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MARKET DOMINANT PLUG-IN HYBRID ELECTRIC VEHICLES OPTIMAL CONTROL FOR MINIMUM CHARGING COST AND V2G REGULATION SERVICE
Title
MARKET DOMINANT PLUG-IN HYBRID ELECTRIC VEHICLES OPTIMAL CONTROL FOR MINIMUM CHARGING COST AND V2G REGULATION SERVICE
Creator
Li, Zhihao
Date
2012-07-23, 2012-07
Description
Plug-in hybrid electric vehicles (PHEVs) share the characteristics of hybrid electric vehicles (HEVs) and electric vehicles (EVs), employing...
Show morePlug-in hybrid electric vehicles (PHEVs) share the characteristics of hybrid electric vehicles (HEVs) and electric vehicles (EVs), employing electric motors and internal combustion engines (ICEs) for propulsion as well as large capacity batteries for energy storage. With ICEs and fuel tank on board, PHEVs do not have the range limitations posed by EVs; large capacity battery promises long distance all-electric range (AER) and fuel efficiency improvement. PHEVs will play a vital role in future as a sustainable transportation system, promising for environment, energy solution, and economy. It is estimated that by 2015, the total number of PHEVs in the world will be approximately 1.7 million with the U.S. marking leading the industry with about one million PHEVs. Growing penetration of PHEVs will place significant impacts on the grid, either as additional electric loads or potential assets which could provide various vehicleto- grid (V2G) services. There are four potential grid services that PHEVs can provide: base load generation, peak load shaving, spinning reserve, and regulation. PHEVs are not competitive in base load or peak load markets due to limited battery capacities. In addition, PHEVs are not real generating units and the energy stored in batteries is absorbed from grid. V2G support is taken into account as frequency regulation by participating in ancillary service markets. However, if implemented without proper control, large scale PHEVs will cause increases of peak load and destabilize the grid. This paper proposes an optimization strategy to maximize V2G profits as well as to minimize charging costs. The optimization strategy is based on a forecast of future electricity price for both residential electricity and regulation market. Due to the stochastic nature of electricity price, final prices cannot be deterministically calculated. Therefore, the addressed problem is solved by stochastic dynamic programming to find the economically optimal solution with price uncertainties. Constraints caused by vehicle utilization as well as technical limitations are taken into account. Additional costs arising from discharging batteries for ancillary service can be partially or completely compensated by V2G profits. In this Ph.D. research work, economical impacts of PHEV fleet are examined in Pennsylvania Jersey Maryland (PJM) regulation market. The major contributions of this paper are: Mathematically model the optimal control of PHEV with comprehensive transition function and cost function; A full study of battery life and cost that considers different ageing factors; A stochastic study of uncertainty and volatility in electricity price; Include battery degradation and price uncertainty in the comprehensive function for optimal control.
Ph.D. in Electrical Engineering, July 2012
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