Improved parameters for economic dispatch problems by teaching learning optimization

摘要

The solution of Economic Dispatch (ED) problems mainly depends on the modelling of thermal generators. The physical variations such as aging and ambient temperature affect the modelling parameters and are unavoidable. As these parameters are the backbone of ED solution, the periodical estimation of these characteristics coefficients is necessary for accurate dispatch. The process is formulated as an error minimization problem and a nature inspired algorithm namely Teaching Learning Based Optimization (TLBO) is proposed as an estimator. This work provides a frame work for the computation of coefficients for quadratic and cubic cost functions, valve point loading, piece-wise quadratic cost and emission functions. The effectiveness of TLBO is demonstrated on 5 standard test systems and a practical Indian utility system, involving varying degree of complexity. TLBO yields better results than benchmark Least Error Square (LES) method and other evolutionary algorithms. The economic deviation is also tested with existing systems.