Fractional Order AGC for Distributed Energy Resources Using Robust Optimization (1611.09755v1)

Abstract: The applicability of fractional order (FO) automatic generation control (AGC) for power system frequency oscillation damping is investigated in this paper, employing distributed energy generation. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell and aqua electrolyzer along with other energy storage devices like the battery and flywheel. The controller is placed in a remote location while receiving and sending signals over an unreliable communication network with stochastic delay. The controller parameters are tuned using robust optimization techniques employing different variants of Particle Swarm Optimization (PSO) and are compared with the corresponding optimal solutions. An archival based strategy is used for reducing the number of function evaluations for the robust optimization methods. The solutions obtained through the robust optimization are able to handle higher variation in the controller gains and orders without significant decrease in the system performance. This is desirable from the FO controller implementation point of view, as the design is able to accommodate variations in the system parameter which may result due to the approximation of FO operators, using different realization methods and order of accuracy. Also a comparison is made between the FO and the integer order (IO) controllers to highlight the merits and demerits of each scheme.

• The paper shows that fractional order PID controllers tuned via robust optimization significantly enhance system robustness against parameter variations.
• It uses particle swarm optimization and Monte-Carlo simulations to validate performance improvements in distributed renewable energy grids.
• The study highlights practical benefits of a centralized control approach that balances simplified maintenance with trade-offs between robust and optimal designs.

Insights into Fractional Order AGC for Distributed Energy Resources

The paper explored in the paper "Fractional Order AGC for Distributed Energy Resources Using Robust Optimization" investigates the applicability and advantages of fractional order (FO) automatic generation control in distributed energy systems. These systems incorporate varied autonomous power generation sources including wind turbines, solar photovoltaics, diesel engines, fuel cells, and aqua electrolyzers, alongside energy storage devices like batteries and flywheels.

Key Contributions and Methodologies

The authors assert the potential of fractional calculus-based control mechanisms, which have gained traction in recent years for their versatility in numerous domains, to enhance robust control in power systems. They implement a unified, centralized control structure as opposed to multiple decentralized ones. This choice simplifies system maintenance and reduces the number of controller parameters, despite introducing some performance deterioration by using a single control signal for all subsystems.

The system's controller relies on a fractional order PID (FOPID) framework, tuned using robust optimization techniques—specifically, variants of Particle Swarm Optimization (PSO). By leveraging these algorithms, the paper demonstrates that FO controllers can sustain higher parameter variations without significant performance dips, an advantageous feature given hardware imprecision inherent in the implementation of FO systems.

Numerical Results and Analysis

The authors present robust optimization as an effective paradigm for tuning controller parameters, providing numerical evidence that robust controllers outperform optimally tuned ones when system parameter perturbations occur. Their analysis employs a Monte-Carlo simulation approach to account for controller parameter variability, showing that while optimal designs may break down under perturbation, robust designs maintain functionality.

Moreover, the paper extends its analysis to variations in system parameters, such as inertia and damping constants, delineating the FOPID controller's superiority over its integer order counterpart in maintaining grid stability under diverse scenarios.

Theoretical and Practical Implications

In theoretical terms, the paper underscores the potential of FO control systems in managing the complexities and inherent uncertainties of modern power grids. The authors successfully argue for the robustness of FOPIDs, particularly in the context of fluctuating renewable energy inputs and unstable communication links, marking a significant departure from traditional integer order control methodologies.

Practically, adopting robust optimization with PSO algorithms offers an appealing solution for real-world implementations of hybrid power systems, where exact system modeling remains challenging. Furthermore, acknowledging trade-offs between optimal and robust designs poses important considerations for system engineers focusing on long-term operational reliability versus immediate performance efficiency.

Future Prospects

While the paper establishes a solid framework for robust FO controller implementations in distributed energy resource management, it directs future work towards hardware validation of these concepts. Exploring further enhancements and practical applications, especially concerning real-time environments and additional system perturbation scenarios like packet dropouts, remains crucial. Additionally, integrating newer algorithmic developments within the FO control optimization space serves as a promising avenue for future research endeavors.

Ultimately, the insights presented form a foundational step toward revolutionizing smart grid controls, aligning with global trends towards decarbonized and decentralized energy systems—a reflection of both evolving technological capabilities and societal expectations of sustainability.