Designing an Electric Charging Station for Plug-In-Hybrid-Electric-Vehicles in the Face of Uncertain Demand

Abstract

This study is focused on the design of a Plug-in Hybrid Electric Vehicle (PHEV) charging station with battery storage. Wind and solar power as well as electricity from the grid are supplied to the charging station to charge the battery units. In order to generate profit, power can also be sold to the grid when there is an excess of power generated by wind or solar sources. The objective is to minimize PHEV charging cost while still storing an adequate amount of power in the battery to satisfy PHEV charging demand. This requires monitoring demand, the amount of power generated, and the market clearing price for energy (MCPE) and trading power with the power grid accordingly. In this model the various sources of energy are modeled independently and there is uncertainty in the generation of electricity by wind power, uncertainty in customer demand, and uncertainty in the MCPE. The proposed model is composed using a charging station in Dallas County, Texas. An artificial neural network (ANN) and auto regressive methods are used to forecast wind power output and MCPE, respectively. By generating electricity from renewable sources, charging stations may become active participants in the power market. This can both reduce the system load during peak demand and facilitate the deployment of PHEVs.