Distributed Dynamic State Estimation for Microgrids

TL;DR

This paper introduces a distributed dynamic state estimation method for microgrids that uses a state-space model with branch currents as states and bus voltages as inputs, enabling accurate, localized, and distributed estimation.

Contribution

It proposes a novel Kalman-based filtering scheme for microgrid state estimation that estimates states and inputs simultaneously in a distributed manner.

Findings

Effective in microgrid scenarios with load-induced voltage fluctuations

Distributed estimation reduces communication overhead

Simulation confirms feasibility and performance

Abstract

Conventionally, the dynamic state estimation of variables in power networks is performed based on the forecasting-aided model of bus voltages. This approach is effective in the stiff grids at the transmission level, where the bus voltages are less sensitive to variations of the load. However, in microgrids, bus voltages can fluctuate significantly under load changes, the forecasting-aided model may not sufficiently accurate. To resolve this problem, this paper proposes a dynamic state estimation scheme for microgrids using the state-space model derived from differential equations of power networks. In the proposed scheme, the branch currents are the state variables, whereas the bus voltages become the inputs which can vary freely with loads. As a result, the entire microgrids system can be partitioned into local areas, where neighbor areas share the common inputs. The proposed estimation scheme then can be implemented in a distributed manner. A novel Kalman-based filtering method is derived to estimate both states and inputs simultaneously. Only information of common inputs is exchanged between neighboring estimators. Simulation results of the 13-bus Potsdam microgrid (New York State) are provided to prove the feasibility and performances of the proposed scheme.