Learning Robust Model Predictive Control for Voltage Control of Islanded Microgrid

TL;DR

This paper introduces a learning-enhanced robust model predictive control approach for voltage regulation in islanded microgrids, effectively managing nonlinear loads and uncertainties with improved adaptability and theoretical guarantees.

Contribution

It combines Gaussian Process regression with Tube-Based RMPC to reduce conservativeness and enhance robustness in voltage control of islanded microgrids.

Findings

Improved voltage tracking performance in simulations.

Effective handling of load uncertainties.

Theoretical proof of recursive feasibility and stability.

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. The proposed method is based on the Tube-Based Robust Model Predictive Control (RMPC), an online optimization-based method which can handle the constraints and uncertainties as well. The challenge with this method is the conservativeness imposed by designing the tube based on the worst-case scenario of the uncertainties. This weakness is amended in this paper by employing a combination of a learning-based Gaussian Process (GP) regression and RMPC. The advantage of using GP is that both the mean and variance of the loads are predicted at each iteration based on the real data, and the resulted values of mean and the bound of confidence are utilized to design the tube in RMPC. The theoretical results are also provided to prove the recursive feasibility and stability of the proposed learning based RMPC. Finally, the simulation results are carried out on both single and multiple DG (Distributed Generation) units.