Design of a two-level controller for load frequency control in the presence of wind farms and battery energy storage system based on VIC | Zendy

Libing Chen | Zendy; Mohamed Salem | Zendy; Khlid Ben Hamad | Zendy; Natarajan Prabaharan | Zendy; Soichiro Ueda | Zendy; Tomonobu Senjyu | Zendy

Open Access

Design of a two-level controller for load frequency control in the presence of wind farms and battery energy storage system based on VIC

Author(s) -

Libing Chen,

Mohamed Salem,

Khlid Ben Hamad,

Natarajan Prabaharan,

Soichiro Ueda,

Tomonobu Senjyu

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3596853

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Power generation from renewable energy sources (RES) due to its uncertain nature and load variations in electrical power grids can lead to inertia reduction and, consequently, frequency deviation. In this study, to solve this problem and regulate the frequency, an automatic generation control (AGC) system along with a battery energy storage system (BESS) based on virtual inertia control (VIC) is used. In order to reduce the effects of inter-area oscillations (IAO) in power systems, the approach used is that a linear quadratic controller (LQC) is used to optimize the parameters of the PI controller. This control configuration is designed as a dual structure: in the first layer, the state errors of the PI controller are improved by minimizing the integral square error (ISE) criterion. Then, in the second layer, a LQC controller, acting as a supervisor, adjusts the optimal values of the PI parameters in the lower layer. Due to the presence of noise and the lack of direct measurement of all state variables, the Kalman Filter (KF) is used as an observer to estimate inaccessible states. The objective functions for frequency tuning are also optimized using the Firefly Algorithm (FA) in the MATLAB software platform. The data obtained from the simulations, which have been evaluated under various conditions, indicate that the proposed solution, in addition to improving the efficiency of frequency tuning, has a higher accuracy in reducing the size of the overshoot, subsidence, and settling time compared to other solutions.

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