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Microgrid Power Sharing: Adaptive vs. Nonlinear Predictive Models
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DOI：10.1109/TIA.2025.3587217.png)
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Abstract
En 中文
This study evaluates and compares two advanced model predictive control (MPC) strategies: adaptive MPC with successive linearization (ADMPC-SL) and nonlinear MPC (NLMPC)—for real-time microgrid (MG) control. Focusing on power sharing, load management, and stability, the research utilizes centralized MPC with linear time-varying and nonlinear prediction models to optimize distributed energy resources (DERs). The proposed approaches are evaluated through real-time simulations using OPAL-RT technologies on an islanded MG. Moreover, it is validated on an expanded MG system with additional DERs to demonstrate scalability and robustness. The results show that ADMPC-SL reduces computational requirements by 50% compared to NLMPC, making it suitable for resource-constrained systems, while NLMPC delivers superior accuracy, especially during transient conditions. Stability analysis and participation factor evaluations confirm bounded input-bounded output (BIBO) stability and identify dominant modes in the MG system. These findings highlight the trade-offs between computational efficiency and control precision, providing practical insights for the deployment of advanced MPC strategies in modern MG applications.
Keywords:
Adaptive control
nonlinear model predictive control
microgrids
successive linearization
power sharing
Journal
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1.1W
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3.5W