Optimal model predictive control for LFC of multi-interconnected plants comprising renewable energy sources based on recent sooty terns approach

In this paper, an optimal model predictive control (MPC) is tuned using recent optimizer named sooty terns optimization algorithm (STOA). The proposed control is employed to design load frequency control (LFC) installed in interconnected system including different renewable energy sources (RESs). The proposed method is utilized to identify the MPC optimal parameters to minimize the integral time absolute error (ITAE) of the frequencies and tie-line power deviations. The analysis is performed on three multi-interconnected systems, the first one includes two units of thermal and PV with maximum power point tracking (MPPT). The others include linear/nonlinear three-interconnected system with/without superconducting magnetic energy storage (SMES). The nonlinear model is implemented by considering generation rate constraint (GRC) and governor dead-band (GDB). Moreover, the proposed MPC-LFC is investigated under system parameters’ uncertainties. Furthermore, random wind speed and load disturbance are analyzed for multi-interconnected system. The performance of the proposed MPC-LFC optimized via STOA is compared with the proportional-integral (PI) controller designed via firefly algorithm (FA), genetic algorithm (GA), MPC with FA, stain bower braid algorithm (SBO), multi-verse optimizer (MVO), and intelligent water drops algorithm (IWD). The obtained results confirmed the competence of the proposed STOA in designing the optimal MPC-LFC compared to the others.