Artificial Intelligence Based Load Scheduling for Plugged in Electric Vehicles in Smart Grid

Ubaid ur Rehman


Plugged in Electric Vehicles (PHEV’s) are enormously eco friendly and highly appreciated transportation system in various developed countries. The PHEV’s integration into the conventional grid required significant modifications in order to control load shedding, reducing unit cost, even out peak demands in quest to make a grid reliable. Recent research studies are mainly focusing to counter these issues by employing multi objective optimization techniques. The objective of this method is to reduce demand; energy cost and enhances the presence of PHEV’s for charging and discharging by creating substantial scheduling vector. This research work has proposed split scheduling vector to charge and discharge an EV, to achieve the required results by minimizing peak to average demand ratio (PAR) and generate profit for the owners by decreasing the total energy cost.

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Chief Editor

Prof. Dr. Tariq Rahim Soomro 
College of Computer Science & Information Systems

Editorial Advisory Board (Internal)

Dr. Syed Irfan Hyder
Dr. S.M. Aqil Burney
Dr. Ejaz Ahmed
Dr. Mohammad Irshad Khan
Dr. Shahid Amjad
Dr. Fatima Riaz
Dr. Insia Hussain
Dr. Ehsan Rehman
Dr. Imran Majid
Dr. Khurram Iqbal
Dr. Zeeshan Shahid
Dr. Seema Ansari
Dr. Muhammad Mansoor Alam


Editorial Advisory Board (International)

Prof. Dr. Mazliham Mohd Su'ud, President, Multimedia University, Malaysia

Dr. Eiad Yafi, Deputy Dean, Institute of Post Graduate Studies, Universiti Kuala Lumpur, Malaysia

Prof. Dr. Ghassan Al-Qaimari, President, Emirates College of Technology, Abu Dhabi, UAE
Prof. Dr. Patrice Boursier, Universite de La Rochelle, La Rochelle, France
Prof. Dr. Mudassir Uddin, Professor, University of Karachi, Pakistan
Dr. Nadeem Doudpota, Associate Professor, Al-Baha University, KSA
Dr. Haithem Abdelrazaq Almefleh, Associate Professor, Yarmouk University, Yarmouk, Jordan
Dr. Saiful Islam Ansari, Assistant Professor, University of Tabuk, Saudi Arabia