Why integrating science, data centres and clean energy infrastructure is critical to future growth
Simon Wyatt, Partner, Cundall
The UK’s science and technology clusters are entering a new phase of growth, driven by expansion across life sciences, advanced manufacturing, AI, quantum and materials research. However, the energy and infrastructure systems that support these activities, particularly power and heat networks, have not evolved at the same pace. This creates both a constraint and a strategic opportunity. By integrating science campuses with data centres and clean energy infrastructure, the UK can address power limitations while unlocking greater efficiency, resilience and long term economic value.
Many of the UK’s leading clusters, including Cambridge, Oxford, London, Harwell and emerging regional hubs, are located in areas where electricity networks are already constrained. In these locations, grid capacity is no longer a background consideration but a primary determinant of whether projects can proceed. Developers are increasingly facing multi-year connection timelines, limited substation headroom, and material uncertainty over when capacity will become available.
At the same time, demand is increasing sharply. AI driven research requires high-performance local compute, laboratories operate continuously through automation, and secure data environments are increasingly colocated with research institutions. The scale of this demand is significant: modern hyperscale data centres now routinely exceed 100 MW, with major campus developments reaching 200 to 500 MW and, in some cases, approaching 1 GW at full build out. This is equivalent to introducing the electrical load of a large industrial cluster or small city into already constrained networks. The result is a structural mismatch between demand growth and available capacity, where access to power is becoming the critical bottleneck to science cluster expansion.
Critically, this is not simply a question of current demand, but of future competitiveness. Global leadership in science and AI will depend on access to large scale, high performance compute, and therefore on the energy systems that underpin it. Countries that can plan for and deliver gigawatt scale compute infrastructure will shape the next generation of research and innovation. Those that cannot will be forced to rely on external capacity, with implications for sovereignty, cost and speed of discovery.
At a national level, the pressure is even more pronounced. The UK currently has around 1.6 GW of installed data centre capacity, yet projects in the connection queue are collectively seeking tens of gigawatts of additional capacity. In aggregate, these requests exceed current peak electricity demand, highlighting the scale of the challenge facing the grid. Data centres already account for around 2 percent of UK electricity demand today, but this could rise towards 10 percent by 2030 as AI workloads scale. This is not incremental growth; it represents a fundamental shift in the structure of national electricity demand and reinforces the need to reduce reliance on centralised supply alone.
In this context, grid reinforcement on its own will not unlock the required capacity at the pace demanded by science and technology growth. Network upgrades are capital intensive, geographically constrained, and subject to long delivery timescales. Without a more integrated and forward looking approach, there is a material risk that infrastructure constraints limit the UK’s ability to host the next generation of AI and research platforms.
Data centres must therefore be considered as integrated energy infrastructure, not standalone digital assets. For science clusters, they are core enabling systems, providing low latency compute, sovereign data environments and control over energy and thermal flows. However, the rise of AI is further intensifying the challenge. Traditional data centre racks operated at around 5 to 10 kW, whereas AI ready infrastructure now demands 30 to 100 kW per rack, with leading edge deployments exceeding this range. This step change in density means significantly more power concentrated in smaller footprints, increasing pressure on already constrained connection points.
The response must therefore shift towards system level integration. Internationally, this is already visible, with major technology providers developing dedicated energy systems combining renewables, storage and flexible generation to support compute demand. In the UK context, this translates into integrated campus approaches including colocated renewables, energy storage, private wire networks, microgrids and smart demand management aligned to research cycles. Critically, these solutions do not just supply additional power, they reduce peak grid dependency, improve connection viability, and enable projects to proceed ahead of full network reinforcement.
This is particularly important when considered alongside the UK’s heat decarbonisation challenge. Heat remains one of the largest contributors to UK energy demand and carbon emissions, and the transition away from fossil fuel heating is driving increased electrification. Without intervention, this will further intensify pressure on already constrained electricity networks.
In this context, waste heat recovery from data centres becomes a strategic asset rather than a secondary benefit. Modern data centres, particularly those supporting AI workloads, generate large volumes of consistent, high grade heat. When integrated into campus and local energy systems, this heat can be reused to support laboratories, academic buildings, district heating networks and adjacent developments. This directly supports the decarbonisation of heat by displacing fossil fuel demand, while also reducing the need for additional electrical load from alternative low carbon solutions such as heat pumps.
The system level benefits are significant. Waste heat reuse reduces total energy demand across the cluster, lowers operating costs, improves carbon performance and alleviates pressure on constrained electricity networks. It also aligns with the UK’s shift towards more localised, integrated energy systems, where heat and power are planned together rather than in isolation.
Importantly, this approach transforms data centres from perceived infrastructure challenges into contributors to local energy systems. When combined with a mix of building typologies that balance cooling dominated and heating dominated loads, there is an opportunity to optimise energy flows at a campus or district scale, reducing both peak demand and system inefficiencies while delivering wider social and economic co benefits.
Individually, science, data centre and energy sectors each face structural constraints. Together, they offer a pathway to overcome them. An integrated, place based approach enables shared planning of compute, power and heat, targeted investment in grid upgrades where they add most value, and faster delivery of science infrastructure without being wholly dependent on upstream reinforcement.
Ultimately, the next generation of UK science clusters will be shaped as much by infrastructure strategy as by research capability. Access to power and low carbon heat is now a gating factor for growth, and preparedness for the scale of AI driven energy demand will determine whether the UK remains at the forefront of global research. By aligning science, digital infrastructure and clean energy, and by treating waste heat as a strategic asset within the wider energy system, the UK can enable faster development, improve resilience, and create globally competitive, low carbon science clusters capable of supporting innovation at scale in the AI era.
