Jul 2026· International Seminar on Intelligent Technology and Its Applications· pp. 140-145· 0 citations· 16 references
Abstract
Distribution network reconfiguration plays an important role in maintaining system performance during fault conditions while minimizing operational losses. This paper presents an Adaptive Modified Firefly Algorithm (AMFA) for determining optimal switching configurations in a distribution network under several feeder fault scenarios. The proposed method evaluates a total of 64 possible switching combinations and demonstrates fast convergence, reaching optimal or near-optimal solutions within only $1-3$ iterations. The results show a significant reduction in power losses from an initial condition of 0.5378 MW to as low as 0.0394 MW, while ensuring that all 14 general loads remain supplied. In addition to solution quality, the computational performance of the method is highly efficient, requiring only 5-7 seconds to obtain the optimal configuration. This is considerably faster than conventional manual operation, which typically takes 10-15 minutes and may not guarantee the minimum loss condition. The findings indicate that the proposed AMFA approach is capable of improving both the speed and accuracy of decision-making in distribution system reconfiguration, making it a practical solution for real-time applications under fault conditions.
Traditional centralized voltage control methods have limitations in real-time and adaptability. In order to improve the operation economy and voltage quality of the system, this paper proposes a voltage cooperative control and optimal scheduling strategy combining improved genetic algorithm. In this method, a mixed integer nonlinear optimization model is built with the goal of minimizing network loss, minimizing voltage deviation and minimizing operation cost of regulating equipment, and an annealing selection mechanism is introduced to significantly enhance the global optimization ability and convergence speed of the algorithm. Simulations on IEEE 33 nodes, 123 nodes and real European low voltage network data sets show that the proposed strategy can reduce the average voltage deviation of the system by 18.7%-32.4%, the total operating cost by 12.5%-21.3%, and the average optimization time by about 40% compared with the basic genetic algorithm and particle swarm optimization algorithm. To sum up, the proposed strategy can effectively coordinate photovoltaic inverters, on-load voltage regulating transformers, capacitor banks and other types of equipment, realize fast and economical voltage optimal dispatching, and provide reliable decision support for the safe and economical operation of distribution network under high penetration distributed energy access.
You Wen, Danni Chen, Ke Xiong et al.· International Conference on...· 0 citations
Aim: The aim of this research is to minimize power losses and improve the voltage profile of the Ado-Ekiti 33 kV feeders through optimal network reconfiguration using a Hybrid Genetic Algorithm–Particle Swarm Optimization technique.
Study Design: This study investigates the optimal reconfiguration of the Ado-Ekiti 33 kV radial distribution network using a Hybrid Genetic Algorithm–Particle Swarm Optimization (GA–PSO) approach for technical-loss reduction and voltage-profile improvement. A computational simulation and comparative optimization design was adopted.
Place and Duration of Study: Ado-Ekiti, Nigeria, from August 2025 to July 2026.
Methodology: The Ado-Ekiti network was modelled in MATLAB and evaluated using a Backward/Forward Sweep (BFS) load-flow procedure appropriate for radial distribution feeders. The optimization determined feasible switching configurations subject to bus-voltage, radiality, branch-current, power-balance, connectivity, and switch-status constraints, with a multi-objective fitness function combining active-power-loss and voltage-deviation terms. These constraints ensured that the optimal configuration obtained by the Hybrid GA–PSO algorithm was electrically feasible and operationally practicable. Performance was compared with that of standalone GA and PSO, and the framework was validated using the IEEE 33-bus benchmark.
Results: The Hybrid GA–PSO approach reduced total active power losses by 38.44%, from 2.094 MW to 1.289 MW, while improving the minimum bus voltage from 0.916 p.u. to above 0.95 p.u. The optimization achieved estimated annual energy savings of 3,173.31 MWh, approximately 3.17331 GWh/year, equivalent to approximately ₦131 million in economic benefits, demonstrating improved network efficiency and voltage performance.
Conclusion: The hybridization of GA and PSO effectively leveraged the global-search and diversity-preservation capabilities of GA and the rapid convergence ability of PSO, thereby addressing the individual limitations of each algorithm. The study demonstrates the potential of constrained network reconfiguration using Hybrid GA–PSO to reduce technical losses and improve voltage performance. Total active-power loss decreased from 2.094 MW to 1.289 MW, representing a 38.44% reduction, while the minimum bus voltage improved from 0.916 p.u. to approximately 0.962 p.u.
