Optimal allocation and contingency analysis of Embedded Generation deployment in distribution network using Genetic Algorithm

Veeramsetty, Venkataramana; Lakshmi, G. V. Naga; Jayalaxmi, A.

doi:10.1109/icceet.2012.6203763

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…3. [14] 33,69 Minimization of losses GA Haesen et al [15] 69 Minimization of losses GA Singh and Goswami [16] 33 Minimization of losses GA Singh et al [17] 16,37 Minimization of losses GA Veeramsetty et al [18] 69 Minimization of losses GA Prommee and Ongsakul [19] 69 Minimization of energy losses PSO El-Zonkoly [20] 30 Minimization of power losses PSO Wang and Singh [21] 13,33 Multiple objectives ACO Abu-Mouti and El-Hawary [22] 69 Maximize voltage limit ABC Rao et al [23] 33,69 Maximize EG Capasity HS Ghosh et al [24] 6,14,30 Maximize EG Capasityt PH Koutroumpezis and Safigianni [25] 31 Minimization of energy losses PH Mohd Ikhwan et al [26] 11/0.4kV Reliability improvement MCS Lakshmi et al [27] 15,69 Minimization of power losses FA Fathi and Ghiasi [28] 33 Minimum DG investment cost GA, PSO Generation capacity of DG unit i.e. P g and location are the decision variables.…”

Section: Mathematical Modeling Of Dg Placement Optimization Problemmentioning

confidence: 99%

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Lakshmi

Jayalaxmi

Veeramsetty

2021

Technol Econ Smart Grids Sustain Energy

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Optimal placement of distributed generation (DG) is an essential task for distribution companies in order to operate the distribution network in good operating conditions. Optimal placement of DG units is an optimization problem where minimization of active power losses in the network is considered as an objective function. In this paper, hybrid genetic dragonfly algorithm is used as an optimization technique to find the optimal location and size of distributed generation units. The proposed algorithm is implemented on IEEE 15 and PG & E 69 bus distribution systems in MATLAB environment. Based on the simulation results it has been observed that with proper placement and size of DG units, distribution network can be operated with less active power losses.Keywords Distributed generation • Optimal placement • Hybrid genetic dragonfly algorithm • Active power loss Nomenclature ΔY t+1 Step vector for dragonflies A iAlignment value of dragonfly i C i Cohesion value of dragonfly i E iEnemy source of dragonfly i F iFood source of dragonfly i I k i Current at bus 'i' and iteration 'k' I k LR Current injecting at receiving node of line 'L' at iteration 'k' I l Current flowing through line l I k L Current through line 'L' at iteration 'k' I max l

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Section: Mathematical Modeling Of Dg Placement Optimization Problemmentioning

confidence: 99%

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Lakshmi

Jayalaxmi

Veeramsetty

2021

Technol Econ Smart Grids Sustain Energy

Self Cite

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“…e success of real number coding of chromosomes directly determines whether the operation can carry out the following genetic operations such as selection, crossover, and variation [17]. (2) Determine the initial population: according to the mechanism of real number coding in the previous step and the rules, the initial chromosome group [18], that is, the population, which is composed of chromosomes [19]. Each chromosome in the population is a unique individual.…”

Section: Genetic Algorithm Implementation Processmentioning

confidence: 99%

Research on Social Talent Governance Based on Genetic Algorithm

Zeng

2021

Scientific Programming

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The existing social talent governance algorithms have a number of issues such as slow convergence rate, relatively low data accuracy, recall rate, and low anti-interference. To address these problems, this paper proposes a research on social talent governance algorithm based on genetic algorithm. We discuss the difference between the traditional and the genetic algorithms and determine the implementation process of genetic algorithm. On this basis, the excellent individuals are determined by independent computing fitness, and the initialization population is designed according to the individual similarity threshold. After the population is defined, the roulette and deterministic sampling selection method are integrated to clarify the selection calculation process. Based on the calculation results, we design the crossover operator by segmented single-point crossover between individuals. The mutation operator is designed by segmented mutation of different gene segments according to the calculation results. The results are incorporated into the simulated annealing acceptance probability to conduct simulated annealing for the individuals after the cross-mutation operation and set relevant conditions after the end of the algorithm. We seek the optimal solution of the data within the number of iterations and finally realize the whole process of social talent governance algorithm. The experimental results show that the proposed algorithm has fast convergence rate, high data precision and recall, and has certain feasibility.

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“…Moreover, the non-optimal DG output can cause higher power loss than the initial condition and in the worst case, it could cause the distribution system to collapse. Therefore, many studies on optimal DG output and placement have been done [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Distributed Generators Power Factor to Simultaneous Optimization Analysis

Jamian

Mustafa

Abdullah

2015

AMM

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In this paper, the Particle Swarm Optimization (PSO) technique is used to determine the optimal coordination for Distributed Generator (DG). The DG output and location is determined simultaneously. Furthermore, this study covers both single and multiple DGs analyses. Five different Power Factor (PF) values are also assigned to the DG units, which are 0.8, 0.85, 0.9, 0.95 and 1.0. Thus, the impacts of DG power factor to the optimal placement and output are investigated. From the result, the optimal DG placement is similar, regardless of the PF condition. For the single DG unit, the optimal location is bus 6 and for three DGs analysis, the optimal locations are at buses 14, 24, 30. However, the PF significantly influences the optimal DG output, power loss reduction and voltage profile improvement. Three DGs with PF 0.8 is the best option to reduce the power loss in the distribution network to the lowest value.

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Optimal allocation and contingency analysis of Embedded Generation deployment in distribution network using Genetic Algorithm

Cited by 10 publications

References 6 publications

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Research on Social Talent Governance Based on Genetic Algorithm

Investigating the Impact of Distributed Generators Power Factor to Simultaneous Optimization Analysis

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