Frequency stability improvement in EV-integrated power systems using optimized fuzzy-sliding mode control and...

Area of Science:

• Electrical Engineering
• Control Systems
• Power Systems

Background:

• Growing power demand and renewable energy integration challenge power system stability.
• Electric vehicles (EVs) introduce further complexity with bidirectional power flow, impacting frequency stability.
• Robust control strategies are essential for managing frequency and power deviations in modern grids.

Purpose of the Study:

• To develop and evaluate a novel Fuzzy-Sliding Mode Controller (FSMC) for load frequency control (LFC).
• To enhance power system stability amidst high renewable energy penetration and EV integration.
• To optimize controller design using an advanced metaheuristic algorithm.

Main Methods:

• Implemented a Sliding Mode Controller (SMC) for baseline robustness analysis.
• Developed an FSMC by integrating fuzzy logic with SMC to handle system nonlinearities and uncertainties.
• Designed and tuned the FSMC using a Modified Gannet Optimization Algorithm (MGOA), comparing its performance against the standard Gannet Optimization Algorithm (GOA).
• Analyzed the impact of EV integration on frequency and tie-line power dynamics under various conditions.
• Validated simulation results via real-time implementation on an OPAL-RT 4510 platform.

Main Results:

• The FSMC demonstrated superior performance in handling nonlinearities, communication delays, and parameter variations.
• The MGOA showed improved convergence speed and precision over the GOA for controller tuning.
• The MGOA-tuned FSMC achieved faster settling times, reduced overshoot, and enhanced stability metrics compared to existing methods.
• Real-time implementation confirmed the proposed methodology's robustness and practicality.

Conclusions:

• The proposed MGOA-tuned FSMC is a robust and intelligent solution for LFC in power systems with high renewable energy and EV penetration.
• The study confirms the effectiveness of advanced control strategies in maintaining grid stability and reliability.
• The findings support the practical application of intelligent control systems in addressing contemporary power system challenges.