You are in:Home/Publications/M.M. SALAMA, M.M. ELGAZAR, S.M. ABDELMAKSOUD, H.A. HENRY, "Short Term Optimal Generation Scheduling of Multi-Chain Hydrothermal System Using Constriction Factor Based Particle Swarm Optimization Technique (CFPSO)", International Journal of Scientific and Research Publications, V 3, Issue 4, April 2013, ISSN 2250 - 3153.

Prof. Mohamed Moenes Mohamed Salama :: Publications:

Title:
M.M. SALAMA, M.M. ELGAZAR, S.M. ABDELMAKSOUD, H.A. HENRY, "Short Term Optimal Generation Scheduling of Multi-Chain Hydrothermal System Using Constriction Factor Based Particle Swarm Optimization Technique (CFPSO)", International Journal of Scientific and Research Publications, V 3, Issue 4, April 2013, ISSN 2250 - 3153.
Authors: M.M. SALAMA, M.M. ELGAZAR, S.M. ABDELMAKSOUD, H.A. HENRY
Year: 2013
Keywords: Not Available
Journal: International Journal of Scientific and Research Publications
Volume: V 3, , ISSN 2250 - 3153.
Issue: Issue 4,
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Mohamed Moenes Mohamed Salama _Short T O G Sch of Multi Chain CFPSO.pdf
Supplementary materials Not Available
Abstract:

In this paper, the particle swarm optimization technique with constriction factor is proposed to solve short term multi chain hydrothermal scheduling problem with non smooth fuel cost objective functions. The performance of the proposed algorithm is demonstrated on hydrothermal test system comprising of three thermal units and four hydro power plants. A wide range of thermal and hydraulic constraints such as power balance constraint, minimum and maximum limits of hydro and thermal units, water discharge rate limits, reservoir volume limits, initial and end reservoir storage volume constraint and water dynamic balance constraint are taken into consideration. The simulation results of the proposed technique are compared with those obtained from other methods such as, simulated annealing (SA) and evolutionary programming (EP) to reveal the validity and verify the feasibility of the proposed method. The test results show that the proposed algorithm achieves qualitative solution with less computational time when compared to the other methods.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus