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. 2015 Feb 12;15(2):4302-25.
doi: 10.3390/s150204302.

An overview of distributed microgrid state estimation and control for smart grids

Md Masud Rana  1 , Li Li  2
Affiliations

An overview of distributed microgrid state estimation and control for smart grids

Md Masud Rana et al. Sensors (Basel). .

Abstract

Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method.

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Figures

Figure 1.
Figure 1.
The main characteristics of a smart grid.
Figure 2.
Figure 2.
System architecture for the smart grid paradigm.
Figure 3.
Figure 3.
Different communication standards and protocols of a smart grid.
Figure 4.
Figure 4.
Block diagram of the microgrid incorporating distributed energy resources.
Figure 5.
Figure 5.
DER state information is coordinated by wireless sensor in the smart grid.
Figure 6.
Figure 6.
Example of the system framework for smart grids deployment.
Figure 7.
Figure 7.
A linear decentralized WSN based DER monitoring system.
Figure 8.
Figure 8.
The bipartite graph for one sensor that can sense one system state directly.
Figure 9.
Figure 9.
Model for communication systems and dynamic state estimation.
Figure 10.
Figure 10.
The role of the WSN in the optimization of the smart grid control.
Figure 11.
Figure 11.
Point common coupling (PCC) voltage estimation for υ1 using the proposed KF SE.
Figure 12.
Figure 12.
PCC voltage estimation for υ2 using the proposed KF SE.
Figure 13.
Figure 13.
PCC voltage estimation for υ3 using the proposed KF SE.
Figure 14.
Figure 14.
PCC voltage estimation for υ1 using the proposed KF SE with two faulty sensors.
Figure 15.
Figure 15.
PCC voltage estimation for υ2 using the proposed KF SE with two faulty sensors.
Figure 16.
Figure 16.
PCC voltage estimation for υ3 using the proposed KF SE with two faulty sensors.
Figure 17.
Figure 17.
PCC voltage control using the proposed control method.
See this image and copyright information in PMC

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