Optimal allocation of plug-in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm

• Published in: IET science, measurement & technology Vol. 11; no. 8; pp. 1058 - 1070
• Main Authors: Feshki Farahani, Hassan, Rashidi, Farzan
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.11.2017
• Subjects: active power‐line conditioners; differential evolution based artificial bee colony algorithm; distribution system operator; electric vehicle charging; electric vehicles; hybrid optimisation algorithm; optimisation; optimisation framework; plug‐in electric vehicle capacity; Research Article; active power-line conditioners; optimisation; electric vehicles; electric vehicle charging; optimisation framework; plug-in electric vehicle capacity; differential evolution based artificial bee colony algorithm; distribution system operator; hybrid optimisation algorithm
• ISSN: 1751-8822; 1751-8830
• DOI: 10.1049/iet-smt.2016.0444

Plug-in electric vehicles (PEVs) can produce active, reactive and distorted power as well as pollution reduction. This study proposes an optimisation framework to allocate the PEVs capacity to generate each power component considering to grid and vehicle constraints, technical concerns and market price. In the proposed framework, PEVs compete with active power-line conditioners (APLCs) to generate distorted power and with generators to produce active and reactive power. An objective function is defined which includes distribution system operator (DSO) payment for each market participant. This function is minimised based on a hybrid optimisation algorithm (HOA) combining artificial bee colony (ABC) and differential evolution (DE) algorithms subject to grid and vehicles constraints. In the presented algorithm, a novel self-adaptive modification phase is proposed to improve overall ability of the algorithm for optimisation applications. The effectiveness and efficiency of the method is demonstrated on a low-voltage network with 134 customers as a case study.