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Multi-Period Optimization Model for Electricity Generation Planning Considering Plug-in Hybrid Electric Vehicle Penetration

Author

Listed:
  • Lena Ahmadi

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

  • Ali Elkamel

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

  • Sabah A. Abdul-Wahab

    (Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khod 123, Muscat, Sultanate of Oman)

  • Michael Pan

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

  • Eric Croiset

    (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)

  • Evgueniy Entchev

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

Abstract

One of the main challenges for widespread penetration of plug-in hybrid electric vehicles (PHEVs) is their impact on the electricity grid. The energy sector must anticipate and prepare for this extra demand and implement long-term planning for electricity production. In this paper, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated into an electricity production planning model. A case study pertaining to Ontario energy planning is considered to optimize the value of the cost of the electricity over sixteen years (2014–2030). The objective function consists of the fuel costs, fixed and variable operating and maintenance costs, capital costs for new power plants, and the retrofit costs of existing power plants. Five different case studies are performed with different PHEVs penetration rates, types of new power plants, and CO 2 emission constraints. Among all the cases studied, the one requiring the most new capacity, (~8748 MW), is assuming the base case with 6% reduction in CO 2 in year 2018 and high PHEV penetration. The next highest one is the base case, plus considering doubled NG prices, PHEV medium penetration rate and no CO 2 emissions reduction target with an increase of 34.78% in the total installed capacity in 2030. Furthermore, optimization results indicate that by not utilizing coal power stations the CO 2 emissions are the lowest: ~500 tonnes compared to ~900 tonnes when coal is permitted.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:5:p:3978-4002:d:49256
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    References listed on IDEAS

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    Cited by:

    1. Chandra Ade Irawan & Peter S. Hofman & Hing Kai Chan & Antony Paulraj, 2022. "A stochastic programming model for an energy planning problem: formulation, solution method and application," Annals of Operations Research, Springer, vol. 311(2), pages 695-730, April.
    2. Adeel Arif & Muhammad Rizwan & Ali Elkamel & Luqman Hakeem & Muhammad Zaman, 2020. "Optimal Selection of Integrated Electricity Generation Systems for the Power Sector with Low Greenhouse Gas (GHG) Emissions," Energies, MDPI, vol. 13(17), pages 1-37, September.
    3. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    4. Li, Wei & Jia, Zhijie & Zhang, Hongzhi, 2017. "The impact of electric vehicles and CCS in the context of emission trading scheme in China: A CGE-based analysis," Energy, Elsevier, vol. 119(C), pages 800-816.
    5. Nikolaos E. Koltsaklis & Athanasios S. Dagoumas, 2021. "A power system scheduling model with carbon intensity and ramping capacity constraints," Operational Research, Springer, vol. 21(1), pages 647-687, March.

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