Solar Retina: Crowd-Sourcing "Behind-the-Meter" Solar PV Data

Electricity generation from solar installations continues to grow at a furious rate, with a large share of new solar capacity being installed on residential and commercial buildings. This rise in solar capacity on the distribution grid is beginning to create real challenges for grid planners and operators. Increasing solar generation at the distribution edge can affect power quality and reliability, including, for example, reverse power-flow and voltage flicker. Unfortunately, utility distribution grid planners lack visibility into behind-the-meter distributed solar PVs, making assessing these power quality risks tricky and raising the soft costs of solar power. At base, this behind-the-meter blindness is a data problem. Utilities have been trying to fill the data gap by utilizing models of solar generation, which are commonly based on irradiance readings by either ground sensors or satellites. However, utilities and grid operators are uncomfortable with the imprecision of these irradiance-based solar distribution planning tools, noting that current distributed solar planning tools do not provide the reliable, realistic, and definitive analysis they need to plan for a high-penetration solar future. Using crowd-sourced solar generation data from actual PVs, the SolarRetina will provide utilities and grid operators with needed data about how behind-the-meter solar generation interacts with and affect their distribution grids, instantly and at low-cost. When completed, the SolarRetina platform will provide utilities with a subscription, web-based application that allows them to instantly obtain important information about the generation profile of the behind-the- meter systems in a location of their choosing, like a feeder line or circuit, within their service territory. Under this proposal, we will develop a fully integrated Alpha Prototype that automatically acquires data directly from solar owners, stores and processes the data, and provides utility specific analytics in a customer friendly user-interface.