Tunicate Swarm Algorithm-Based Load Frequency Controller for Renewable Energy Resources Combined Heat Power Network

Load Frequency Control (LFC) has turned into increasingly complex in various connected energy systems because of rising integration of renewable power resources like photovoltaic (PV) cells plus wind turbines. This work develops an up-to-date fine-tuning technology named the Tunicate Swarm Approach (TSA) for the prime adjusting of a Proportional-Integral-Derivative (PID) controller for LFC. PID tuning challenge of in a robust LFC layout is framed as an optimization issue based on the time-based cost equation that is addressed using the proposed method. TSA has a strong ability to identify the most promising solutions. The block representation of the blend layout is implemented, the simulation outcomes are displayed to evaluate the efficacy of the advised TSA-driven scheme in comparison with Firefly Approach (FA), Genetic Approach (GA), Ant Lion Optimization (ALO), and Water Cycle Approach (WCA). These outcomes demonstrate that the suggested controller performs more efficient than the others when analysing time-domain characteristics and various performance metrics.