Multi-Period Optimization Model for ElectricityGeneration Planning Considering Plug-in HybridElectric Vehicle Penetration

Energies

Michael Pan¹  Eric Croiset¹  Lena Ahmadi¹  Ali Elkamel¹  Peter L. Douglas¹  Sabah A. Abdul-Wahab²  Evgueniy Entchev³ 

[1] Chemical Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada;Department of Mechanical and Industrial Engineering, College of Engineering,Sultan Qaboos University, P.O. Box 33, Al-Khod 123, Muscat, Sultanate of Oman;Energy Technology Centre, Natural Resources Canada, Ottawa, ON K1A 1M1, Canada;

关键词: plug-in hybrid electric vehicles;    mixed integer programing;    forecasting;    optimization;    energy planning;    power plants;    carbon management;   

DOI  :  10.3390/en8053978

来源: DOAJ

【 摘 要 】

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 CO2 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 CO2 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 CO2 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 CO2 emissions are the lowest: ~500 tonnes compared to ~900 tonnes when coal is permitted.

【 授权许可】

Unknown