An optimum generation scheduling of electrical power system considering the effect of renewable energy sources by using a hybrid metaheuristic search algorithm

Hybrid algorithms are increasingly being used in optimization strategies. In this paper, a unique hybrid strategy to address the generation scheduling issue, which is non-linear, non-convex, combinatorial, and highly constrained in nature, is given. It makes use of the Chaotic Seagull Optimization Algorithm (CSOA) and the Sine–cosine Optimization Algorithm (SCA). A chaotic search method is used to keep the algorithm from falling towards local minima and to strike a fair balance between the capabilities of exploration and exploitation. This study is examining the consequences of renewable energy sources more closely since they are sustainable and kind to the environment. To handle the stochastic nature of the wind source, an appropriate probability distribution function is employed. The effectiveness and viability of units between 10 and 100 are studied, and the results are compared to those attained utilizing other methods suggested by the literature. The results demonstrate that the proposed technique provides more effective solutions than the other options being thought about. When thermal units are coupled to sources from wind and electric cars, the findings demonstrate that the suggested technique offers excellent economic options. Convergence curves are provided to demonstrate the proposed algorithm’s resilience.