Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer
TL;DR: 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.
Abstract: 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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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.
Abstract: Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.
522 citations
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.
Abstract: Because of their high dimensionality, dealing with large datasets can hinder the data mining process. Thus, the feature selection is a pre-process mandatory phase for reducing the dimensionality of datasets through using the most informative features and at the same time maximizing the classification accuracy. This paper proposes 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. The sigmoid function is used to transform the continuous search space to the binary one in order to match the binary nature of the feature selection problem. The two-phase mutation enhances the exploitation capability of the algorithm. The purpose of the first mutation phase is to reduce the number of selected features while preserving high classification accuracy. The purpose of the second mutation phase is to attempt to add more informative features that increase the classification accuracy. As the mutation phase can be time-consuming, the two-phase mutation can be done with a small probability. The wrapper methods can give high-quality solutions so we use one of the most famous wrapper methods which called k-Nearest Neighbor (k-NN) classifier. The Euclidean distance is computed to search for the k-NN. Each dataset is split into training and testing data using K-fold cross-validation to overcome the overfitting problem. Several comparisons with the most famous and modern algorithms such as flower algorithm, particle swarm optimization algorithm, multi-verse optimizer algorithm, whale optimization algorithm, and bat algorithm are done. The experiments are done using 35 datasets. Statistical analyses are made to prove the effectiveness of the proposed algorithm and its outperformance.
213 citations
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.
Abstract: The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.
85 citations
Cites background from "Optimal Allocation of Distributed G..."
...GWO is used to study complex and constrained problems in recent years and it is proved to be a suitable solution [27], [28]....
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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.
Abstract: Optimal planning of renewable energy-based DG units (RE-DGs) in active distribution systems (ADSs) has many positive technical and economical implications and aim to increase the overall system performance. The optimal allocation and sizing of RE-DGs, particularly photovoltaic (PV) and wind turbine (WT), is still a challenging task due to the stochastic behavior of renewable resources. This paper proposed a novel methodology to solve the problem of RES-DGs planning optimization based on improved Harris Hawks Optimizer (HHO) using Particle Swarm Optimization (PSO). The uncertainties associated with the intermittent behaviour of PV and WT output powers are considered using appropriate probability distribution functions. The optimization problem is formulated as a non-linear constrained optimization problem with multiple objectives, where power loss reduction, voltage improvement, system stability, and yearly economic saving have been taken as the optimization objectives taken into account various operational constraints. The proposed methodology, namely HHO-PSO, has validated on three test systems; standard IEEE 33 bus and 69 bus systems and 94 bus practical distribution system located in Portuguese. The obtained results reveal that the HHO-PSO provide better solutions and maximizes the techno-economic benefits of the distribution systems for all considered cases and scenarios. Furthermore, simulation results are evaluated by comparing to those well-known approaches reported in the recent literature.
73 citations
Cites background or methods from "Optimal Allocation of Distributed G..."
...The planning problem of RE-DGs can be considered as a constrained, non-linear, discrete optimization problem [35]....
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...The authors in [35] applied HGWO to find the potential solution of DGs placement problem where primary outcomes reveal technical benefit related to the distribution system....
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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.
Abstract: Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real world applications. In the GWO algorithm, “C”is an important parameter which favoring exploration. At present, the researchers are few study the parameter “C”in GWO algorithm. In addition, during the evolution process, the other individuals in the population move towards to the α, β, and δ wolves which are to accelerate convergence. However, GWO is easy to trap in the local optima. This paper presents a modified parameter “C”strategy to balance between exploration and exploitation of GWO. Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima. The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.
67 citations
Cites methods from "Optimal Allocation of Distributed G..."
...[28] proposed a novel hybrid GWO algorithm based on crossover and mutation operators for optimizing the configuration of distributed generator units....
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References
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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.
10,082 citations
"Optimal Allocation of Distributed G..." refers methods in this paper
...THE GREY WOLF OPTIMIZER The grey wolf optimizer (GWO) is a swarm intelligence algorithm introduced by Mirjalili, Mirjalili, and Lewis in 2014 [31], that does not require any tuning on the part of the user....
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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.
Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents 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. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.
4,321 citations
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...The uniform or binomial crossover proposed in [33] is used here....
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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.
1,556 citations
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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.
Abstract: A direct approach for unbalanced three-phase distribution load flow solutions is proposed in this paper. The special topological characteristics of distribution networks have been fully utilized to make the direct solution possible. Two developed matrices-the bus-injection to branch-current matrix and the branch-current to bus-voltage matrix-and a simple matrix multiplication are used to obtain load flow solutions. Due to the distinctive solution techniques of the proposed method, the time-consuming LU decomposition and forward/backward substitution of the Jacobian matrix or Y admittance matrix required in the traditional load flow methods are no longer necessary. Therefore, the proposed method is robust and time-efficient. Test results demonstrate the validity of the proposed method. The proposed method shows great potential to be used in distribution automation applications.
880 citations
"Optimal Allocation of Distributed G..." refers methods in this paper
...The fitness of each solution is determined by substituting this in the fitness function given by (4) and executing a load flow by the direct approach proposed by Teng [35]....
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...[35] J.-H. Teng, ‘‘A direct approach for distribution system load flow solutions,’’ IEEE Trans....
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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.
Abstract: This paper investigates the problem of multiple distributed generator (DG units) placement to achieve a high loss reduction in large-scale primary distribution networks. An improved analytical (IA) method is proposed in this paper. This method is 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. A technique to get the optimal power factor is presented for DG capable of delivering real and reactive power. Moreover, loss sensitivity factor (LSF) and exhaustive load flow (ELF) methods are also introduced. IA method was tested and validated on three distribution test systems with varying sizes and complexity. Results show that IA method is effective as compared with LSF and ELF solutions. Some interesting results are also discussed in this paper.
689 citations
"Optimal Allocation of Distributed G..." refers methods in this paper
...Reference [6] proposes an analytical expression, [7] an improved analytical (IA) method, and [8] a dual index analytical approach....
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