A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty

In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The ob...

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Vydané v:Scientific reports Ročník 14; číslo 1; s. 29532 - 31
Hlavní autori: Duan, Fude, Basem, Ali, Jasim, Dheyaa J., Eslami, Mahdiyeh, Okati, Mustafa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 27.11.2024
Nature Publishing Group
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639/166/4073/4071
639/166/4073/4099
639/166/987
639/4077
639/4077/4073
639/4077/4079
639/4077/4082
639/4077/893
639/4077/909
Convex lens imaging
Decision making
Distribution network
Energy resources
Energy sources
Humanities and Social Sciences
Multi-objective improved horse herd optimizer
multidisciplinary
Objective function
Optimization
Reliability
Science
Science (multidisciplinary)
Turbines
Unscented transformation
Wind power
Wind turbine allocation
Convex lens imaging
Unscented transformation
Wind turbine allocation
Reliability
Distribution network
Multi-objective improved horse herd optimizer
ISSN:2045-2322, 2045-2322
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Shrnutí:In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer. This enhancement utilizes mirror imaging based on convex lens principles to address issues of premature convergence. Additionally, the decision-making process is designed to identify the final fuzzy solution among the non-dominant solutions within the Pareto front set. The simulation results are presented with and without considering uncertainty in two scenarios of deterministic and stochastic WT allocation on 33- and 69-bus distribution networks and different objectives are compared. Also, the effect of incorporating uncertainties are evaluated on power loss and reliability using the MOIHHO. Moreover, the superiority of the MOIHHO is investigated in achieving better objective function value compared with conventional MOHHO, multi-objective particle swarm optimization (MOSPO), multi-objective gray wolf optimizer (MOGWO), and multi-objective gazelle optimization algorithm (MOGOA). The obtained results demonstrated that considering the UT-based stochastic scenario, the power losses cost is increased, and the reliability is weakened for 33- and 69-bus networks in comparison with the deterministic scenario.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-78977-0