Maximization savings in distribution networks with optimal location of type-I distributed generator along with reconfiguration using PSO-DA optimization techniques

Rafi, Vempalle; Dhal, P.K.

doi:10.1016/j.matpr.2020.06.547

Cited by 18 publications

(13 citation statements)

References 10 publications

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“…In this paper, both DNR and DG allocation problem is solved simultaneously by applying a hybrid CS-GWO algorithm to obtain a desired optimal solution. [41], hypercube ant colony optimization algorithm [42], Fireworks Algorithm [43], modified plant growth simulation algorithm [44], adaptive cuckoo search [45], binary particular swarm optimization algorithm [46], enhanced evolutionary algorithm [47], uniform voltage distribution based constructive reconfiguration algorithm [48], discrete artificial bee colony algorithm [49], Harmony search algorithm (HSA) and particle artificial bee colony algorithm (PABC) [50], hybrid Grey Wolf Optimizer (GWO)-Sine Cosine Algorithm (SCA) [51], hybrid Particle Swarm Optimizer (PSO)-ant colony optimization (ACO) [52], comprehensive teaching-learning-based optimization algorithm [53], Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO) [54], Strength Pareto Evolutionary Algorithm 2 [55], adaptive shuffled frogs leaping algorithm [56], Particle swarm optimization (PSO) and Dragonfly algorithm (DA) [57], Stochastic fractal search algorithm [58], Mixed Particle Swarm Optimization [59], Symbiotic Organism Search Algorithm [60], Equilibrium optimization algorithm [61], Harris Hawks Optimization [62], Meta-heuristic matrix moth-flame algorithm [63], Bacterial Foraging with Spiral Dynamic (BF-SD) algorithm [64], chaotic stochastic fractal search algorithm [66], Modified Selective particle swarm optimization (SPSO) method [73], Grasshopper optimization algorithm (GOA) [74], Grid based Multi-Objective Harmony Search Algorithm (GrMHSA) [75], Modified Whale Optimization (MOWOA) algorithm and fuzzy decision-making method [77], Fuzzy Expert System (FES) method [78], Manta-Ray Foraging Optimization (MRFO) algorithm [79], Rider Optimization Algorithm [80]. The summary of rece...…”

Section: Related Workmentioning

confidence: 99%

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

Kumar¹,

Rudramoorthy²

2021

IJEEI

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This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problems. The obtained test results for the 33-bus system show that minimization of active power loss was enhanced by 74.73%,73.35%, and 80.37% for light, nominal, and heavy load conditions, respectively, and similarly for 69-bus system is 81.50%, 84.74%, and 88.86%. The minimum voltage value for 33-bus system under nominal load condition was enhanced from 0.9130 p.u to 0.9865 p.u and similarly for the 69-bus system is 0.9094 p.u to 0.9842 p.u. Respectively. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm with existing methods is presented. This method is tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios. Finally, the comparative analysis shows that the proposed algorithm was more efficient in minimizing power losses and enhancing the voltage profile of the system.

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Section: Related Workmentioning

confidence: 99%

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

Kumar¹,

Rudramoorthy²

2021

IJEEI

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“…[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] Among the recent studies that dealt with the best ways to implement this strategy. There are other strategies, such as system reconfiguration [27][28][29][30][31][32] as well as distributed generation, [33][34][35][36][37][38][39][40][41] that need high capital to proceed with their implementation, which may not be available. In recent years, series capacitors have been used high-voltage networks, and it gives many profits.…”

Section: Enhancing Distribution System Performance Strategiesmentioning

confidence: 99%

Optimum estimation of series capacitors for enhancing distribution system performance via an improved hybrid optimization algorithm

Mohamed

Abdelaziz

Darwish

et al. 2023

Energy Science & Engineering

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As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy-saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I-GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I-GWO has a movement strategy called dimension learning-based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I-GWO-TS) algorithm can solve mixed-integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high-power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors' location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system.

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“…The study in 28 employed RESs uncertainty and a β-chaotic sequence spotted hyena optimizer was proposed to allocate two wind turbines for losses reduction, improving voltage profile and stability index. Hybrid optimization techniques have been reported in 26,[29][30][31] which exploits the benefits of several optimizers with at a quicker convergence to the optimal solution and an improved the optimization efficiency. An Adaptive PSO (APSO) algorithm-based approach was applied in 32 for DG and capacitor bank allocation considering minimization of active and reactive power losses and maximizing the voltage profile.…”

Section: Introductionmentioning

confidence: 99%

“…Several metaheuristic-based optimization techniques are found in literature [23][24][25][26][27][28][29][30][31][32][33] and the performance of each technique varies in terms of accuracy and convergence time. Authors in 23 proposed bio-inspired algorithms to find the optimal mix of RES units with multi-objectives such as minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management Applying Load Elasticity Integrating Renewable Resources

Ragab

Ibrahim

Desouki

et al. 2023

Preprint

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Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with demand response (DR) for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Whale Optimization (WO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart.

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Maximization savings in distribution networks with optimal location of type-I distributed generator along with reconfiguration using PSO-DA optimization techniques

Cited by 18 publications

References 10 publications

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

Optimum estimation of series capacitors for enhancing distribution system performance via an improved hybrid optimization algorithm

Optimal Energy Management Applying Load Elasticity Integrating Renewable Resources

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