Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance

This paper presents an innovative approach to enhance the efficiency of radial distribution networks by optimizing the placement of distributed generation (DG) units and network reconfiguration simultaneously while considering voltage-dependent load models, including constant power, constant current...

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Veröffentlicht in:	Electrical engineering Jg. 107; H. 4; S. 3909 - 3946
Hauptverfasser:	Maurya, Priyanka, Tiwari, Prabhakar, Pratap, Arvind
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2025 Springer Nature B.V
Schlagworte:	Algorithms Deviation Distributed generation Economics and Management Effectiveness Electric potential Electric power loss Electrical Engineering Electrical loads Electrical Machines and Networks Energy Policy Engineering Fractals Heuristic Heuristic methods Impact analysis Networks Neural networks Nodes Optimization Optimization algorithms Optimization techniques Original Paper Placement Power Electronics Power factor Radial distribution Reactive power Reconfiguration Systems stability Test systems Voltage Voltage-dependent load models Bus voltage deviation reduction Loss reduction Optimal placement of DG Hippopotamus optimization algorithm Network reconfiguration
ISSN:	0948-7921, 1432-0487
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Abstract	This paper presents an innovative approach to enhance the efficiency of radial distribution networks by optimizing the placement of distributed generation (DG) units and network reconfiguration simultaneously while considering voltage-dependent load models, including constant power, constant current, constant impedance, and composite load models. This study utilizes the Hippopotamus Optimization Algorithm (HOA), a novel approach inspired by the unique behaviors of hippopotamuses, for optimal DG planning and network reconfiguration. By mimicking the hippos' strategic positioning, defense mechanisms, and evasion techniques, HOA is used to optimize the weighted sum of multiple objective functions, including active and reactive power losses and bus voltage deviation. Additionally, the study analyzes the impact of various DG planning strategies with network reconfiguration on energy loss cost savings. The effectiveness of the HOA is demonstrated on 84-node practical and 141-node radial distribution networks. The results demonstrated that combining strategic DG placement with network reconfiguration significantly improved system performance across different voltage-dependent load models. This combined approach outperformed DG planning without reconfiguration under optimal power factor conditions, improving active power loss by 55.47%, reactive power loss by 55.85%, and bus voltage deviation by 47.86% in the 84-node network and 91.20%, 91.56%, and 78.50% in the 141-node network. Additionally, the efficacy of HOA is compared with practical swarm optimization, whale optimization, grasshopper optimization, zebra optimization, coot bird optimizer, and firefly algorithms. Overall, this approach significantly enhances the efficiency and reliability of power distribution networks, especially within complex power systems.
AbstractList	This paper presents an innovative approach to enhance the efficiency of radial distribution networks by optimizing the placement of distributed generation (DG) units and network reconfiguration simultaneously while considering voltage-dependent load models, including constant power, constant current, constant impedance, and composite load models. This study utilizes the Hippopotamus Optimization Algorithm (HOA), a novel approach inspired by the unique behaviors of hippopotamuses, for optimal DG planning and network reconfiguration. By mimicking the hippos' strategic positioning, defense mechanisms, and evasion techniques, HOA is used to optimize the weighted sum of multiple objective functions, including active and reactive power losses and bus voltage deviation. Additionally, the study analyzes the impact of various DG planning strategies with network reconfiguration on energy loss cost savings. The effectiveness of the HOA is demonstrated on 84-node practical and 141-node radial distribution networks. The results demonstrated that combining strategic DG placement with network reconfiguration significantly improved system performance across different voltage-dependent load models. This combined approach outperformed DG planning without reconfiguration under optimal power factor conditions, improving active power loss by 55.47%, reactive power loss by 55.85%, and bus voltage deviation by 47.86% in the 84-node network and 91.20%, 91.56%, and 78.50% in the 141-node network. Additionally, the efficacy of HOA is compared with practical swarm optimization, whale optimization, grasshopper optimization, zebra optimization, coot bird optimizer, and firefly algorithms. Overall, this approach significantly enhances the efficiency and reliability of power distribution networks, especially within complex power systems. This paper presents an innovative approach to enhance the efficiency of radial distribution networks by optimizing the placement of distributed generation (DG) units and network reconfiguration simultaneously while considering voltage-dependent load models, including constant power, constant current, constant impedance, and composite load models. This study utilizes the Hippopotamus Optimization Algorithm (HOA), a novel approach inspired by the unique behaviors of hippopotamuses, for optimal DG planning and network reconfiguration. By mimicking the hippos' strategic positioning, defense mechanisms, and evasion techniques, HOA is used to optimize the weighted sum of multiple objective functions, including active and reactive power losses and bus voltage deviation. Additionally, the study analyzes the impact of various DG planning strategies with network reconfiguration on energy loss cost savings. The effectiveness of the HOA is demonstrated on 84-node practical and 141-node radial distribution networks. The results demonstrated that combining strategic DG placement with network reconfiguration significantly improved system performance across different voltage-dependent load models. This combined approach outperformed DG planning without reconfiguration under optimal power factor conditions, improving active power loss by 55.47%, reactive power loss by 55.85%, and bus voltage deviation by 47.86% in the 84-node network and 91.20%, 91.56%, and 78.50% in the 141-node network. Additionally, the efficacy of HOA is compared with practical swarm optimization, whale optimization, grasshopper optimization, zebra optimization, coot bird optimizer, and firefly algorithms. Overall, this approach significantly enhances the efficiency and reliability of power distribution networks, especially within complex power systems.
Author	Tiwari, Prabhakar Pratap, Arvind Maurya, Priyanka
Author_xml	– sequence: 1 givenname: Priyanka surname: Maurya fullname: Maurya, Priyanka email: priya456maurya@gmail.com organization: Department of Electrical Engineering, Madan Mohan Malaviya University of Technology – sequence: 2 givenname: Prabhakar surname: Tiwari fullname: Tiwari, Prabhakar organization: Department of Electrical Engineering, Madan Mohan Malaviya University of Technology – sequence: 3 givenname: Arvind surname: Pratap fullname: Pratap, Arvind organization: Department of Electrical Engineering, Madan Mohan Malaviya University of Technology
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SubjectTerms	Algorithms Deviation Distributed generation Economics and Management Effectiveness Electric potential Electric power loss Electrical Engineering Electrical loads Electrical Machines and Networks Energy Policy Engineering Fractals Heuristic Heuristic methods Impact analysis Networks Neural networks Nodes Optimization Optimization algorithms Optimization techniques Original Paper Placement Power Electronics Power factor Radial distribution Reactive power Reconfiguration Systems stability Test systems Voltage
Title	Application of the hippopotamus optimization algorithm for distribution network reconfiguration with distributed generation considering different load models for enhancement of power system performance
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