Masikana, S. B.Sharma, GulshanSharma, SachinCelik, Emre2024-08-232024-08-2320240948-79211432-0487https://doi.org/10.1007/s00202-024-02417-5https://hdl.handle.net/20.500.12684/14557The integration of additional renewable energy sources, such as solar PV, into the current power grid is a global priority due to the depletion of traditional supplies and rising power demand. In order to achieve load frequency control (LFC) of the power system with integration of solar PV, this study employs the construction of a proportional integral derivative (PID) scheme that has been fine-tuned via the flower pollination algorithm (FPA). When evaluating the performance of FPA-PID on an interconnected thermal power system, three distinct error values-integral time absolute error (ITAE), integral time multiplied by square error (ITSE), and integral of absolute error (IAE)-are taken into consideration. The results are compared with those of genetic algorithm, particle swarm optimization, and hybrid bacteria foraging optimization based PID. It can be observed that the error values achieved with FPA-PID are substantially lower than those obtained with other PID designs, which are ITSE of 2.07e-05, ITAE of 0.01839, and IAE of 0.008889. Furthermore, the PV integration has further decreased the ITSE to 7.872e-06, the ITAE to 0.008953, and the IAE to 0.005376. All error levels have been further reduced because of the integration of unified power flow control (UPFC) in series with the tie-line and redox flow battery (RFB) separately, utilizing the FPA-PID scheme with solar PV. Finally, it is seen that FPA-PID with solar PV and with UPFC outperforms other LFC designs. The graphical LFC plots verify that FPA-PID with solar PV and with UPFC has capability to reduce the frequency, tie-line power, and area control error excursions in comparison to other LFC designs.en10.1007/s00202-024-02417-5info:eu-repo/semantics/openAccessLFCSolar PVOptimization techniquesPIDFlower pollination algorithmUPFCRFBAutomatic-Generation ControlLearning Based OptimizationSearch AlgorithmDesignAgcArticle2-s2.0-85192996112WOS:001222383700007Q2N/A