An Adaptive Class Topper Optimization for Scheduling Thermal Generation with Cubic Fuel Cost Function

This paper proposes a population-based metaheuristic optimization algorithm, i.e., an adaptive class topper optimization (ACTO), for solving cubic fuel cost objective functions economic load dispatch with equality and inequality constraints. The working principle of the proposed methodology is based on the learning improvement of students in a class for knowledge updating. In general, the use of polynomials function to represent generator fuel cost curves in real-time economic dispatch is an industry standard, and it significantly impacts the accuracy of the economic load dispatch solution. Many existing conventional methods struggle to deal with cubic fuel cost functions that reflect the non-linearity of the real generator response. Therefore, the proposed optimization technique is particularly efficient in solving non-linear higher-order objective functions for both smaller and larger generating units and it significantly decreases the computational time to obtain optimal fuel cost. The proposed algorithm has been successfully tested on the following dispatch case studies: 3 units, 26 units cubic fuel cost function without losses and 3 units cubic system with losses for different loading conditions. The results reveal that the adaptive class topper optimization algorithm outperform other existing methodology when addressing the higher-order generator cost functions.