Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

Thangaraj Yuvaraj, Natarajan Prabaharan (), Chinnappan John De Britto, Muthusamy Thirumalai, Mohamed Salem and Mohammad Alhuyi Nazari ()
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Thangaraj Yuvaraj: Centre for Smart Energy Systems, Chennai Institute of Technology, Chennai 600069, India
Natarajan Prabaharan: School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India
Chinnappan John De Britto: Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai 602105, India
Muthusamy Thirumalai: Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai 602105, India
Mohamed Salem: School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Malaysia
Mohammad Alhuyi Nazari: Faculty of New Sciences and Technologies, University of Tehran, Tehran 1417935840, Iran

Sustainability, 2024, vol. 16, issue 19, 1-34

Abstract: The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

Keywords: electrical vehicles; electric vehicle charging station; power loss; spotted hyena optimization algorithm; renewable distributed generation; distribution static compensator; radial distribution system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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