Bonobo Optimizer Inspired PI-(1+DD) Controller for Robust Load Frequency Management in Renewable Wind Energy Systems

Abstract

With the growing presence of renewable energy sources (RESs), the necessity for adaptive and robust control strategies becomes more pronounced. This article proposes a self-adaptive bonobo optimizer (SABO)-based proportional integral one plus double derivative (PI-(1+DD)) controller that offers a novel solution to the load frequency control (LFC). It draws inspiration from the reproductive strategies of bonobos, employing unique mating behaviors to enhance optimization processes. This innovative approach introduces memory capabilities, repulsion-based learning, and diverse-mating strategies. It is developed to tune the PI-(1+DD) controller for handling the LFC in a two-area power system involving a thermal plant and RESs of a wind farm. The proposed SABO algorithm is applied in a comparative manner to the standard bonobo optimization algorithm (BOA), Coot algorithm, particle swarm optimizer (PSO), and Pelican optimization approach (POA). Also, the SABO-based PI-(1+DD) controller is contrasted to PI and PIDn controllers. The simulation findings distinguish the proposed SABO-based PI-(1+DD) controller as a versatile and adaptive controller offering a more resilient and efficient approach to tackle the complexities introduced by the evolving energy landscape. It demonstrates its potential to significantly improve the dynamic response of power systems, particularly in the face of step load changes and random fluctuations. The proposed SABO-based PI-(1+DD) controller shows significant enhancement compared to BOA, Coot, POA, and PSO with 38.81%, 46.27%, 16.79%, and 37.40%, respectively. Also, it demonstrates an impressive percentage improvement of 97.1% compared to the PIDn controller and 74.88% over the PI controller considering random consecutive fluctuations in the system.