Optimized Sliding Mode Frequency Controller for Power Systems Integrated Energy Storage System With Droop Control

This paper proposes an adaptive-based single-phase higher-order sliding mode controller (SMC) optimized by the honey badger algorithm (HBA). The developed control method is employed for load frequency control (LFC) in electrical power systems (EPSs) that incorporates a battery energy storage system (BESS) with droop control. Unlike conventional SMCs, the proposed higher-order SMC eliminates the reaching phase by utilizing the second derivative of the sliding motion and embedding the sliding surface directly within the original system, significantly reducing oscillations and chattering effects. Furthermore, an adaptive mechanism dynamically estimates the unknown bound of system uncertainties, enabling real-time compensation and minimizing steady-state errors. The system’s global stability is rigorously analyzed through linear matrix inequality (LMI) and Lyapunov stability theory, highlighting the robustness and practicality of the proposed method. To further enhance the system performance, the HBA is employed to optimize controller parameters, providing a balance between fast response and low overshoot. Additionally, a coordinated BESS control strategy integrating droop control and state-of-charge management is introduced, enabling seamless interaction between the energy storage system and generators to mitigate large disturbances in uncertain EPSs. Finally, extensive case studies under various configurations and conditions demonstrate the superior performance of the proposed controller compared to other related methods. Simulation results consistently confirm that the proposed solution outperforms alternative approaches across all scenarios. This is demonstrated by the minimal over/undershoot value in frequency deviations: $1.913\times 10^{-3}$ for the isolated EPS, $3.3134\times 10^{-4}$ and $0.6476\times 10^{-4}$ for two-area EPS equipped with ten BESS units. In the final case, the presence of BESS results in a significant reduction in peak value by approximately 22.9%, 16.5%, and 22.3% of each respective area of EPS.