  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.
