Frequency stabilization in sustainable energy sources integrated power systems using novel cascade noninteger fuzzy controller

ABSTRACT Current power system has taken a paradigm shift from the conventional structure to the hybrid consisting of thermal and renewable energy sources (RESs), such as hydro, tidal, geothermal, etc. power generating units. However, RESs are intermittent and extremely unpredictable, thus may cause huge frequency deviations. For the smooth operation of the power system in the wake of RESs intermittency and continuously varying load demands, an robust and ameliorated load frequency control (LFC) strategy is requisite. Therefore, in this paper, a new cascade fuzzy-noninteger (fractional order) proportional derivative with filter-proportional integral (CFPDμF-PI) control policy is proposed to cope with the frequency abnormality that occurs due to the presence of renewable generating units in the existing power system. The CFPDμF-PI controller adopts FPDμF as a master and integer order PI as a slave controller. The recently introduced slime mold algorithm (SMA) is employed as a stochastic optimizer to tune the controller parameters. Two case studies have been presented to investigate the performance of the proposed controller. Case-1 simply focuses on the implementation of the proposed technique on a two-area non-reheat thermal power system while case-2 involves the two-area thermal-hydro power system. In both cases, the efficiency of the control method is validated in the presence of tidal, geothermal power plants, and solid oxide fuel cells. To affirm the contribution of our proposal, comparative studies with the existing state-of-the-art techniques have been conducted under identical conditions. From the data analysis, it is witnessed that the proposed control approach with the integration of RESs presents an advancement in the four performance indices by 75.29%, 77.79%, 90.48%, 94.68%, and 36.19%, 40.87%, 49.41%, 50% for case-1 and case-2, respectively. Different scenarios for the robustness analysis validate the capability of the proposed approach in LFC and suitability for other real-world applications.