  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.
