This project developed and demonstrated an AI-driven energy trading and management platform to optimise use of flexible energy assets as part of a smart local energy system.

Lead Organisation

QBots Energy







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Summary: impacts and findings

In this project, Keele University and Qbots together developed a local energy trading and optimisation platform to balance energy supply, storage and demand between buildings at the university campus. It showed that such a system can increase the use of locally-generated energy, minimise energy use from the national grid, and reduce stresses on the network from new demands.

Project aims and approach

Distribution system operators aim to reward customer flexibility in energy use, but this requires customers to have systems that can respond to variable market price signals.

In the Urban-X project, Keele University worked with Qbots Energy to demonstrate a local energy trading and optimisation platform.

Such a platform could form part of an innovative energy service proposition enabling wider adoption of smart grid systems especially among small businesses and organisations. The vision was to enable local energy markets that incentivise local generation and demand management while reducing costs for consumers.

Qbots Energy’s technology uses data from half-hourly and smart-metered sites, energy monitoring sensors, building management systems, solar generators and battery storage, to enable energy trading and optimisation.

The Urban-X project developed a local system trading energy between buildings at Keele University. The team created a grid-connected test-bed, using data from the university buildings run through an artificial intelligence based intermediary service platform.

The system balances the supply and demand of energy across the network by optimising energy use and storage in real time – maximising the use of locally generated energy, minimising buying from the national grid, and reducing stresses on the network from demands such as electric vehicle charging.


  • QBots Energy
  • University of Keele


April 2020 to June 2022 

Achievements and barriers

The smart energy trading platform for SMEs trialled in this project was found to reduce the cost of green energy by 25% by using Internet of Things based distributed optimisation of storage, generation and other flexible electricity assets, while saving carbon emissions.

Through this project, Qbots Energy’s approach to the local energy market model was established and validated with the market – including with energy supplier partners, customers, generators, investors and other partners.

The Q Energy solution enables SMEs to create ‘buying clubs’ leveraging the demand from a group of customer sites and then optimally matching demand every 30 minutes with local solar and wind farms. Combining battery storage and demand flexibility, this approach can reduce imbalance costs, minimise energy buying from wholesale markets at carbon-intensive periods, and maximise buying when prices are low or negative, delivering savings for all.

A further trial took place with a commercial customer in Greater Manchester, with potential future customers including city centre developments, universities, business parks, science parks and airports.


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