National Grid ESO has teamed up with Sheffield Solar (at the University of Sheffield) on the PV_Live initiative, which captures data from a network of solar – or photovoltaic (PV) – panels on thousands of homes, schools, factories and fields to feed into the ESO’s models to predict and balance supply and demand.
It’s one of a series of projects that the ESO has been working on with innovation partners to improve renewable energy forecasting, alongside a link-up with the Met Office to boost weather forecast accuracy and a collaboration with the Alan Turing Institute to harness the power of machine learning for solar power prediction.
The innovative project owes its inspiration to a community orienteering initiative in the Peak District, which crowdsourced time and distance data onto a website for everyone to benefit from. It struck Dr Alastair Buckley – a physics lecturer at the University of Sheffield and orienteering enthusiast – that the same could be done with PV data, and the Microgen database was born at the university.
Work later started with the ESO to capture the data Alastair and the research team at Sheffield were accruing from this previously invisible network of solar panels, and today – with funding from Ofgem’s Network Innovation Allowance (NIA) – the PV_Live project continues to support the ESO as it balances the electricity system in real time.
PV_Live mines Sheffield’s Microgen database, as well as a data stream from smart tech energy company, Passiv Systems, to get generation data from a sample of PV systems for a given half hour – the time period ESO’s modelling works to – and then scales it up to give representative real-time PV performance across the country for ESO to draw on.
“The PV data that PV_Live provides is critical to the management of the transmission system,” says Kevin Tilley, who project manages the initiative on behalf of the ESO. “We use the PV data to help forecast and monitor the national power requirements on the network in real time, and shortly after the first delivery of the data, we were able to improve our forecasts of these flows for the first time in years. Engineers in our planning teams have also been able to predict the regional flows more accurately around the network, enabling safer and more economic operation.”
For more information on the PV_Live project and the story behind it, read the feature The invisibles: tracking solar energy on the University of Sheffield’s website.