An Overview of Distributed Microgrid State Estimation and Control for Smart Grids

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.

Controlling the renewable microgrid using semidefinite programming technique

“…Compute the initial state value using (14) Compute the Kalman gain using (11) Update state estimation and covariance matrix using (12) and (13) …”

“…Even though it considers the system impairments, it assumes that the communication is fault-free between the power system and control center. Moreover, the Kalman filter (KF) together with the simple linearquadratic-Gaussian control method is explored in [3,11,12]. To utilize the KF estimator, one needs to specify the initial state value.…”

Kalman Filter Based Microgrid State Estimation Using the Internet of Things Communication Network

“…By exploiting the two-way communications, it becomes possible to replace the current power system with more intelligent infrastructure [2]. Consequently, the smart control centre feels the requirement of a robust and scalable technique for state estimation (SE) that allows continuous and accurate wide-area real-time monitoring of power system operation and customer utilization of smart grid [3], [4]. To achieve this end, the goals and ideas of such intelligent energy management systems are parallel to those of the internet of things (IoT), which can exploit reasonable security and privacy of distributed energy resources (DERs) messages, seamless interoperability and far-reaching connectivity [5].…”

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems

“…Such outliers that are far away from the expected measuring data create the potential risk of misleading the estimated result [6]. The WLS, primarily based on a single snap shot of measurements, is not a robust method and it may lead to a biased estimated result even when a single bad measurement occurs [3,7]. One common way to alleviate the influence of outliers is to use more snap shots of measurements.…”