The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation (DRG) sources such as solar and wind necessitate the use of an efficient load frequency control (LFC) technique. Therefore

a hybrid version of two metaheuristic algorithms (arithmetic optimization and African vulture's optimization algorithm) is developed. It is called the ‘arithmetic optimized African vulture's optimization algorithm (AOAVOA). This algorithm is used to tune a novel type-2 fuzzy-based proportional-derivative branched with dual degree-of-freedom proportional-integral-derivative controller for the LFC of a three-area hybrid deregulated power system. Thermal

electric vehicle (EV)

and DRG sources (including a solar panel and a wind turbine system) are connected in area-1. Area-2 involves thermal and gas-generating units (GUs)

while thermal and geothermal units are linked in area-3. Practical restrictions such as thermo-boiler dynamics

thermal-governor dead-band

and generation rate constraints are also considered. The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances. By functioning as energy storage elements

EVs and DRG units can enhance dynamic responses during peak demand. As a result

the effect of the aforementioned units on dynamic reactions is also investigated. To validate its effectiveness

the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature. It is determined that the suggested AOAVOA improves fitness by 40.20% over the arithmetic optimizer (AO)

while frequency regulation is improved by 4.55% over an AO-tuned type-2 fuzzy-based branched controller.