Finally, emerging trends, such as AI-enhanced MPC, digital twin-based testing, and event-driven control, are outlined to support the development of next-generation robust MPC strategies for power
Model predictive control (MPC) is a promising technique for optimizing microgrid operations by considering system constraints and forecasting disturbances.
A comprehensive review of model predictive control (MPC) in microgrids, including both converter-level and grid-level control strategies applied to three layers of microgrid hierarchical
Cavus, M., Dissanayake, D. & Bell, M. Deep-fuzzy logic control for optimal energy management: A predictive and adaptive framework for grid-connected microgrids.
The authors of this paper have developed a control approach that utilizes model predictive control (MPC) and is characterized by its robustness and optimality. MPC has the ability to
DC microgrids (MGs) are one of the most critical components of smart grids, since they are responsible for providing high-quality power to dc consumers continuously. In this article, a novel
MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC
Because of the dynamic and uncertain nature of the MG environment, intelligent control strategies have become the most effective method for ensuring optimal performance [1]. Among
Abstract—This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of nonlinear loads and parametric uncertainties at the primary level of control.
To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the
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