Transmission Congestion Management by Rescheduling of Generator using Slime Mould Algorithm

Author(s):  
Subhasish Deb ◽  
Subir Datta ◽  
R. Lalbiakmuani ◽  
Kh. Premkumar Singh
2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.


2021 ◽  

Abstract Transmission congestion issues became more severe and difficult to control as the power sector became more deregulated. The grey wolf optimization algorithm is proposed to relieve congestion by rescheduling generation effectively, resulting in the least congestion cost. The selection of participating generators is based on sensitivity, and the proposed technique is used to determine the best-rescheduled output active power generation to minimize line overload. The IEEE-30 bus system is used to test the proposed optimization technique. It has been demonstrated that when compared to other algorithms like the real coded genetic algorithm, particle swarm optimization, and differential evolution algorithm, the proposed approach produces excellent results in terms of congestion cost.


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