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

Lena Ahmadi, Ali Elkamel, Sabah A. Abdul-Wahab, Michael Pan, Eric Croiset, Peter L. Douglas and Evgueniy Entchev
Additional contact information
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

Energies, 2015, vol. 8, issue 5, 1-25

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.

Keywords: plug-in hybrid electric vehicles; mixed integer programing; forecasting; optimization; energy planning; power plants; carbon management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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