Oluwagbenga Samuel Alake, E. Fasina, B. Adebanji· Journal of Energy Research a...· 0 citations
This paper investigates the problem of multi-objective optimization of a distribution network using BESS. For a test network model, the placement and capacity of BESS were optimized based on criteria such as energy losses, as well as the reliability metrics SAIDI, SAIFI and eENS. Calculations were carried out using quasi-dynamic simulation and Monte Carlo simulation, whilst the NSGA-II, NSGA-III, CMOPSO and SMSEMOA algorithms were used as optimiza-tion methods. The paper presents a comparative analysis of the convergence and performance of meta-heuristic algo-rithms. It is shown that genetic algorithms ensure a more thorough exploration of the design space and generate a broader set of non-dominant solutions, but require greater computational resources. A statistically significant influence of the distance to network tie open points, the distance to power supply centers, and the topological centrality of nodes on the effectiveness of BESS deployment has been established. It is shown that the greatest positive effect from the use of BESS is achieved in remote and topologically peripheral network parts with limited restoration capabilities.
The results obtained allow for the development of an approach to the preliminary selection of candidate nodes prior to running computationally complex meta-heuristic algorithms, which makes it possible to significantly reduce the design space and accelerate network optimization through the deployment of BESSs. Ref. 8, fig. 4, tables 2.
T. Katsadze, D. Borsuk· Praci Institutu elektrodinam...· 0 citations
The increasing penetration of nonlinear loads and distributed generation (DG) in distribution networks poses significant challenges to voltage regulation and power loss minimization. Although Unified Power Quality Conditioner (UPQC), has proven effective in enhancing power quality and system stability, determining its optimal location and size remains a complex optimization problem. This study examines the optimal siting and sizing of UPQC in radial distribution systems with the objectives of minimizing real and reactive power losses while improving voltage profiles. A multi-objective optimization model is formulated and solved using the Firefly Algorithm (FA), with validation carried out on the IEEE 33-bus test system and the 71-bus University of Maiduguri feeder of Yola Electricity Distribution Company. For the IEEE 33-bus system, optimal UPQC placement at bus 5, achieved loss reductions from 201.775 kW and 134.991 kVAr to 56.478 kW (72.01%) real and 45.127 kVAr (66.57%) reactive and eliminated voltage violations. For the 71-bus feeder, UPQC placement at bus 39, reduced real and reactive losses from 306.586 kW and 289.050 kVAr to 144.902 kW (52.74%) real and 193.035 kVAR (33.34%) reactive and significantly mitigated voltage violations. Comparative analysis with the Artificial Bee Colony (ABC) algorithm shows that FA achieved faster convergence and offered slightly better loss reduction compared to ABC. It also delivered slightly lower UPQC ratings of 2705 kW and 1580 kW compared to ABC with 2720 kW and 1584 kW for the IEEE 33-bus test system and 71-bus University feeder, resulting in improved capacity economic efficiency. These findings demonstrate the effectiveness of the proposed FA method in accurately determining the optimal location and size of UPQC and highlight its strong potential for practical power quality enhancement in radial distribution systems.
Unknown authors· Zaria Journal of Electrical...· 0 citations
Integrating renewable energy sources into distribution networks can improve operational efficiency and reduce power losses. This study proposes a hybrid optimization method combining Archimedes Optimization Algorithm (AOA), Football Team Training Optimization Algorithm (FFTOA), Teaching-Learning Based Optimization Algorithm (TLBOA), and Starfish Optimization Algorithm (SFOA) to determine the optimal placement and sizing of photovoltaic units. The hybrid strategy enhances the original AOA by improving convergence speed and reducing the risk of local optima. FTTOA with Fitness Distance Balance helps select better guiding solutions, TLBOA supports information sharing and population diversity, and SFOA strengthens exploration and solution regeneration. A Beta distribution is used to model solar irradiance uncertainty. The proposed method is tested on the IEEE 33-bus distribution system for PV allocation. Results show that PV integration improves voltage profiles and reduces energy losses. Compared with other methods, the proposed approach achieves competitive performance and improved stability while maintaining reasonable computational cost.
V. Le, Hoang Nhan Nguyen, Vinh Tuong Tran et al.· E3S Web of Conferences· 0 citations
The increasing penetration of distributed generation (DG) has raised short-circuit current levels, potentially affecting the coordination of overcurrent relays (OCRs) used for distribution network protection. To address this issue, this study applies the grasshopper optimization algorithm (GOA) to optimize the time dial setting (TDS) with the objective of minimizing the total operating time of main relays while satisfying the required coordination time interval (CTI) constraints. The optimization results are compared with those obtained using particle swarm optimization (PSO) for three IEC standard inverse-time characteristics, and the selected characteristic is validated using ETAP simulations. The optimization results show that the Extremely Inverse (EI) characteristic achieves the minimum total main relay operating time of 3.0780 s while maintaining the minimum CTI requirement of 0.20 s. Furthermore, GOA produces optimization results comparable to PSO, with differences of less than 0.3% across all tested characteristics. These findings indicate that GOA is an effective alternative for improving OCR coordination while maintaining the required CTI standards.
Rizki Adi Wijaya, M. Pujiantara, Hendra Kusuma· International Seminar on Int...· 0 citations
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