Bio-inspired Computational Heuristics Integrated with Active-set Method to Study Economics Load Dispatch Problems involving Stochastic Wind Power (T-0417) (MFN 5065)

Abstract

In this research work, Bio-inspired Computational Heuristic Algorithms (BCHAs) integrated with Active-Set Method (ASM) are designed to Study Economics Load Dispatch (ELD) Problems with valve point effects involving stochastic wind power. These BCHAs are developed through variants of genetic algorithms based on different set of functions for its fundamental operators in order to make exploration and exploitation in the entire search space for finding the global optima, while the ASM algorithms is used for rapid refinement of the results. The designed schemes are intended to test on different ELD systems consist of combination of thermal generating units and wind power plants with and without valve point effects. The accuracy, convergence, robustness and complexity of the proposed schemes will be examined through comparative studies based on sufficient large number of independent runs and their statistical analyses. Beside the novel application of BCHAs hybrid with ASM to integrated power plants systems based on wind and thermal generating units other advantages of the schemes are simplicity of the concept, ease in implementation and wider domain of applicability.