Optimal Placement of Capacitor Bank in Reorganized Distribution Networks Using Genetic Algorithm

Bakker, Emma; Deljou, Vahid; Rahmani, Javad

doi:10.7753/ijcatr0804.1002

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Test Network References C1 C2 C3 C4 C5 C6 C7 C8 C9 OF1 X X -------38 bus-Roy-Billinton Test System [8] X -X -X X ---IEEE 30, 57, 118 and 300 bus [10,27,29] X -X -X ----IEEE 33 bus [13] X X X X -----IEEE 33 and 94 bus [14,15] X X X -X X ---IEEE 30 bus [21] X X X -X ----IEEE 33 and 85 bus [25,28] X X X X X ----IEEE 33 and 119 bus [5] OF2 X --------IEEE 10, 23 and 34 bus [12] X -X -X ----IEEE 22, 69, 85 and 141 bus [32] OF3 X -X -X X ---IEEE 30, 57, 118 and 300 bus [10,27] X X X -X X ---IEEE 30 bus [21] OF4 X -X ---X -X IEEE 10, 33 and 69 bus [16,17,22,26] X -X ----X -IEEE 10, 15 and 34 bus [19] X -X ------IEEE 30 and 85 bus [20] X -X ------IEEE 33, 34, 69 and 85 bus [23,27] X -X -X -X -X IEEE 85 and 118 bus [24] OF5 X ---X ----IEEE 28-bus [11] X --------IEEE 9-bus [30] OF6 X -X --X X --IEEE 30 bus [18] X -X -X X X --IEEE 30, 57 and 118 bus [27] X -X -X ----IEEE 30, 118 and 300 bus [29] Energies 2019, 12, 4239 4 of 36…”

Section: Constraintsmentioning

confidence: 99%

“…The newest SWA algorithm gives the best results, for both test systems. Energies 2019, 12, 4239 2 of 36 can be grouped in four main categories: numerical [3]; analytical [4]; heuristic [5][6][7]; and artificial intelligence, population based (Artificial Neural Networks, metaheuristics) [8,9]. An overview about the metaheuristics used for the problem of capacitor banks allocation is made in the following, highlighting their specific purpose.…”

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confidence: 99%

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Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

et al. 2019

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Energy losses and bus voltage levels are key parameters in the operation of electricity distribution networks (EDN), in traditional operating conditions or in modern microgrids with renewable and distributed generation sources. Smart grids are set to bring hardware and software tools to improve the operation of electrical networks, using state-of the art demand management at home or system level and advanced network reconfiguration tools. However, for economic reasons, many network operators will still have to resort to low-cost management solutions, such as bus reactive power compensation using optimally placed capacitor banks. This paper approaches the problem of power and energy loss minimization by optimal allocation of capacitor banks (CB) in medium voltage (MV) EDN buses. A comparison is made between five metaheuristic algorithms used for this purpose: the well-established Genetic Algorithm (GA); Particle Swarm Optimization (PSO); and three newer metaheuristics, the Bat Optimization Algorithm (BOA), the Whale Optimization Algorithm (WOA) and the Sperm-Whale Algorithm (SWA). The algorithms are tested on the IEEE 33-bus system and on a real 215-bus EDN from Romania. The newest SWA algorithm gives the best results, for both test systems.

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Section: Constraintsmentioning

confidence: 99%

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confidence: 99%

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

et al. 2019

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“…The genetic algorithm applies a set of solutions to the optimization problem in each generation. The selection process chooses the individuals with the best fitness; these individuals mutate and reproduce new genes [20][21][22][23][24][25][26]. Therefore, the best optimum solutions are attained through mimicking the natural process genes mutation, selection, and reproduction.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimization of Lift Gas Allocation using Evolutionary Algorithms

López¹,

Koç²,

Bakker³

et al. 2019

IJCATR

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In this paper, the particle swarm optimization (PSO) algorithm is proposed to solve the lift gas optimization problem in the crude oil production industry. Two evolutionary algorithms, genetic algorithm (GA) and PSO, are applied to optimize the gas distribution for oil lifting problem for a 6-well and a 56-well site. The performance plots of the gas intakes are estimated through the artificial neural network (ANN) method in MATLAB. Comparing the simulation results using the evolutionary optimization algorithms and the classical methods, proved the better performance and faster convergence of the evolutionary methods over the classical approaches. Moreover, the convergence rate of PSO is 13 times faster than GA's for this problem.

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“…When distribution system becomes more complexly with many branches and nodes, many researches have been dealt with the problem of determining optimal capacitor placements and many methods have been proposed such as voltage drop increase [1], generic algorithm [2][3], metaheuristic algorithms [4], hybrid mathematical formulation [5], voltage profile improvement and loss reduction [6], voltage support and minimum total cost [7], etc. General purpose for these methods is to reduce the active power required from the power system; reduce load for transformers and medium voltage transmission lines; reduce power loss and electric energy; improve voltage quality; optimize the cost function according to conventional constraints [1][2][3][4][5][6][7][8][9][10]. These methods provide new approaches to determine optimal capacitor placements but they can't be applied in large grids with many buses because they have large calculation and it must be reprogrammed for each detailed grid.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effect of Capacitor Placements at High and Low Voltage Transformer Sides as Voltage Profile and Total Cost in Distribution Systems

Thanh Thuy

2023

Int J Res Rev

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This study mentions the problem of determining optimal placements and sizes for capacitor banks by comparing different schemes that allow implemented buses at both high and low voltage side, only high voltage side or low voltage side of transformers in distribution systems. Criterions evaluated for each scheme are active and reactive power losses in whole system, voltage value at all buses, installation and operation cost and accumulative profit saving. Standard with and without transformer branches in distribution systems and Newton-Raphson method are introduced to make clear about the way to analyze power flows in whole system. Optimal placements and sizes for capacitor banks are determined by using a proposed algorithm and optimal capacitor placement tool in ETAP software. Simulation results can help to show the comparisons, evaluations and make decision about optimal capacitor placements and sizes. The contribution shows the best scheme that install capacitors at low voltage side of transformers because it has the lowest implemented capacity, highest economic factors while voltage quality is always in allowable range. Achieved results can be applied in any distribution system, help managers have overall review and decide optimal placements and sizes for capacitor banks. Keywords: Capacitor bank, OCP Tool, ETAP software, Optimal placement, Optimal size, Voltage quality, Optimal cost.

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Optimal Placement of Capacitor Bank in Reorganized Distribution Networks Using Genetic Algorithm

Cited by 5 publications

References 26 publications

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms

Optimization of Lift Gas Allocation using Evolutionary Algorithms

Analysis of the Effect of Capacitor Placements at High and Low Voltage Transformer Sides as Voltage Profile and Total Cost in Distribution Systems

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