

Abstract

Currently, most countries are looking to reduce their dependency on imported oil. The added advantage of reducing green house gas emissions and other pollutants has been strong reasons for the growing support for Electric Vehicles. As electric vehicles would be using the power grid to charge their batteries, there are prevalent doubts as to whether the existing power grid will be able to support the increase in load. It is of great interest to the electric utilities to evaluate the capability of the existing grid to withstand high electric vehicle penetration. The fact that there will be higher concentration of electric vehicles in affluent neighborhoods is of great concern. In this thesis, the impact of electric vehicle concentration is studied and the effects evaluated. The electric vehicle flow in the system is first modeled and the corresponding behavior is studied. This model is integrated into an agent-based simulation to model the demand curve of residential customers. Finally, the demand curve is used in a loss-of-life calculation of the transformer to evaluate the impact on the grid.

Suggested Citation

  • Safiullah, Hameed, 2011. "Evaluation of Grid Level Impacts of Electric Vehicles," MPRA Paper 58517, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58517
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    References listed on IDEAS

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    1. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    2. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    3. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    4. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    5. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.
    6. Sioshansi, Ramteen & Fagiani, Riccardo & Marano, Vincenzo, 2010. "Cost and emissions impacts of plug-in hybrid vehicles on the Ohio power system," Energy Policy, Elsevier, vol. 38(11), pages 6703-6712, November.
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    More about this item

    Keywords

    Energy Economics; Electricity Markets; Energy Systems; Renewable Power;
    All these keywords.

    JEL classification:

    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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