Document Type
Article
Version Deposited
Published Version
Publication Date
10-12-2021
Publication Title
IET Generation, Transmission, and Distribution
DOI
https://doi.org/10.1049/gtd2.12297
Abstract
This paper proposes an enhanced three-layer predictive hierarchical power management framework for secure and economic operation of islanded microgrids. The tertiary control, guaranteeing the microgrid economic operation, is built upon the semi-definite programming-based AC optimal power flow model, which periodically sends power references to secondary control. To mitigate uncertainties arising from renewable generations and loads, a centralized linear model predictive control (MPC) controller is proposed and implemented for secondary control. The MPC controller can effectively regulate the microgrid system frequency by closely tracking reference signals from the tertiary controller with low computational complexity. Droop-based primary controllers are implemented to coordinate with the secondary MPC controller to balance the system in real time. Both synchronous generators (SGs) and solar photovoltaics (PVs) are simulated in the microgrid power management framework. A unified linear input-state estimator (ULISE) is proposed for SG state variable estimation and control anomaly detection due to compromised cyber-physical system components, etc. Simulation results demonstrated that SG states can be accurately estimated, while inconsistency in control signals can be effectively detected for an enhanced MPC. Furthermore, comparing with conventional proportional-integral (PI) control, the proposed hierarchical power management scheme exhibits superior frequency regulation capability whilst maintaining lower system operating costs.
Recommended Citation
Zhang, J., Li, J., Wang, N., & Wu, B. (2021) An enhanced predictive hierarchical power management framework for islanded microgrids. IET Generation, Transmission & Distribution 2021; 1–14. https://doi.org/10.1049/gtd2.12297
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Comments
This is an open access article with a Creative Commons license.