Dynamic Load Scheduling Optimization Utilizing the Rao Algorithm for Improved System Performance

This paper explores the optimal scheduling of conventional generating units for two dynamic test systems, and enhances the systems performance. The Rao algorithmic approach, a parameter-free population-based optimization method, is utilized to improve the solutions through cooperative interactions on the basis of the best and worst candidate solutions in each iteration. Practical factors, such as ramp-rate limits, valve point effects and transmission loss, were incorporated to reflect real-world operational challenges. The Rao algorithm achieved high-quality solutions in terms of generation cost and reduced pollutant emission. These results highlight the effectiveness of the algorithm in solving complex nonlinear optimization problems and improving the efficiency of dynamic load scheduling.