Distributed Estimation of the Operating State of a Single-Bus DC MicroGrid without an External Communication Interface

TL;DR

This paper introduces a decentralized maximum likelihood estimation method for determining the operating state of a single-bus DC MicroGrid without external communication, leveraging local voltage measurements and controlled disturbances.

Contribution

It presents a novel decentralized estimation approach that does not require external communication, using voltage disturbances and a non-linear model for improved MicroGrid state estimation.

Findings

The proposed MLE algorithm performs well in simulations.

The method effectively estimates generation and load states.

It provides conditions for the existence of a global optimum.

Abstract

We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters. The solution relies on the fact that the primary control parameters are set in accordance with the local power generation status of the generators. Therefore, the steady state voltage is inherently dependent on the generation capacities and the load, through a non-linear parametric model, which can be estimated. To have a well conditioned estimation problem, our solution avoids the use of an external communication interface and utilizes controlled voltage disturbances to perform distributed training. Using this tool, we develop an efficient, decentralized Maximum Likelihood Estimator (MLE) and formulate the sufficient condition for the existence of the globally optimal solution. The numerical results illustrate the promising performance of our MLE algorithm.