Octopus Energy has confirmed its second “Saving Sessions” trial saw customers reduce their energy demand by a collective 112MW.
This figure is higher than the energy reduced in the first trial on 15 November which saw a collective 108MW of grid flexibility provided, the company said.
Around 200,000 households took part in the first demand flexibility scheme trial led by Octopus with many of those having returned for the second trial on the 22 November.
Between 5-6pm on 15 November, 200,000 households reduced their electricity use, with the average household lowering its demand by over half (59%).
Octopus Energy has stated that the second “Saving Sessions” flexibility scheme retained 94% of its participants from the first trial with the top 5% saving an average of £9 across the two sessions. On average households received over £1 for the energy they shift out for the duration of the scheme period.
“We are thrilled by these first results. By cooking tea a bit later or taking the dog for a walk, people could save a pound or two. The savings may be small, but just like the 20% off a pack of sausages in the supermarket, it all adds up,” said Alex Schoch, head of flexibility at Octopus Energy.
“We are proud to have pioneered this trailblazing scheme which has the power to change the face of the grid forever, making energy cleaner and cheaper for everyone.”
Across the two first test sessions, collectively, Octopus customers got paid over £525,000 for providing flexibility to the grid. These tests are set to continue for another 10 sessions that will be announced over the winter period.
More than 420,000 customers of Octopus’ 1.4 million eligible electric smart meter customers have already signed up to the scheme, which is running from November to March next year and is still accepting new sign ups.
The National Grid ESO’s demand flexibility service was set out in the energy regulator’s Winter Outlook report in October. Within this, the operator stated that while there is a risk of blackouts, it is “cautiously confident” that it had the tools to manage the predicted constraints.
