Open AccessOptimal planning of plug‐in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties
Author(s) -
Shojaabadi Saeed,
Abapour Saeed,
Abapour Mehdi,
Nahavandi Ali
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0312
Subject(s) - sizing , demand response , reliability (semiconductor) , electric vehicle , genetic algorithm , monte carlo method , automotive engineering , electricity , computer science , constraint (computer aided design) , reliability engineering , mathematical optimization , power (physics) , simulation , engineering , electrical engineering , physics , quantum mechanics , mechanical engineering , art , statistics , mathematics , machine learning , visual arts
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.