A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System

In this work, a chaotic search-based hybrid Sperm Swarm Optimized-Gravitational Search Algorithm (CSSO-GSA) is proposed for automatic load frequency control (ALFC) of power system (HPS). The HPS model developed using multiple sources (thermal, bio-fuel, and renewable energy (RE)) that generate to balance the system’s demand. To regulate system, parameters proportional-integral-derivative (PID) controller ALFC are obtained by minimizing integral time absolute error HPS. effectiveness technique verified with various combinations (all sources, thermal RE) connected into system. Further, robustness investigated performing sensitivity analysis considering variation weather intermittency RE in real-time. However, type source does not have any severe impact on but uncertainties present generation required robust controller. addition, validated comparative stability analysis. results show CSSO-GSA strategy outperforms SSO, GSA, SSO-GSA methods terms steady-state transient performance indices. According optimization, main indices such as settling (ST) (ITAE) significantly improved 60.204% 40.055% area 1 57.856% 39.820% 2, respectively, compared other existing methods.