Generator rescheduling for congestion management with incorporation of wind farm using Artificial Bee Colony algorithm

Deb, Subhasish; Gope, Sadhan; Goswami, Arup Kumar

doi:10.1109/indcon.2013.6726002

Cited by 15 publications

(12 citation statements)

References 18 publications

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“…A comparative analysis is presented with other algorithms to put forward the efficiency of the proposed CM approach. The comparison has been established with RED [35], PSO [36], ABC [37], FFA [38]. The CM problem has also been analyzed with some latest techniques GWO, − constraints, and DA and the results of these approaches are also included in this research article for comparative analysis purpose.…”

Section: Resultsmentioning

confidence: 99%

“…Table 4 represents the outcomes brought about with the incorporation of proposed BA technique for the CM problem. The results reported in the literature [35][36][37] Table 4 for a comparative analysis. The congestion cost of 7751.36 $/h is obtained with the proposed BA method and is minimum amongst the congestion costs achieved with the other existing techniques.…”

Section: Modified 39-bus New England Systemmentioning

confidence: 99%

“…The optimization of the congestion cost was performed with PSO [36]. Deb et al have introduced Artificial Bee Colony (ABC) algorithm to optimize the real power output of the generators to manage the overloading of the transmission lines [37]. Verma et al mollified congestion using FFA [38].…”

Section: Introductionmentioning

confidence: 99%

“…-The optimum outcomes offered by BA are analyzed and contrasted with the other recent algorithms like RED [35], PSO [36], ABC [37], FFA [38], Grey Wolf Optimization (GWO) [46], −constraint [47] and Dragonfly Algorithm (DA) [48], which is done in order to sort out the best algorithm for the proposed work. -The comparative analysis has been established based on the congestion cost, convergence profile and system losses between BA and other algorithms to provide an efficient CM approach.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bat Algorithm for Congestion Alleviation in Power System Network

Paul¹,

Kumar

Dalapati³

2021

Technol Econ Smart Grids Sustain Energy

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Section: Resultsmentioning

confidence: 99%

Section: Modified 39-bus New England Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bat Algorithm for Congestion Alleviation in Power System Network

Paul¹,

Kumar

Dalapati³

2021

Technol Econ Smart Grids Sustain Energy

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“…In [7], the zonal congestion management approach is carried out by the optimal rescheduling of real and reactive power generator. In [8], the generator rescheduling with incorporation of wind farm in the system is analysed. Wind farm is selected based on power transfer distribution factors.…”

Section: Literature Surveymentioning

confidence: 99%

Congestion management of power system with uncertain renewable resources and plug‐in electrical vehicle

Prajapati

Mahajan

2019

IET Generation, Transmission & Distribution

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Mitigation of Grid Congestion by Curtailment of Thermal Generators and Renewable Energies Based on Sensitivity Analysis

Tanno

Kaneko

Hayashi

et al. 2022

IEEJ Transactions Elec Engng

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Grid congestion is expected owing to the interconnection of massive renewable energies within the transmission system. Power system operators need to resolve the problem by controlling the output of thermal and renewable generators, such as curtailment, which should be the smallest possible for efficient utilization. Moreover, the equality of generation opportunities should be maintained among renewable energies. However, achieving the two objectives simultaneously is challenging, because the output curtailment effect on grid congestion in a loop system varies depending on its configuration. Further, the thermal generators are curtailed before renewable energies in the merit order scheme necessitating adjustments in the thermal generator output considering the renewable energy curtailment. This study proposed a method to determine the output curtailment ratio of thermal power and renewable energies employing sensitivity analysis, aiming to ensure equality, and maximize the power generation opportunities of renewable energies for the mitigation of grid congestion. The effectiveness of the proposed method was verified via numerical simulations using a power system model based on an actual power system in the northern Tohoku area of Japan. The results indicate that the proposed method can reduce the total amount of curtailment and improve the equality of generation opportunities.

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Generator rescheduling for congestion management with incorporation of wind farm using Artificial Bee Colony algorithm

Cited by 15 publications

References 18 publications

Bat Algorithm for Congestion Alleviation in Power System Network

Bat Algorithm for Congestion Alleviation in Power System Network

Congestion management of power system with uncertain renewable resources and plug‐in electrical vehicle

Mitigation of Grid Congestion by Curtailment of Thermal Generators and Renewable Energies Based on Sensitivity Analysis

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