Using information entropy to quantify uncertainty in distribution networks

Information about the certainty of the predicted state of a network is crucial for the effective management of a modern power system. In transmission systems, observability analysis is used to assess whether the estimated state is valid, based on the available measurements. In distribution systems there is no method for quantifying the uncertainty in a modelled state; this places limitations on how the modelled state can be used. This paper explores a new method of quantifying the uncertainty in a state estimation solution, with information entropy. A Monte Carlo simulation approach was used to determine the probability of the network being in a specific state. The proposed approach allows for the objective evaluation of the certainty of a state solution in distribution networks, which can be easily interpreted by distribution network service providers. Case studies were conducted, results are resented and discussed.