The deployment of renewable resources in distributed grid systems poses a set of new challenges mainly due to their variability and dependency on climate parameters, which can have a major impact on the system parameters that are needed to measure power flow and state estimation. More precisely, a state estimator aims at providing estimated values of the system parameters roughly equal to the true values by eliminating measurement errors. In the presence of renewables, many studies in the past have incorporated the system uncertainties for power flow measurement and state estimation.
Stochastic power flow (SPF) and forecast-aided state estimation, also called dynamic state estimation (DSE) should take into consideration the uncertain behavior of power generations and loads. Our analytical-based MSE state estimator aims at achieving minimum mean squared error (MMSE) and benefits from the prior study of SPF, which involves the probability density functions (PDF’s) of the system parameters. The main advantage of this estimator is based on its ability to instantaneously incorporate the dynamics of the power system. Since the proposed MMSE-based estimator is an analytical approach, it can provide estimated values instantaneously. In addition, its performance remains good, even with a limited number of measurements, which is important in the case of a wide system.