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Real-Time Distribution Grid State Estimation with Limited Sensors and Load Forecasting

High penetration levels of distributed generation (DG) and electric vehicles (EVs) diversify power flow and bring uncertainty to distribution networks, making planning and control more involved for distribution system operators (DSOs). The increased risk of constraint violation triggers the need to augment forecasts with real- time state estimation. This is economically and technically challenging since it requires investing in a large number of sensors and these have to communicate with often older and slower supervisory control and data acquisition (SCADA) systems. We address distribution grid state estimation via combining only a limited set of sensors with load forecast information. It revisits open problems in a recent paper that proposes a Bayesian estimation scheme. We derive the estimator for balanced power networks via rigorous modeling. An off-line analysis of load aggregation, forecast accuracy and number of sensors provides concrete engineering trade-offs to determine the optimal number of sensors for a desired accuracy. This estimation procedure can be used in real time as an observer for control problems or off-line for planning purposes to asses the effect of DG or EVs on specific network components.