Green microgrid’s LFC using recursive step-by-step optimized multi-stage fuzzy controller with separated inference systems

Amidst the growing integration of renewable energy sources (RES), the efficient administration of load-frequency control (LFC) in microgrids (MG) continues to be a significant challenge. In response to the complexities of modern MGs operations, this study introduces a novel two-stage fuzzy controller aimed at enhancing the system’s dynamic responses. The proposed controller consists of two levels, each of which contains a separate and autonomous fuzzy inference system (FIS). The proposed controller includes proportional (P) and derivative (D) control operators in the first level, and in the second level, the combination of proportional and integral (I) operators is used. The suggested fuzzy P-fuzzy D multiplied by 1+(fuzzy P - fuzzy I) which is named FPFD-(1+FPFI) controller parameters are tuned by solving an optimization problem to reduce energy wastage and prevent undesirable dynamic responses. The parameters of the controller and the membership functions (MF) at each level are both optimized. The optimization process utilizes an enhanced particle swarm optimization (PSO) algorithm. The decisive superiority of the FPFD-(1+FPFI) controller has been confirmed by evaluating its performance in an all-renewable MG compared to conventional controllers. Reduction of frequency deviation in the face of disturbances in the demand side or production of renewables, better performance in the presence of uncertainty in the parameters of the system model, better dynamic responses against nonlinear factors such as time delays, and also, robustness against cyberattacks are prominent features of the proposed FPFD-(1+FPFI) controller. In addition, the results of the studies show that the controller, with its fast and accurate performance, reduces the dependence on the energy storage systems to maintain the stability of the system by more than 40%. The efficiency of the proposed controller is also verified through laboratory scale evaluation.