Self Scheduling using Lagrangian Relaxation and Particle Swarm Optimizer

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.