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The study aims to enhance frequency stability in interconnected microgrids using optimized controllers.

June 4, 20260 citationsOpen Access

Firefly Optimized Controllers for Frequency Stabilization in an Interconnected Micro-Grid Fed With Solar Pv Systems and Electric Vehicles

Key Points

The study aims to enhance frequency stability in interconnected microgrids using optimized controllers.
Utilized classical controllers (I, PI, PID) optimized with the Firefly algorithm.
Simulated a two-area microgrid system with diverse energy sources including solar PV and electric vehicles.
Evaluated the performance based on settling periods and peak amplitude deviations.
The FA-PID controller showed superior performance with minimal settling periods and peak amplitude deviations.
Robust performance was observed under varying demand conditions with the FA-PID controller.
Insensitivity to significant deviations in load demands was demonstrated, indicating reliability.

Abstract

ABSTRACT Microgrids (MGs) rely heavily on renewable energy sources (RES), making frequency stability a top need.The intermittent nature of renewable energy sources, along with their stochastic load characteristics, worsenthis issue. This manuscript tackles the concern of Frequency Control in a two-area MG system incorporatingan equal number of diverse energy sources such as solar PV systems, aqua-electrolyzers, wind turbines,energy storage devices, Electric Vehicles, Bio-gas generators and fuel cells in both Area-1 and Area-2.Toaddress the issue of load frequency management (LFM), classical controllers including integral (I),proportional-integral (PI), and proportional-integral-derivative (PID) controllers are employed and optimizedusing the Firefly algorithm (FA). Simulation results reveal the PID controller's superior performance,characterized by minimal settling periods and peak amplitude deviations. Moreover, the FA-PID controllertuned with FA technique exhibits robust performance under varying demand conditions and demonstratesinsensitivity to significant deviations in load demands.

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