Title :
Self Scheduling using Lagrangian Relaxation and Particle Swarm Optimizer
Author_Institution :
Yarmouk Univ., Irbid
Abstract :
This paper presents a hybrid approach for the self scheduling using Lagrangian relaxation and particle swarm optimizer. Tax is considered in the proposed formulation. The proposed approach is applied to a 36 unit test system. Sensitivity analysis is performed to demonstrate the importance of considering the tax and hourly forecasted probability that reserves are called and generated. The results are compared with those obtained from other approaches.
Keywords :
particle swarm optimisation; power generation scheduling; power markets; pricing; probability; sensitivity analysis; taxation; forecasted probability; hybrid approach; lagrangian relaxation; particle swarm optimizer; self scheduling; sensitivity analysis; taxation; Cost function; Economic forecasting; Lagrangian functions; Load forecasting; Optimization methods; Particle swarm optimization; Sensitivity analysis; Spinning; Stochastic processes; Uncertainty; Particle Swarm Optimizer; Self Scheduling; Tax;
Conference_Titel :
Power Engineering, 2007 Large Engineering Systems Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
978-1-4244-1583-0
Electronic_ISBN :
978-1-4244-1583-0
DOI :
10.1109/LESCPE.2007.4437367
