Enhancing Distribution System Situational Awareness Using Smart Meters

Surendra Bajagain

doi:10.7273/000006318

The increasing levels of distributed energy resources (DERs) penetrations can resultin significant operational challenges at the power distribution level. This calls for enhancing distribution system level situational awareness to assist distribution system operators in making the best operational decisions under disturbances. In this work, enhancing situational awareness implies extending the visibility of the power distribution system beyond the substation. Extensive roll-out of smart meters allows utilities to collect significantly more real-time data, which can be leveraged to develop new monitoring tools. This work uses smart meter data available at the grid-edge to enhance the power distribution system’s situational awareness. Specifically, within a three-level SA framework that includes perception (level-1), comprehension (level-2) and projection (level-3), this work makes the following contributions with regard to level-2 SA tools. First, we evaluate the impacts of inadequate end-use load models and associated nonlinearities on system state variables. To this end, we compare the harmonicdistortions due to a detailed and approximated constant current model for nonlinear residential loads on North American residential distribution systems. North American residential distribution system with split-phase configurations is usually modeled as a single-phase equivalent model. The harmonic distortion analysis with the single-phase equivalent network model is found to underestimate the current distortions and overestimate the voltage distortions. The analysis with three different approximate models for an unbalanced split-phase residential distribution system shows that the balanced split-phase residential distribution system is a reasonably accurate representation of the unbalanced split-phase residential distribution system. Second, we evaluate the impacts of measurement non-idealities on system-level states. To this end, we evaluate the effects of the following smart-meter attributes: measurement interval, time synchronization error, meter bias, and measurement noise on distribution system voltage and total loss. The analysis shows that temporal aggregation of smart meter data can alter the data distribution. The time synchronization error for household smart meters does not follow Gaussian distribution and the incomplete data reporting creates uncertainty in system-level states. Third, to enhance the real-time SA of distribution systems, we develop a state estimation algorithm that provides an integrated monitoring of primary and secondary feeders. This approach enables us to use measurement information from the smart meters at the grid-edge by appropriately modeling the secondary feeder beyond the distribution-level service transformer. The accuracy of the state estimation algorithm is tested for different levels of measurement and model inaccuracies and for non-gaussian measurement error distributions. Finally, to enhance distribution-level SA during outage conditions, we develop a spectral clustering-based outage detection algorithm that is simple to implement and solves efficiently by leveraging the smart meter outage notifications and forecasted load data. In conclusion, this thesis highlights and develops tools to effectively use smart meters to enhance distribution-level SA beyond primary feeders to the customer level.

Enhancing Distribution System Situational Awareness Using Smart Meters

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