A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during next 24 hours in order to meet electricity demand as well minimize total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching-learning-based optimization algorithm, called MTLBO, effectively solve DELD problem system containing thermal generations wind power. The planning comprises fuel cost function with valve-point loading effect transmission losses. Also, uncertainties of along various equality inequality operational constraints such limits, ramp rate prohibited operating zones balance are considered Additionally, proposed MTLBO learning phase properly integrated into teaching improve convergence characteristic original TLBO. Moreover, enhance feature local optima avoidance, interaction up five students incorporated knowledge each student. To exhibit effectiveness approach, algorithm applied 14 real test functions. In addition, several cases 10 30 unit systems investigated over period. Compared main TLBO mostly used algorithms prior studies, simulation results demonstrate efficiency superiority approach terms consistency, robustness, finding better plausible optimal solutions.