Abstract: A computer-implemented method for solar energy disaggregation at a node of a distribution system includes obtaining first data including samples of averaged net power measured by a meter installed at the node and second data including samples of solar irradiation local to the node averaged over intervals temporally correlated with the first data. A physics-based model of the node is trained using the first and second data, to optimize a set of model parameters. The physics-based model is defined such that an average net power includes a composite of an average solar generation and an average power consumption at the node and the average solar generation is modeled as a function of an average solar irradiation. An aggregate solar generation at the node is estimated/predicted from actual irradiation data/irradiation forecast data local to the node using a model parameter optimized by the training.

Abstract: A system and method for estimation of grid impedance in a power converter controller is disclosed in which a current controller is provided to inject an injection current while operating online and coupled to a power grid at a point of common coupling (PCC), the injection current being benign to an operating current of the power converter. A voltage controller is provided to regulate input voltage of the power converter. An impedance module is provided to determine parameters for the injection current, including injection interval and duration, current magnitude, current frequency, and symmetrical components. The impedance module may extract measurements of current and voltage at the PCC at an operating point shifted from a previous operating point in response to the injection current. The impedance module may extract symmetrical components of current and voltage measurements, and compute estimated grid impedance based on the extracted symmetrical components.

Abstract: A method for estimating an admittance matrix for a transmission network comprises estimating in an upper layer time-series data for hidden nodes in the transmission network, computing in a lower layer an estimated admittance matrix for the transmission network and iteratively exchanging information between the upper layer and the second lower layer to produce a final admittance matrix for the transmission matrix. In some embodiments, the method may further comprise spatially dividing the transmission network into a plurality of subnets in the upper layer and estimating the time-series data for the hidden nodes for each subnet. Estimating the times series data for the hidden nodes for each subnet in parallel.

Abstract: A system and method for estimation of grid impedance in a power converter controller is disclosed in which a current controller is provided to inject an injection current while operating online and coupled to a power grid at a point of common coupling (PCC), the injection current being benign to an operating current of the power converter. A voltage controller is provided to regulate input voltage of the power converter. An impedance module is provided to determine parameters for the injection current, including injection interval and duration, current magnitude, current frequency, and symmetrical components. The impedance module may extract measurements of current and voltage at the PCC at an operating point shifted from a previous operating point in response to the injection current. The impedance module may extract symmetrical components of current and voltage measurements, and compute estimated grid impedance based on the extracted symmetrical components.

Abstract: A method for estimating an admittance matrix for a transmission network comprises estimating in an upper layer time-series data for hidden nodes in the transmission network, computing in a lower layer an estimated admittance matrix for the transmission network and iteratively exchanging information between the upper layer and the second lower layer to produce a final admittance matrix for the transmission matrix. In some embodiments, the method may further comprise spatially dividing the transmission network into a plurality of subnets in the upper layer and estimating the time-series data for the hidden nodes for each subnet. Estimating the times series data for the hidden nodes for each subnet in parallel.