Allocation of distributed generation and battery switching stations for electric vehicle using whale optimiser algorithm

• Main Authors: Sultana, Umbrin, Khairuddin, Azhar, Rasheed, Nadia, Qazi, Sajid Hussain, Mokhtar, Ahmad Safawi
• Format: Article
• Language: English
• Published: University of Kuwait 2018
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/85405/1/AzharKhairuddin2018_AllocationofDistributedGenerationandBatterySwitching.pdf; http://eprints.utm.my/id/eprint/85405/; https://kuwaitjournals.org/jer/index.php/JER/article/view/4833
• Institution: Universiti Teknologi Malaysia

With the increasing demand for electrical vehicles (EVs) in the existing distribution system due to road traffic sustainability, fuel costs reduction, and environmental improvement by the promotion of low carbons in transportation, system planners need to minimise energy losses and improve voltage profile of the grid. Few studies resolved these issues via optimum placement of distributed generation (DG) and battery switching station (BSS) units in distribution system; however, these techniques considered only active power loss minimisation with various methodological limitations. Therefore, a new application of whale optimiser algorithm (WOA) is proposed to solve these limitations. The simultaneous placement based approach of the units has been adopted to minimise active and reactive energy losses of 33- and 69-bus distribution systems. System performance has been analyzed based on multiple technical criteria, such as system loading factor, voltage profile improvement, and active and reactive power loss reduction indices. The results of WOA have been proven to be superior to those of artificial bee colony and gravitational search algorithms. Therefore, the proposed methodology can guide energy planners in determining optimal allocation of multiple DG and BSS units in their systems,; in addition to the expected energy loss reduction within the system, BSS, and DG planning and operational constraints.