Stability-Guaranteed Nonlinear Model Predictive Control for Power Management in Navy Microgrids with Pulsed Power Loads

Core Concepts

A stability-guaranteed nonlinear model predictive control (LNMPC) strategy is proposed for power management and voltage regulation in navy microgrids with pulsed power loads, integrating complex droop control for optimal power sharing among distributed generation units.

Abstract

This paper presents a novel control strategy for medium voltage DC (MVDC) naval shipboard microgrids (MGs), employing a nonlinear model predictive controller (NMPC) enhanced with stabilizing features and an intricate droop control architecture.
The key highlights are:

Development of NMPC control algorithms for MVDC naval shipboard MGs with guaranteed stability and recursive feasibility through the addition of terminal ingredients based on Lyapunov stability theory.

Integration of a reduced order model of MVDC naval shipboard MGs with virtual capacitive and resistive droop controllers as the prediction model to maintain voltage restoration, power balance, and obtain optimal power sharing among the available energy sources (synchronous generators, battery energy storage systems, and supercapacitors).

Investigation of the integrated control algorithms (complex droop control and Lyapunov-based NMPC) against fluctuation in pulsed power loads (PPLs) in both duration and magnitude, as well as noise-influenced load uncertainty, and comparison with a conventional PI controller.

The proposed Lyapunov-based NMPC (LNMPC) quickly regulates the output voltage and adeptly allocates supercapacitors for PPLs, while the battery energy storage system and auxiliary generators handle the steady state loads. The LNMPC exhibits superior performance compared to the PI controller, maintaining power demand adherence and achieving bus voltage regulation with a mean absolute percentage error of 0.007%, while reaching stability twice as fast for voltage and output power.

Stats

The output voltage variation is within regulated limits of ±5%, specifically only up to 1.67% variation.
The LNMPC managed to stabilize the output power of each distributed generation unit four times faster than the PI controller.
The LNMPC maintains power balance and bus voltage within the permissible boundaries (±5% differences) with a tracking error of 0.02% under load uncertainty.

Quotes

"This combination quickly regulates the output voltage and adeptly allocates supercapacitors for pulsed power loads (PPLs), while the battery energy storage system (BESS) and auxiliary generators handle the steady state loads."
"A key feature of this study is the formulation of terminal cost and constraints, providing recursive feasibility and closed-loop stability in the Lyapunov sense, that offers a more robust and effective approach to naval power and energy management."

Key Insights Distilled From

Nonlinear Model Predictive Control for Navy Microgrids with Stabilizing Terminal Ingredients

by Saskia Putri... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2312.03604.pdf

Deeper Inquiries

How can the proposed LNMPC strategy be extended to handle uncertainties in the system parameters and model mismatch

The proposed LNMPC strategy can be extended to handle uncertainties in the system parameters and model mismatch by incorporating robust control techniques. One approach is to integrate robust optimization methods within the LNMPC framework to account for uncertainties in the system dynamics and disturbances. Robust MPC formulations can include robust constraints that ensure system stability and performance even in the presence of parameter variations or model inaccuracies. By formulating the optimization problem with robust constraints, the controller can provide guarantees on system performance under uncertain conditions. Additionally, adaptive control techniques can be employed to continuously update the model parameters based on real-time data, allowing the controller to adapt to changing system dynamics and uncertainties. By combining robust and adaptive control strategies, the LNMPC can effectively handle uncertainties in the system parameters and model discrepancies, enhancing the overall robustness and reliability of the naval shipboard microgrid control system.

What are the potential challenges in implementing the LNMPC in a real-world naval shipboard microgrid and how can they be addressed

Implementing LNMPC in a real-world naval shipboard microgrid may pose several challenges that need to be addressed for successful deployment. One potential challenge is the computational complexity of the control algorithm, especially in large-scale microgrid systems with multiple distributed energy resources. To overcome this challenge, efficient optimization algorithms and hardware platforms can be utilized to ensure real-time implementation of the control strategy. Another challenge is the integration of the control system with the existing power management infrastructure on naval vessels, which may require system retrofitting and coordination with other onboard systems. Proper system identification and modeling of the naval shipboard microgrid are crucial to ensure the effectiveness of the LNMPC strategy. Additionally, cybersecurity measures must be implemented to protect the control system from cyber threats and ensure the secure operation of the microgrid. By addressing these challenges through careful system design, modeling, and implementation, the LNMPC can be successfully deployed in real-world naval shipboard microgrid applications.

What other advanced control techniques, such as adaptive or robust control, could be integrated with the LNMPC to further enhance the performance and reliability of the naval power system

In addition to LNMPC, integrating adaptive control techniques such as Model Reference Adaptive Control (MRAC) or Self-Tuning Control (STC) can further enhance the performance and reliability of the naval power system. Adaptive control methods can adjust the controller parameters based on online system identification, allowing the control system to adapt to changing operating conditions and uncertainties. By continuously updating the control parameters, adaptive control can improve the tracking accuracy and robustness of the system, particularly in dynamic environments with varying load conditions. Moreover, the integration of robust control techniques like H-infinity control or sliding mode control can provide additional robustness against disturbances and uncertainties in the system. By combining LNMPC with adaptive and robust control strategies, the naval power system can achieve superior performance, stability, and resilience in challenging operating conditions.