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    Home » Government looks at tech to tackle peak electricity demand
    Tech

    Government looks at tech to tackle peak electricity demand

    Savannah HeraldBy Savannah HeraldNovember 20, 20253 Mins Read
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    Government looks at tech to tackle peak electricity demand
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    Tech Trends & Innovation: The Latest in Tech News

    Key takeaways
    • £4m funding supports tech projects to manage the UK electricity grid and reduce peak demand.
    • Clean Energy: 2GW Peak Time Flexibility challenge seeks solutions to shift evening and weekend demand.
    • AI and smart meter plus weather data forecasting to predict and reduce peak electricity usage.
    • Automation of heating, cooling, and vehicle-to-grid (EV) tech to use cheap, clean power.
    • Scaling simulations and modelling aims to speed market-ready products and cut long-term consumer bills.

    Science minister Patrick Vallance has unveiled £4m in funding to support technology projects that can help manage the UK’s electricity grid.

    Vallance has called on technology and artificial intelligence (AI) specialists to design ways to help reduce electricity bills by working on how to cut peak demand for grid electricity by two gigawatts. According to the Department for Science, Innovation and Technology (DSIT), reducing peak electricity usage could slash UK reliance on expensive, polluting fossil fuels at the busiest times, which would boost energy security and help cut energy costs.

    “This is a challenge with real impact – if we get it right, we’ll save families money, protect the planet and make the UK a clean energy superpower,” he said.

    The Clean Energy: 2GW Peak Time Flexibility challenge is the first of five to be announced as part of the R&D Missions Accelerator Programme – backed by the £500m set out in the Spending Review. The project will be led by UK Research and Innovation (UKRI) in collaboration with the Department for Energy Security and Net Zero (DESNZ), which is responsible for leading the government’s clean energy mission.

    The five-year challenge involves both shifting electricity demand from peak times during the evening and weekends, when people are at home, and better forecasting. For instance, combining smart meter data with weather forecasts, AI could be used to predict how much energy will be used ahead of time.

    The government is also considering how heating or cooling buildings could be automated in a way that makes the most of when clean energy is most available, and at its cheapest.

    Another technology the government is considering is the vehicle-to-grid capability being integrated into some electric vehicles (EVs). Here, the EV’s large battery can be charged when electricity is at its cheapest and cleanest, and, assuming the car remains plugged in, can then be sent back to the grid to help manage peak electricity demand.

    DSIT said these initiatives could reduce the need to build more grid infrastructure and new power plants, leading to a reduction in consumer bills in the longer term and supporting Labour’s mission to make Britain a clean energy superpower by 2030.

    The initial funding has been allocated to investigate how current approaches to grid management can be scaled up. DSIT said this will include building simulation and modelling capability so successful products can be brought to market more quickly.

    “We are working to build a more flexible electricity system, giving households more choice and control over when and how they use energy,” said minister for climate Kerry McCarthy.

    “This new challenge will help deliver that, exploiting the exciting potential of AI and other cutting-edge tech to help more people access flexible tariffs and save on bills as part of our plan for change.”

    DSIT plans to unveil more challenges over the coming weeks and months to encourage experts to apply their knowledge to tackle challenges including keeping streets safe, building an NHS fit for the future and unlocking growth to deliver on Labour’s plan for change.

    Read the full article from the original source


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