Separating Feeder Demand Into Components Using Substation, Feeder, and Smart Meter Measurements

Real-time, feeder-level energy disaggregation seeks to separate the measured total demand of a distribution feeder into components (e.g., into the aggregate demand-responsive load and remaining demand on the feeder, or into the aggregate generation and demand on the feeder). It can benefit distribution system operators and demand response providers by providing real-time information about balancing reserve requirements or the aggregate demand-responsive load, among other applications. In this work, we develop a feeder-level energy disaggregation algorithm that uses measurements from multiple sources that are available on different timescales. The algorithm is based on online learning and uses sensor fusion to incorporate output equations associated with disparate active and reactive power flow measurements, complex bus voltage measurements, and residential smart meter measurements. The algorithm also uses distribution substation measurements. Case studies simulate a three-phase distribution feeder model with unbalanced loads, where the active and reactive demand of the loads are modeled as a function of voltage and temperature. These models are parameterized using real-world active power demand data at each time-step. Results indicate that disaggregating three demand components is possible using feeder head active power measurements, that including feeder head reactive power measurements improves disaggregation accuracy by 32% on average, and that including intermittent real-time smart meter measurements further improves disaggregation accuracy.