Open Access

ARTICLE

Optimization of Reconfiguration and Resource Allocation for Distributed Generation and Capacitor Banks Using NSGA-II: A Multi-Scenario Approach

Tareq Hamadneh¹, Belal Batiha², Frank Werner^3,*, Mehrdad Ahmadi Kamarposhti^4,*, Ilhami Colak⁵, El Manaa Barhoumi⁶

1 Department of Mathematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Department of Mathematics, Faculty of Science and Information Technology, Jadara University, Irbid, 21110, Jordan
3 Faculty of Mathematics, Otto-von-Guericke University, Magdeburg, 39016, Germany
4 Department of Electrical Engineering, Jo.C., Islamic Azad University, Jouybar, Iran
5 Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey
6 Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Salalah, Oman

* Corresponding Authors: Frank Werner. Email: ; Mehrdad Ahmadi Kamarposhti. Email: ,

Computer Modeling in Engineering & Sciences 2025, 143(2), 1519-1548. https://doi.org/10.32604/cmes.2025.063571

Received 18 January 2025; Accepted 30 April 2025; Issue published 30 May 2025

Abstract

Reconfiguration, as well as optimal utilization of distributed generation sources and capacitor banks, are highly effective methods for reducing losses and improving the voltage profile, or in other words, the power quality in the power distribution system. Researchers have considered the use of distributed generation resources in recent years. There are numerous advantages to utilizing these resources, the most significant of which are the reduction of network losses and enhancement of voltage stability. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Intersect Mutation Differential Evolution (IMDE) algorithms are used in this paper to perform optimal reconfiguration, simultaneous location, and capacity determination of distributed generation resources and capacitor banks. Three scenarios were used to replicate the studies. The reconfiguration of the switches, as well as the location and determination of the capacitor bank’s optimal capacity, were investigated in this scenario. However, in the third scenario, reconfiguration, and determining the location and capacity of the Distributed Generation (DG) resources and capacitor banks have been carried out simultaneously. Finally, the simulation results of these three algorithms are compared. The results indicate that the proposed NSGAII algorithm outperformed the other two multi-objective algorithms and was capable of maintaining smaller objective functions in all scenarios. Specifically, the energy losses were reduced from 211 to 51.35 kW (a 75.66% reduction), 119.13 kW (a 43.54% reduction), and 23.13 kW (an 89.04% reduction), while the voltage stability index (VSI) decreased from 6.96 to 2.105, 1.239, and 1.257, respectively, demonstrating significant improvement in the voltage profile.

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