Thesis
Optimization application and effect analysis of V2G in electric vehicles
Washington State University
Master of Science (MS), Washington State University
05/2020
DOI:
https://doi.org/10.7273/000004200
Handle:
https://hdl.handle.net/2376/125024
Abstract
Vehicle-to-grid (V2G) has the potential to offer financial benefits to Electrical Vehicles (EVs) owners and electricity utilities since excessive electricity supply places a heavy load on the electric system while V2G is suggested as a solution for peak shaving through the emerging demand response or ancillary service program in the wholesale electricity market. The battery pack of the EVs are considered as an energy storage device providing power and energy services to the power grid. Considering the fluctuation of power grid load and the electricity costs of electric vehicles for end-users simultaneously are urgent needs for the power system operators. In this paper, a Mixed Integer Linear Programming (MILP) formulation is developed to optimize the charging and discharging of EVs based on the actual electric vehicle running data. The user's stochastic driving habits including arrival and departure times and their EVs' initial State of Charge (SOC), and their charging demand are also investigated by using Monte Carlo method to simulate the EVs usage. The demonstration results show that when V2G technology is used with less battery degradation, proposed method can improve the load fluctuation situation and reduce end-user electricity costs even earn a profit in the meanwhile.
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Details
- Title
- Optimization application and effect analysis of V2G in electric vehicles
- Creators
- Jiachen Fan
- Contributors
- Feng Zhao (Advisor) - Washington State University, Engineering and Computer Science (VANC), School of
- Awarding Institution
- Washington State University
- Academic Unit
- Engineering and Computer Science (VANC), School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Identifiers
- 99900896438001842
- Language
- English
- Resource Type
- Thesis