Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement

Veeramsetty, Venkataramana; Venkaiah, Chintham; Kumar, D. M. Vinod

doi:10.1007/s12667-017-0268-2

Cited by 34 publications

(17 citation statements)

References 43 publications

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“…5 and 6 respectively for optimally placing two DG units with 0.9 lagging power factor under MATLAB environment [42]. IEEE 15 bus and PG& E 69 bus distribution systems data are drawn from [43].…”

Section: Analytical Studymentioning

confidence: 99%

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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: Analytical Studymentioning

confidence: 99%

“…As all these metaheuristic algorithms are stochastic in nature, comparison among all these algorithms for DG optimal placement Fig. 5 Single line diagram for IEEE 15 bus distribution system [43] Fig. 6 Single line diagram for PG & E 69 bus distribution system [43] problem is observed in stochastic environment.…”

Section: Comparative Analysismentioning

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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“…Veeramsetty et al proposed an optimal power flow based on the hybrid genetic dragonfly algorithm to improve the reliability of the radiant power distribution system. 16 Guo et al…”

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

“…So as to improve the convergence performance and operating efficiency of basic DA, some scholars have proposed many hybrid optimization algorithms based on the hybrid of DA and other meta‐heuristic algorithms, and applied them in related fields. Veeramsetty et al proposed an optimal power flow based on the hybrid genetic dragonfly algorithm to improve the reliability of the radiant power distribution system 16 . Guo et al proposed an engine adaptive calibration optimization algorithm based on multi‐objective DA and multi‐objective genetic algorithm based on substructure artificial neural network to improve engine performance 17 .…”

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

Wind driven dragonfly algorithm for global optimization

Zhong

Zhou

Luo

et al. 2020

Concurrency and Computation

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Dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

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“…In this study, loss sensitivity factors were used to measure the actual impact of real and reactive power generation at any bus in the distribution system on APL. The sensitivity of the APL of the radial distribution system with respect to real power generation at bus 'b' is computed using (12). Similarly sensitivity of DG units reactive power generation at bus 'b' on the APL has been computed using (13) and 14for lagging and leading power factor DGs, respectively.…”

Section: Loss Sensitivity Factorsmentioning

confidence: 99%

Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction

Veeramsetty

Venkaiah

Kumar

2020

IET Generation, Transmission & Distribution

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Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement

Cited by 34 publications

References 43 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

Wind driven dragonfly algorithm for global optimization

Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction

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