A novel Green Leaf‐hopper Flame optimization algorithm for competent frequency regulation in hybrid microgrids

Abstract

AbstractA proficient frequency regulation scheme has been explored in this work for two interconnected hybrid microgrids () comprised of multiple renewable energy sources, like solar‐thermal units, wind turbine generator units, solar photovoltaic, biogas turbine, biodiesel generator, and storage units. A new optimization algorithm has been formulated to replicate the photo‐tactic swarming behavior of green leafhoppers named Green Leaf‐hopper Flame optimization algorithm (GLFOA) to tune the system controllers (proportional integral (PI)/proportional derivative (PD)/proportional integral derivative (PID) controllers). The superiority of GLFOA is confirmed by comparing the performance with several popular optimization algorithms. To assess the stability of the several rigorous tests have been conducted considering availability/nonavailability of renewable resources, gusty wind, real‐time variation of solar radiation and wind velocities along with the random load disturbances. GLFOA tuned PID controllers were found to be the best to contain the frequency deviations. The maximum peak overshoot, undershoot, and settling time obtained by GLFOA tuned PID controllers are 0.01051 Hz, −0.00764 Hz, and 14.15 s, respectively, which are within an acceptable limit. Further, there is an improvement of 0.15% in the performance index obtained by GLFOA over its counterpart obtained by grey wolf optimization, confirming the superiority of the GLFOA tuned controller.