A Refined Teaching-Learning Based Optimization Algorithm for Dynamic Economic Dispatch of Integrated Multiple Fuel and Wind Power Plants

Abstract

Dynamic economic dispatch problem (DEDP) for a multiple fuel power plant is a nonlinear and nonsmooth optimization problem when valve-point effects, multifuel effects, and ramp-rate limits are considered. Additionally wind energy is also integrated with the DEDP to supply the load for effective utilization of the renewable energy. Since the wind power may not be predicted, a radial basis function network (RBFN) is presented to forecast a one-hour-ahead wind power to plan and ensure a reliable power supply. In this paper, a refined teaching-learning based optimization (TLBO) is applied to minimize the overall cost of operation of wind-thermal power system. The TLBO is refined by integrating the sequential quadratic programming (SQP) method to fine-tune the better solutions whenever discovered by the former method. To demonstrate the effectiveness of the proposed hybrid TLBO-SQP method, a standard DEDP and one practical DEDP with wind power forecasted are tested based on the practical information of wind speed. Simulation results validate the proposed methodology which is reasonable by ensuring quality solution throughout the scheduling horizon for secure operation of the system.