  Today's power generation and distribution networks are quickly moving toward
automated control and integration of renewable resources - a complex,
integrated system termed the Smart Grid. A key component in planning and
managing of Smart Grids is State Estimation (SE). The state-of-the art SE
technologies today operate on the basis of slow varying dynamics of the current
network and make simplifying linearity assumptions. However, the integration of
smart readers and green resources will result in significant non-linearity and
unpredictability in the network. Therefore in future Smart Grids, there is need
for ever more accurate and real-time algorithms. In this work, we propose and
examine new SE methods that aim to achieve these measures by approximating the
true distribution of the state variables, rather than a linearized version as
done for instance in Kalman filtering. Through simulations we show that in the
presence of non-linearities and non-Gaussian noise, our general SE framework
improves accuracy where linear and Kalman-like filters exhibit impaired
performance.
