article · Electrical Engineering
Integrating solar photovoltaic power into existing electrical grids poses stability challenges, particularly for load frequency control. This research investigates a proportional integral derivative controller tuned with the flower pollination algorithm to regulate frequency within an interconnected thermal power system. The approach assesses performance across multiple error metrics, benchmarking results against controllers tuned via genetic algorithms, particle swarm optimisation, and hybrid bacteria foraging optimisation. Findings show the proposed controller achieves lower error values than the alternatives. Incorporating solar power, along with a unified power flow control device placed in series with the tie-line and a redox flow battery, reduces error levels even further. Ultimately, the system combining the tuned controller, solar power, and unified power flow control demonstrates the greatest reduction in frequency deviations, tie-line power fluctuations, and area control errors.
As modern grids incorporate more renewable sources like solar energy, keeping the overall grid frequency stable becomes increasingly difficult. This study shows that advanced optimisation algorithms paired with energy storage and power flow control devices can significantly suppress power disruptions, helping maintain a reliable electricity supply during the transition away from conventional fuels.
The findings are relevant to power system operators, transmission utilities, and grid control software developers seeking enhanced load frequency control for renewable integration. Given that the abstract reports performance metrics and response plots without describing operational field deployment, the technology appears to be at an early, simulation-based stage of research that requires physical grid testing before commercial adoption.
AI-generated from the published abstract. Always read the original work before citing.
Abstract The 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.
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DOI: 10.1007/s00202-024-02417-5
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