By Topic

Application novel Immune Genetic Algorithm for solving Bid-Based Dynamic Economic power load dispatch

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Gwo-Ching Liao ; Department of Electrical Engineering at Fortune Institute of Technology, China ; Jia-Chu Lee

This paper presented a new algorithm-Isolation Niche Immune Genetic Algorithm for solving Bid-Based Dynamic Economic Dispatch (INIGA-BDED) in a power system. Economic Dispatch determines the electrical power to be generated by the committed generating units in a power system so that the generation cost can minimized, while satisfying various load demands simultaneously. The model of Bid-Based Dynamic Economic Dispatch is proposed in order to maximize the social profit under the competitive environment of electricity market. This model synthetically considers various constraints on ramp rates, transmission line capacity and polluting gas emission constraints etc. The Isolation Niche Immune Genetic Algorithm was induced as a new solution for this model. With the introduction of niche technology, the capability of the immune genetic algorithm in dealing with the optimization of multi-peak model function was enhanced. This paper proposed the Niche based on Isolation mechanism which possesses with biological basis. It not only effectively ensures the diversity solutions of the group, but also has a strong ability to guide evolution. On the other hand, the use of immune genetic algorithm itself is a very good and innovative method in the solution for multi-peak model function. A new improving method for this algorithm was also presented in this paper. This research integrated the applications of these two methods to enhance the evolutionary capability in seeking a more optimal solution for solving nonlinear programming. The result of the application of this integrated method for the proposed test had been very good.

Published in:

Power System Technology (POWERCON), 2010 International Conference on

Date of Conference:

24-28 Oct. 2010