Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

Chandrasekaran Venkatesan, Raju Kannadasan, Dhanasekar Ravikumar, Vijayaraja Loganathan, Mohammed H. Alsharif, Daeyong Choi, Junhee Hong and Zong Woo Geem
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Chandrasekaran Venkatesan: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, India
Raju Kannadasan: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, India
Dhanasekar Ravikumar: Electrical and Electronics Engineering, Sri Sairam Institute of Technology, West Tambaram, Chennai 600044, India
Vijayaraja Loganathan: Electrical and Electronics Engineering, Sri Sairam Institute of Technology, West Tambaram, Chennai 600044, India
Mohammed H. Alsharif: Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Korea
Daeyong Choi: School of Electrical Engineering, Chosun University, Gwangju 61452, Korea
Junhee Hong: College of IT Convergence, Gachon University, Seongnam 13120, Korea
Zong Woo Geem: College of IT Convergence, Gachon University, Seongnam 13120, Korea

Sustainability, 2021, vol. 13, issue 24, 1-28

Abstract: Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.

Keywords: available renewable energy potential (AREP); capacitor banks (CBs); distributed generations (DGs); enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO); power loss; voltage deviation index (VDI); voltage stability index (VSI) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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