Anti-tracking of hybrid energy systems for power systems

Anti-tracking in hybrid energy systems involves advanced control strategies that minimize output fluctuations, improve robustness against disturbances, and optimize energy storage usage.

Overview of Anti-Tracking in Hybrid Systems

Hybrid energy systems, combining wind, photovoltaic (PV), and energy storage components, face challenges due to variable renewable generation and load fluctuations. Anti-tracking strategies aim to reduce deviations from planned power output, maintain grid stability, and extend the lifespan of energy storage systems by avoiding unnecessary charge/discharge cycles .

Key Control Approaches

1. Energy Storage-Based Tracking Control

• Uses optimal charge/discharge management of energy storage to smooth output from wind and PV sources.
• Models wind and solar power distributions using Copula functions and applies K-means clustering to reduce computational complexity.
• Implements an interval control mechanism to define acceptable deviation ranges, minimizing unnecessary energy storage activity during minor fluctuations.
• Employs generalized mutual entropy–proximal policy optimization to quantify non-Gaussian tracking errors and adaptively improve robustness.
• Real-world validation shows 31.6% reduction in average tracking error and 35% fewer energy storage cycles, enhancing both performance and system longevity . 2. Active Disturbance Rejection Control (ADRC) with Model Predictive Control (MPC)
• ADRC is applied in outer voltage control loops to reject disturbances without relying heavily on system models.
• MPC is used in inner current control loops for fast response and reduced switching losses.
• Droop control and DC bus voltage compensation maintain stable power distribution and constant DC voltage.
• This approach improves anti-interference ability, shortens regulation time, and maintains DC bus stability in large-capacity hybrid energy storage systems . 3. Robust Backstepping and Maximum Power Point Tracking (MPPT)
• Combines backstepping control for system stability with MPPT for wind turbines and PV arrays.
• Uses particle swarm optimization to maximize energy capture under varying environmental conditions, including partial shading and fluctuating wind speeds.
• Ensures grid integration with minimal voltage deviations and high energy extraction efficiency, achieving over 95% power recovery for wind turbines and 98% PV energy extraction accuracy .

Practical Implications

• Anti-tracking strategies enhance reliability of hybrid energy systems by mitigating the effects of renewable variability.
• They reduce wear on energy storage systems, lowering operational costs and extending service life.
• These methods support seamless grid integration, ensuring stable voltage and power quality even under fluctuating renewable generation.
• Combining adaptive learning, disturbance rejection, and optimization techniques provides a comprehensive solution for modern power systems with high renewable penetration. In summary, anti-tracking in hybrid energy systems leverages advanced control algorithms, energy storage optimization, and predictive modeling to maintain planned output, improve robustness, and enhance overall system efficiency and reliability .