Frequency regulation of an interconnected renewable rich power system with electric vehicles using tilt multistage PIDF controller approach

• Developed a novel tilt multistage PID controller that effectively addresses the Load Frequency Control (LFC) problem, seamlessly integrating the advantages of both tilt and multistage PID controllers into a robust, unified framework. • The proposed controller's effectiveness was assessed in modern power systems under various operating scenarios, including stochastic variability in renewable energy, inherent uncertainties, and communication delays. • Additionally, this work marks the first application of the Electric Eel Foraging Optimization (EEO) algorithm for tuning the proposed controller. • A thorough effort has been made to meet all the necessary modern power system LFC requirements. The new controller has been compared with various existing ones, including standard controllers like PIDF and newer ones such as the TPIDF controller. In modern power systems, the integration of uncertain and sporadic renewable sources, along with uncertainties in load disturbances and inertia, causes rapid power imbalances. This power imbalance, combined with delays in the communication channel and system uncertainties, led to significant frequency deviations in the power system. These deviations affect the other areas and lead to instability in the power system. PID controllers have a solid foundation and are relatively easy to implement; however, they may not fully address the dynamic needs of today’s power grids. Exploring advanced control strategies could enhance their effectiveness and better accommodate the complexities of modern power systems. In response, this paper introduces the Tilt Multistage PID controller with filter (TMSPIDF), designed explicitly for frequency regulation in renewable-integrated power systems. The controller combines the merits of multistage, tilt, and PID controllers within a single controller framework. This controller aims to handle uncertainties associated with power systems more effectively and precisely across various operating scenarios. The performance of the proposed TMSPIDF controller relies on optimal tuning, where the controller's parameters are fine-tuned using the Electric Eel Foraging Optimization (EEO) algorithm. In tests conducted with up to 40 % renewable penetration, the TMSPIDF controller reduced frequency error by 62 % and 39 % and settling times by 75 % and 40 % compared to TPIDF and PIDF controllers. Moreover, the proposed controller demonstrates superiority over TPIDF and PIDF controllers in the face of parametric and system uncertainties.