Sequential Detection and Prediction for Solar Situation Awareness in Power Networks

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The overarching research objective of this project is to develop statistical tools to help power network operators in increasing the solar generator situational awareness. The goals include inferring the solar power generators, their locations, and status, from the real-time power supply and consumption data using sequential data. We take a novel approach leveraging the fact that the on and off of the solar generators will introduce change-points in the difference time series of the power supply and consumption, and propose a statistical framework for solar situational awareness by detecting change-points and estimating their magnitudes from sequential data. We use a bottom-up approach, which is a natural fit to the hierarchical structure of the power networks: we detect change-points at the unit level, and then aggregate them on a network level, using multi-dimensional point process model, as well as considering the inherent sparse and low-dimensional nature of the observations. The change-point detection will be combined with multi-fidelity models to achieve network level prediction of power generation. The results will be verified on simulated high-fidelity time-series and real-data over dynamic power networks.Solar power installations have been increasing in both the residential and commercial areas. However, it is very difficult to know the exact numbers, locations, installed capacities, and production status of these PV panels, especially due to the increasing number of behind-the-meter installations of PV panels and the intrinsic stochasticity in solar production. Not knowing the status of the solar generators in the network can pose a significant challenge to stability and security of power distribution and transmission systems. The precise knowledge of the number, location, capacity, and operational status of residential PV units within a distribution system will be very critical to the daily operation and planning of distribution system and eventually that of transmission systems. Currently, there is no effective way to infer in real-time the solar generators' status in a large-scale power network. The proposed research will change the landscape and significantly advance the state-of-the-art in using statistical methods for solar situational awareness. Utility companies, industry regulators and solar panel marketers are a few of the groups that will benefit from the algorithm developed for situational awareness. For example, having access to detailed information of the status of solar PV production in a given neighborhood can enable local utilities to better balance the area's power supply and demand, improve the quality of electricity service to end users, and increase the reliability and security of distribution power grids. The proposed research will be tightly integrated with education components, and the research results will be made available to national labs as open source software and research findings will be disseminated via conferences and journal publications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.