Feiran Huang

Graduation Semester and Year

2010

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Wei-Jen Lee

Abstract

Plug-in hybrid vehicles are the most feasible approach towards significantly lowering the consumption of oil and improve fuel economy with today's existing technology. In Electric Power Research Institute and the Natural Resources Defense Council (EPRI-NRDC) 2007 study already proved that PHEVs will reduce emissions if they are broadly adopted. However, the charging infrastructures/station become key factor in the success of prevail of PHEVs.The research of this paper is focusing the operation of the PHEV charging station with battery storage units. The battery units conserve the low price clean energy and discharge when demanded. The on-site installed photovoltaic (PV) and off-site (virtual) wind farm are the main supply of charging station to charge the battery units. The grid electricity plays an auxiliary role in the station when the renewable sources are unavailable. The drastically changing market clearing price (MCP) in the deregulation market make it possible that station participates the power trade as storage device. The paper examines the PHEVs charging trend, forecasts wind power with artificial neural network (ANN) model and MCP with the statistical model, and proposes an optimized operation for the battery storage schedule and strategy of power trading to minimize the cost of station. Analysis based on level of forecast uncertainty is utilized to evaluate the optimization.

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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