Distributed Estimation of the Operating State of a Single-Bus DC MicroGrid without an External Communication Interface
This work addresses the challenge of real-time state estimation in microgrids with high renewable penetration, offering a decentralized solution that eliminates the need for external communication, though it appears incremental as it builds on existing estimation methods.
The authors tackled the problem of estimating renewable power generation and demand in single-bus DC microgrids without external communication, proposing a decentralized Maximum Likelihood Estimator that uses controlled voltage disturbances for distributed training, with numerical results showing promising performance.
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.