Frequency Regulation Improves Power Systems using Renewable Energy

Improving the power system performance connected with renewable energy sources is challenging research problem. Researchers have addressed the challenges related to load frequency control (LFC) by employing various methods, such as the automated gain control (AGC) regulator, designing excitation controllers, and implementing control strategies, due to uncertainties in parameters. In a modern power system, a blackout can have a broader impact on a bigger area because of the variability in renewable energy and unforeseen shifts in demand. In this scenario, a more advanced method of managing or regulating is necessary. Controllers utilizing optimization algorithms are gaining popularity due to their user-friendly nature, rapid convergence, and the absence of a requirement for a physical system model in most optimization-based controllers. For optimal cost management and LFC problem solving, this method employs two natural solutions: the Firefly Algorithm (FA) and the Atom Search Optimization (ASO). Our first major contribution is a model of a power system that incorporates renewable energy sources and makes use of frequency regularization. As a component of this strategy, we have been working toward the objective of reducing steady-state errors to enhance the overall accuracy and performance of the power system under steady-state conditions.