Optimal Sizing and Siting of Distributed Generators for Minimization of Power Losses and Voltage Deviation

Agajie, Takele Ferede; Salau, Ayodeji Olalekan; Hailu, Engidaw Abel; Sood, Meenakshi; Jain, Shruti

doi:10.1109/ispcc48220.2019.8988401

Cited by 16 publications

(11 citation statements)

References 7 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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“…Power losses affect the entire power systems, from generation to transmission and to distribution; but this paper is focused mainly on losses in DSs. Losses in electrical power DSs include technical and non-technical losses [ 18 , 19 , 20 , 21 ]. The technical losses are related to the energy distribution process that occurs as a result of the physical nature of equipments and infrastructure of the power system, i.e., copper loss in conductor cables, transformer switches, and generators.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic based optimal placement of voltage regulators and capacitors for distribution systems efficiency improvement

Addisu

Salau

Takele

2021

Heliyon

Self Cite

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Electric power systems in most developing countries, especially Ethiopia, are fed by radial distribution systems (RDSs), which serve as the final link between the high voltage transmission system and the consumers. In this paper, a fuzzy logic optimization method was presented for the efficient location of voltage regulators (VR) and capacitors in distribution systems (DSs). An investigation of Gondar power distribution system was performed specifically at the Gondar feeder in Gondar, Ethiopia which has 60 nodes with a total capacity of 3.413 MW. The Gondar distribution system (DS) is classified into different zones for the purpose of proper operation and supply of electricity. The minimum bus voltage is 0.8515 p.u., while the active and reactive power losses of the feeder are 377.75kW and 222.51 KVAR respectively. Voltages and power loss (PL) indices of the DS nodes were modeled using fuzzy membership functions. A fuzzy expert system (FES) containing a set of heuristic rules was used to determine the voltage regulator (VR) and capacitor placement suitability index. The load flow analysis of the system was simulated using NEPLAN MATLAB software before and after recompense. The effectiveness of the results obtained from both the voltage regulator and capacitor placement optimality using the proposed method are presented and compared.

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“…The reviewed papers in [6,7,8,9,10,11,12,13], show that PDSs are greatly affected by power loss and poor VP [14]. A number of studies have used different approaches to reduce power loss in PDSs such as distributed generation placement [15], optimal capacitor placement [16], and electric distribution network reconfiguration (DNR) [17]. Out of these different approaches, the DNR approach has shown remarkable results for power loss reduction and VP improvement [5,6,7,8,9,10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems

Salau

Gebru

Bitew

2020

Heliyon

Self Cite

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This paper presents an optimal method for optimizing network reconfiguration (NR) problems in a power distribution system (PDS) for the purpose of power loss reduction and voltage profile (VP) improvement. Furthermore, a modified algorithm was presented to address this problem in order to provide a more efficient PDS. Various works which used NR to improve VP and reduce power loss were discussed and summarized in detail. In particular, a modified Selective particle swarm optimization (SPSO) method was used for NR in existing networks considering different loading conditions. The main objective of this study is to minimize real power losses and enhance VP of a distribution system using the proposed SPSO method. The SPSO method was programmed in MATLAB R2016b software and tested using IEEE 33-bus radial distribution system (RDS). The obtained test results show that the real power was enhanced by 99.341%, 97.289%, and 95.389% for the light, normal, and heavy load conditions, respectively. Also, the minimum voltage level in the worst case was significantly enhanced from 0.8841 p.u. to 0.9510 p.u. Towards the end, a comparative analysis of the proposed SPSO with existing methods for distribution network reconfiguration (DNR) is presented. The comparative results show that the proposed SPSO was found to be more efficient in reducing voltage deviation (VD) and power losses in the system.

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Optimal Sizing and Siting of Distributed Generators for Minimization of Power Losses and Voltage Deviation

Cited by 16 publications

References 7 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

Fuzzy logic based optimal placement of voltage regulators and capacitors for distribution systems efficiency improvement

Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems

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