Evolutionary Algorithm Based Centralized Congestion Management for Multilateral Transactions
Commenced in January 2007
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Edition: International
Paper Count: 32769
Evolutionary Algorithm Based Centralized Congestion Management for Multilateral Transactions

Authors: T. Mathumathi, S. Ganesh, R. Gunabalan

Abstract:

This work presents an approach for AC load flow based centralized model for congestion management in the forward markets. In this model, transaction maximizes its profit under the limits of transmission line capacities allocated by Independent System Operator (ISO). The voltage and reactive power impact of the system are also incorporated in this model. Genetic algorithm is used to solve centralized congestion management problem for multilateral transactions. Results obtained for centralized model using genetic algorithm is compared with Sequential Quadratic Programming (SQP) technique. The statistical performances of various algorithms such as best, worst, mean and standard deviations of social welfare are given. Simulation results clearly demonstrate the better performance of genetic algorithm over SQP.

Keywords: Congestion management, Genetic algorithm, Sequential quadratic programming.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1090747

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References:


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[12] Dutta S, Singh S.P: "Optimal rescheduling of generators for congestion management based on particle swarm optimization”, IEEE Trans, Power Syst, Vol 23, pp. 1560-1569,2008. S. Ganesh obtained his B.E. in Electrical and Electronics Engineering in Dr. Sivanthi Adithanar College of Engineering Tiruchendur, Anna University, Tamilnadu, India, in 2009 and did his Master of Engineering (Power Systems Engineering) in St. Joseph’s Engineering College, Anna University, India, in 2013. He is working as an Assistant Professor in the department of Electrical and Electronics Engineering, Chandy College of Engineering, Thoothukudi, Tamilnadu, India.