A new Pareto multi-objective sine cosine algorithm for performance enhancement of radial distribution network by optimal allocation of distributed generators

The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution systems in the recent deregulation caused by continuous load demand and transmission system contingency. In this perspective, a new multi-obje...

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Vydáno v:	Evolutionary intelligence Ročník 14; číslo 4; s. 1635 - 1656
Hlavní autoři:	Raut, Usharani, Mishra, Sivkumar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021 Springer Nature B.V
Témata:	Applications of Mathematics Artificial Intelligence Bioinformatics Contingency Control Deregulation Distributed generation Electric power distribution Electric power loss Energy costs Energy dissipation Engineering Evolutionary algorithms Generators Genetic algorithms Mathematical and Computational Engineering Mechatronics Multiple objective analysis Particle swarm optimization Performance enhancement Pollutants Radial distribution Rank tests Research Paper Robotics Sorting algorithms Statistical Physics and Dynamical Systems System effectiveness Trigonometric functions Voltage stability Power loss Voltage stability Pareto front Non-dominated solutions Distributed generation
ISSN:	1864-5909, 1864-5917
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Abstract	The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution systems in the recent deregulation caused by continuous load demand and transmission system contingency. In this perspective, a new multi-objective sine cosine algorithm is proposed for optimal DG allocation in radial distribution systems with minimization of total active power loss, maximization of voltage stability index, minimization of annual energy loss costs as well as pollutant gas emissions without violating the system and DG operating constraints. The proposed algorithm is enhanced by incorporating exponential variation of the conversion parameter and the self-adapting levy mutation to increase its performance during different iteration phases. The contradictory relationships among the objectives motivate the authors to generate an optimal Pareto set in order to help the network operators in taking fast appropriate decisions. The proposed approach is successfully applied to 33-bus and 69-bus distribution systems under four practical load conditions and is evaluated in different two-objective and three-objective optimization cases. The effectiveness of the algorithm is confirmed by comparing the results against other well-known multi-objective algorithms, namely, strength Pareto evolutionary algorithm 2, non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. The quality of Pareto fronts from different multi-objective algorithms is compared in terms of certain performance indicators, such as generational distance, spacing metric and spread metric ( Δ ), and its statistical significance is verified by performing Wilcoxon signed rank test.
AbstractList	The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution systems in the recent deregulation caused by continuous load demand and transmission system contingency. In this perspective, a new multi-objective sine cosine algorithm is proposed for optimal DG allocation in radial distribution systems with minimization of total active power loss, maximization of voltage stability index, minimization of annual energy loss costs as well as pollutant gas emissions without violating the system and DG operating constraints. The proposed algorithm is enhanced by incorporating exponential variation of the conversion parameter and the self-adapting levy mutation to increase its performance during different iteration phases. The contradictory relationships among the objectives motivate the authors to generate an optimal Pareto set in order to help the network operators in taking fast appropriate decisions. The proposed approach is successfully applied to 33-bus and 69-bus distribution systems under four practical load conditions and is evaluated in different two-objective and three-objective optimization cases. The effectiveness of the algorithm is confirmed by comparing the results against other well-known multi-objective algorithms, namely, strength Pareto evolutionary algorithm 2, non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. The quality of Pareto fronts from different multi-objective algorithms is compared in terms of certain performance indicators, such as generational distance, spacing metric and spread metric ( Δ ), and its statistical significance is verified by performing Wilcoxon signed rank test. The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution systems in the recent deregulation caused by continuous load demand and transmission system contingency. In this perspective, a new multi-objective sine cosine algorithm is proposed for optimal DG allocation in radial distribution systems with minimization of total active power loss, maximization of voltage stability index, minimization of annual energy loss costs as well as pollutant gas emissions without violating the system and DG operating constraints. The proposed algorithm is enhanced by incorporating exponential variation of the conversion parameter and the self-adapting levy mutation to increase its performance during different iteration phases. The contradictory relationships among the objectives motivate the authors to generate an optimal Pareto set in order to help the network operators in taking fast appropriate decisions. The proposed approach is successfully applied to 33-bus and 69-bus distribution systems under four practical load conditions and is evaluated in different two-objective and three-objective optimization cases. The effectiveness of the algorithm is confirmed by comparing the results against other well-known multi-objective algorithms, namely, strength Pareto evolutionary algorithm 2, non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. The quality of Pareto fronts from different multi-objective algorithms is compared in terms of certain performance indicators, such as generational distance, spacing metric and spread metric (Δ), and its statistical significance is verified by performing Wilcoxon signed rank test.
Author	Raut, Usharani Mishra, Sivkumar
Author_xml	– sequence: 1 givenname: Usharani surname: Raut fullname: Raut, Usharani organization: Department of Electrical Engineering, International Institute of Information Technology – sequence: 2 givenname: Sivkumar orcidid: 0000-0002-9749-0581 surname: Mishra fullname: Mishra, Sivkumar email: sivmishra@gmail.com, capgs.smishra@bput.ac.in organization: Department of Electrical Engineering, Centre for Advanced Post Graduate Studies, Biju Pattnaik University of Technology
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Snippet	The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution...
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SubjectTerms	Applications of Mathematics Artificial Intelligence Bioinformatics Contingency Control Deregulation Distributed generation Electric power distribution Electric power loss Energy costs Energy dissipation Engineering Evolutionary algorithms Generators Genetic algorithms Mathematical and Computational Engineering Mechatronics Multiple objective analysis Particle swarm optimization Performance enhancement Pollutants Radial distribution Rank tests Research Paper Robotics Sorting algorithms Statistical Physics and Dynamical Systems System effectiveness Trigonometric functions Voltage stability
Title	A new Pareto multi-objective sine cosine algorithm for performance enhancement of radial distribution network by optimal allocation of distributed generators
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