Enhanced coyote optimizer-based cascaded load frequency controllers in multi-area power systems with renewable

Recently, the renewable energy has been occupied a lot of attention around the world since it presents cheap and sustainable energy. Consequently, its presence in power systems becomes a fact that had to deal with. Hence, load frequency control (LFC) in multi-area power systems that constitute photovoltaic (PV) and thermal plant sources is proposed. Two forms of competing cascaded controllers, namely proportional integral-proportional integral (PI-PI) and proportional-derivative with filter-PI (PDn-PI), are investigated, and their performances are compared with traditional PI and PIDn controller. An enhanced coyote optimization algorithm (ECOA) is proposed for finding the optimal tuned parameters of the proposed controllers. Furthermore, the uncertainty is considered under the variation of system parameters by +/- 40%. The performance of the proposed competing controllers is tested under dynamic load change that is applied individually in each area. These controllers are applied on two dissimilar test cases with various sets of disturbances. The obtained results are compared with various reported techniques. The simulated comparisons declare the great efficiency with high superiority robustness of the proposed cascaded PDn-PI based on ECOA for handling the LFC in multi-area power systems.