Optimizing network reconfiguration to reduce power loss and improve the voltage profile in the distribution system: A practical case study

Musaruddin, Mustarum; Tambi, Tambi; Zulkaidah, Waode; Masikki, Gamal Abdel Nasser; Lolok, Agustinus; Djohar, Abdul; Marwan, Marwan

doi:10.1016/j.prime.2024.100599

e-Prime - Advances in Electrical Engineering, Electronics and E

2024

DOI: 10.1016/j.prime.2024.100599

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Optimizing network reconfiguration to reduce power loss and improve the voltage profile in the distribution system: A practical case study

Mustarum Musaruddin,

Tambi Tambi,

Waode Zulkaidah

et al.

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2024

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Cited by 2 publications

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Network Reconfiguration Using Hybrid Archimedes Optimization Algorithm for Multiobjective Functions

Adegoke,

Sun,

Wang

et al. 2024

Journal of Electrical and Computer Engineering

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Extension of the feeders and high power demand have resulted in more substantial power dissipation and reduced voltage decline. Therefore, minimizing power loss and enhancing the voltage profile are imperative for efficient energy distribution. This research introduces a novel approach called the Hybrid Archimedes Optimization Algorithm (HAOA), which combines the Levy flight and Archimedes Optimization Algorithm (AOA) to improve the performance of AOA. AOA replicated the upward impulse described by Archimedes principle in physics and experienced an imbalance between exploitation and exploration. Utilizing Levy flight results in an improved balance between exploration and exploitation, enhancing algorithm performance in finding optimal solutions. Therefore, hybridizing Levy flight with AOA to enhance its performance results in an improved trajectory balance between exploration and exploitation. This trajectory is utilized to update the object’s position in AOA and is regarded as a feasible method to enhance the slow performance of AOA. The HAOA algorithm was employed to optimize network reconfiguration to decrease power loss and voltage deviation (VD) and improve the voltage stability index (VSI) in radial distribution networks (RDN). The validation of HAOA was assessed using the IEEE 33 and 69 bus systems. The power loss for the IEEE 33 and 69 test systems dropped to 139.55 and 98.459 kW, respectively, compared to the base case values of 202.677 and 224.96 kW. The VD for the 33 and 69 bus systems is 0.00134 and 0.000306 p.u, respectively, compared to the base case values of 0.0035498 and 0.001443 p.u. The VSI has been enhanced to 0.785 p.u for the 33 bus system and 0.8126 p.u for the 69 bus system following the reconfiguration. The results indicate that HAOA outperforms other approaches in terms of lower power, VD, and voltage stability enhancement.

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Network Reconfiguration Using Hybrid Archimedes Optimization Algorithm for Multiobjective Functions

Adegoke,

Sun,

Wang

et al. 2024

Journal of Electrical and Computer Engineering

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Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

Sadaoui,

Bouhadouza,

Hachemi

2024

SEES

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This paper presents a planning strategy for integrating renewable distributed generation (DG) units into a distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing a novel meta-heuristic algorithm, the Blood-Sucking Leech Optimizer (BSLO), the study addresses a multi-objective optimization problem aimed at determining the optimal placement and sizing of DG units, as well as the most effective network topology. This approach seeks to minimize active power losses, improve voltage profiles, reduce installation costs, and lower greenhouse gas emissions. The model accounts for variable load demands, climatic factors (such as ambient temperature, solar irradiation, and wind speed), and fluctuating energy prices, reflecting realistic operating conditions. Tested on the IEEE 69-bus distribution network, the BSLO algorithm demonstrated rapid convergence to the global optimum by effectively balancing exploration and exploitation phases. Compared to other meta-heuristic methods, such as the Grey Wolf Optimizer, Gorilla Troops Optimizer, Walrus Optimization Algorithm, and Artificial Hummingbird Algorithm, the BSLO consistently achieved superior accuracy and faster convergence, resulting in higher precision and optimization efficiency. The optimal deployment of two PV generators and two wind turbines, combined with selective line switch openings, resulted in an 87.66% reduction in active power losses, a 73.30% decrease in voltage deviation, a 51.91% reduction in overall system costs, and a 62.74% decrease in greenhouse gas emissions compared to the base case.

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Optimizing network reconfiguration to reduce power loss and improve the voltage profile in the distribution system: A practical case study

Cited by 2 publications

References 50 publications

Network Reconfiguration Using Hybrid Archimedes Optimization Algorithm for Multiobjective Functions

Network Reconfiguration Using Hybrid Archimedes Optimization Algorithm for Multiobjective Functions

Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

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