Clustered adaptive teaching–learning-based optimization algorithm for solving the optimal generation scheduling problem

In the present paper, the optimum generation scheduling problem of deregulated power system is solved considering the non-smooth and non-convex generator fuel cost characteristics using the clustered adaptive teaching–learning-based optimization (CATLBO) technique. In CATLBO technique, the entire class is separated into many sections and assigned different teacher to each section depending on the performance of that particular section. This sectioning of the class makes proposed algorithm less prone to trapping in local optima and more robust. In this paper, three different objective functions are formulated, and they are total generation cost minimization considering the practical constraints, system loss minimization and L-index/ voltage stability enhancement index (VSEI). In this optimization problem, the generator active power outputs, generator bus voltage magnitudes, transformer tap ratios and bus shunt susceptances are selected as the control variables including the various equality and inequality constraints. The effectiveness and suitability of the proposed algorithm is examined on standard IEEE 30 bus, 57 bus, 118 bus and 300 bus systems, and the simulation results obtained using the proposed algorithm are also compared with many other optimization techniques reported in the literature.