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Ali Nahavandi

Ali Nahavandi

Academic rank: Assistant Professor
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Education: PhD.
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Faculty: Technical Engineering
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Research

Title
Optimal planning of plug-in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties
Type
JournalPaper
Keywords
genetic algorithms; power distribution planning; hybrid electric vehicles; power distribution economics; demand side management; power distribution reliability; battery powered vehicles; power markets; Monte Carlo methods; pricing
Year
2016
Journal IET Generation Transmission & Distribution
DOI
Researchers Ali Nahavandi

Abstract

This study presents a mathematical model to determine the optimal site and the size of plug-in hybrid electric vehicles charging stations (CSs) in the distribution networks. The presented optimal planning is done along with considering the rate of customers’ participation in demand response programs (DRPs) at the presence of some uncertainties associated with the load values and electricity market price. In order to model the load, time-of-use DRP is used. The proposed model involves distribution system manager (DSM) benefit maximising derived from the appropriate use of CS for charging and discharging vehicle batteries, reliability improvement and supplying network's load demand at peak times as the objective function, technical limitation as constraint and the siting and sizing of electric vehicles CS as optimisation variables. The genetic algorithm method with embedded Monte Carlo simulation is used in order to solve the optimisation problem and is applied to the 9-bus and 33-bus networks. The test results indicate that not only does the appropriate planning have an economic benefit for DSM, but also voltage profile and power supply reliability for customers can be improved and active power losses can also be reduced.