Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles

Zeeshan Anjum Memon, Dalila Mat Said (), Mohammad Yusri Hassan, Hafiz Mudassir Munir (), Faisal Alsaif and Sager Alsulamy
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Zeeshan Anjum Memon: Centre of Electrical Energy Systems (CEES), Institute of Future Energy (IFE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
Dalila Mat Said: Centre of Electrical Energy Systems (CEES), Institute of Future Energy (IFE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
Mohammad Yusri Hassan: Centre of Electrical Energy Systems (CEES), Institute of Future Energy (IFE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Johor, Malaysia
Hafiz Mudassir Munir: Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Sindh, Pakistan
Faisal Alsaif: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Sager Alsulamy: Energy & Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO16 7QF, UK

Sustainability, 2023, vol. 15, issue 9, 1-37

Abstract: The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.

Keywords: plug-in electric vehicle; power loss; AOASSA; loading capacity to power loss index (LCPLI) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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