Buildings in the UK guzzle 40% of all produced energy and pump out 40% of carbon emissions, yet no one has figured out how to manage millions of them in real time to balance the grid while keeping individual homes comfortable. Current control systems treat buildings as passive consumers or try to impose a single central goal, which fails because each building has its own needs and the grid ties them together. This project will develop distributed control methods that let buildings dynamically form coalitions—shifting energy use between neighbours based on live data and changing conditions—rather than following a fixed top-down plan. The team will extend data-driven behavioural modelling, distributed optimisation, and reinforcement learning techniques from lab-scale demonstrations toward real-world deployment. If successful, this could transform how the UK’s energy grid operates. Instead of building more power stations to meet peak demand, the grid could rely on flexible building clusters that automatically reduce or shift consumption. The close collaboration with UK Power Networks and SSE Energy Solutions means the methods will be tested on real data and practical constraints, not just theoretical models.
View original technical description
Buildings are responsible for about 40% of carbon emissions and consume about 40% of all produced energy in the UK. Transforming how buildings use and produce energy is a fundamental steppingstone to achieving net-zero carbon emissions and sustainable economic growth. The abundance of data, flexible technologies and advanced control approaches open exciting opportunities to achieve cost-effective system decarbonisation and create places where people love to live for the increased comfort standards. A radical transformation of the building sector is possible using real-time monitoring, learning capabilities, advanced control strategies, distributed optimisation and coordination. Our research demonstrates that the energy consumption of buildings has a vast potential to be flexible and support an efficient grid operation. However, it is unclear how to design distributed control architectures and schemes managing millions of buildings in real-time to simultaneously achieve societal and individual consumer benefits. The proposed project seeks answers to critical open questions: How can we efficiently harness the adaptability of millions of diverse buildings to support the entire energy system while optimizing individual objectives concurrently? How can we harness data reliably to develop scalable, transferable control methods, bringing them closer to practical application? The aim of this research is to develop distributed solutions to reliably manage energy use across groups of buildings. We will consider for the first time the advantage of dynamically forming coalitions according to the environment's variability and individual real-time energy needs. To realise this, we have set the following objectives: 1. Extend the latest data-driven behavioural control and uncertainty modelling approaches, state-of-the-art distributed optimisation methods and reinforcement learning techniques. These methods should be scalable to bridge the gap between lab-scale demonstrations and real-world implementation. 2. Apply these innovative methods to models of building clusters. This will offer insights for shaping policies and driving innovation, bolstering their role in supporting the entire energy system. The close collaboration with UK Power Networks and SSE Energy Solutions will support the data-driven modelling and development of novel adaptive distributed control architectures to maximise the research output impact. A pressing question we will address is how to achieve both individual and societal benefits. Existing distributed solutions are focused on directly achieving a centralised objective. Such solutions do not fit the objectives of simultaneously achieving societal and individual objectives. Substantial performance limitations arise when pursuing exclusively conflicting objectives, since the buildings connected to the grid are strongly coupled.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know