I. J. of Electrical & Electronics Research
Research Article | 
A New Electric EEL Foraging Optimization Technique for Multi-Objective PV Unit Allocation in the Context of PHEV Charging Demand
Author(s): A. Manjula* and G. Yesuratnam
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 12, Issue 3
Publisher : FOREX Publication
Published : 20 July 2024
e-ISSN : 2347-470X
Page(s) : 734-739
Abstract
The existing electric distribution system is under tremendous stress due to reasons like power efficacy & voltage profile, load growth, radial structure etc. Additionally, electric load demand due to PHEVs worsens the existing distribution system performance. Planning of DGs in distribution system is one of the potential solutions for improving existing distribution system performance without changing its infrastructure. Therefore, the primary objective of this research is to determine the optimal way to allocate photovoltaic (PV) based distributed generators (DGs) inside radial distribution networks while taking into account the load demands of both conventional and PHEVs. In the study, three key technical metrics of the distribution network are improved via optimal planning of PV units: maximizing the voltage stability index, minimizing total voltage variation, and minimizing energy loss. Mathematically, weighted objective function is formulated for dealing the above-citied technical metrics. The weighted objective function is optimized using recently developed robust electric eel foraging optimization (EEFO) algorithm. The study first looks into how PHEV load demand affects the technical aspects of the distribution system. Subsequently, improvement of distribution system (accommodating both conventional and PHEVs load demand) performance via optimal planning PV units is discussed. IEEE-69 bus system is considered as a test system in this study. In addition, simulations utilising the differential evolution (DE) and grey wolf optimisation (GWO) methods are carried out in order to examine the robustness of the EEFO approach. The results of these comparisons are described in detail.
Keywords: Distributed generators (DGs), Electric EEL foraging optimization (EEFO) algorithm, Plug-in hybrid electric vehicles (PHEVs), Photovoltaic (PV).
A. Manjula*, Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, India; Email: manjulabanda89@gmail.com
G. Yesuratnam, Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, India; Email: ratnamgy2003@gmail.com
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