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Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer

This paper proposes a solution to this non-convex, discrete problem by using the hybrid grey wolf optimizer, a new metaheuristic algorithm, applied to IEEE 33-, IEEE 69-, and Indian 85-bus radial distribution systems to minimize the power loss.
Optimal allocation of distributed generation units is essential to ensure power loss minimization, while meeting the real and reactive power demands in a distribution network. This paper proposes a solution to this non-convex, discrete problem by using the hybrid grey wolf optimizer, a new metaheuristic algorithm. This algorithm is applied to IEEE 33-, IEEE 69-, and Indian 85-bus radial distribution systems to minimize the power loss. The results show that there is a considerable reduction in the power loss and an enhancement of the voltage profile of the buses across the network. Comparisons show that the proposed method outperforms all other metaheuristic methods, and matches the best results by other methods, including exhaustive search, suggesting that the solution obtained is a global optimum. Furthermore, unlike for most other metaheuristic methods, this is achieved with no tuning of the algorithm on the part of the user, except for the specification of the population size.

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Grey wolf optimizer: a review of recent variants and applications

TL;DR: In this review paper, several research publications using GWO have been overviewed and summarized and the main foundation of GWO is provided, which suggests several possible future directions that can be further investigated.
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A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

TL;DR: A new Grey Wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods to reduce the number of selected features while preserving high classification accuracy.
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A Real-Time EV Charging Scheduling for Parking Lots With PV System and Energy Store System

TL;DR: The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms and can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.
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Optimal Planning of Renewable Energy-Integrated Distribution System Considering Uncertainties

TL;DR: A novel methodology to solve the problem of RES-DGs planning optimization based on improved Harris Hawks Optimizer (HHO) using Particle Swarm Optimization (PSO), which maximizes the techno-economic benefits of the distribution systems for all considered cases and scenarios is proposed.
Journal ArticleDOI

A Random Opposition-Based Learning Grey Wolf Optimizer

TL;DR: A modified parameter “C” strategy to balance between exploration and exploitation of GWO is presented and a new random opposition-based learning strategy is proposed to help the population jump out of the local optima.
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Grey Wolf Optimizer

TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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Differential Evolution: A Survey of the State-of-the-Art

TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
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Krill herd: A new bio-inspired optimization algorithm

TL;DR: The proposed KH algorithm, based on the simulation of the herding behavior of krill individuals, is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.
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A direct approach for distribution system load flow solutions

TL;DR: In this paper, a direct approach for unbalanced three-phase distribution load flow solutions is proposed, where two developed matrices, the bus-injection to branch-current matrix and the branchcurrent to busvoltage matrix, and a simple matrix multiplication are used to obtain load flow solution.
Journal ArticleDOI

Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction

TL;DR: An improved analytical (IA) method based on IA expressions to calculate the optimal size of four different DG types and a methodology to identify the best location for DG allocation is proposed, and a technique to get the optimal power factor is presented for DG capable of delivering real and reactive power.
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