Britain's grid is being asked to manage millions of individual household decisions, not a single demand curve. Yet much of the planning data still relies on averages and archetypes that obscure the local detail that matters most.
- Why national averages misrepresent real demand
- How looped supply cables block heat pump installs at scale
- The barrier currently delaying retrofit at scale
This month sees the National Energy Systems Operator (NESO) run a series of regional workshops that bring together stakeholders to consider the challenges and opportunities the system and its users – all of us - face.
Electrification of heat, the rapid growth of distributed technologies, and demand-side interventions are all changing not just how much energy households use - but when and where they use it. For strategic energy planning, this creates a critical challenge: the system must be designed not for today’s demand, but for a set of plausible, highly localised future scenarios.
At the centre of this challenge lies an often-underutilised asset: granular housing stock data.
A system under transformation
The UK energy system is entering a period of structural change. Government and regulator analysis consistently points to rising electricity demand as transport and heating electrify, with electricity potentially accounting for a significantly larger share of total energy use by 2050.
At the same time, peak demand - rather than total consumption - is emerging as the defining constraint. Electrification of domestic heat illustrates this clearly. While heat pumps can reduce overall energy use, they could significantly increase peak electricity demand, particularly during evening periods when networks are already under strain.
These changes are not occurring uniformly. Instead, demand is becoming more spatially concentrated and variable, driven by local uptake of technologies such as EVs, heat pumps, and domestic storage.
Along with the electrification of other parts of our economic infrastructure, and the rise of data centres, this creates new pressures on distribution networks, which were designed for predictable, centralised generation and relatively stable demand patterns.
In short, the grid is no longer managing a single demand curve - it is managing millions of dynamic, household-level behaviours.
From linear forecasting to scenario-based planning
Traditional grid planning relied on relatively stable forecasts of demand growth. That model is no longer sufficient. Today’s system operator, the National Energy System Operator (NESO), is relying on scenario-based approaches to reflect uncertainty and variability in how different combinations of electrification, flexibility, and energy efficiency might affect networks at a local level.
Crucially, these scenarios depend on understanding how technologies will be adopted across specific geographies - down to local authority or even neighbourhood level. Datasets increasingly include projections of technology uptake, energy consumption, and peak demand at granular spatial scales, recognising that national averages conceal critical local constraints.
The limits of averages
Despite this progress, a fundamental limitation persists - much of the underlying data used to model future demand remains based on averages, archetypes, or simplified assumptions.
This is particularly problematic in the housing sector. Two homes with the same EPC rating or archetype can have markedly different insulation levels, heating systems, occupancy patterns and retrofit potential.
At scale, these differences matter. They determine:
- The feasibility and cost of electrifying heat
- The timing and intensity of peak electricity demand
- The potential for demand reduction through fabric improvements
- The viability of flexibility solutions such as demand shifting or storage
When these variables are averaged out, models risk misrepresenting both the scale and shape of future demand.
Why granular housing data matters
Granular housing stock data - covering fabric performance, heating systems, occupancy patterns and retrofit potential - enables a fundamentally different type of energy planning.
1. Understanding real demand, not assumed demand
Detailed data allows planners to move beyond standardised profiles and generate more accurate, location-specific demand forecasts. This is essential given the recognised uncertainty in estimating residential heating demand, which is influenced by building characteristics, occupant behaviour and technology performance.
2. Modelling peak load and network stress
High-resolution data enables half-hourly demand modelling and identification of peak stress points on the network. This is particularly important as electrified heat shifts demand into concentrated evening peaks.
3. Enabling smarter investment decisions
Grid reinforcement is expensive and time-consuming. Evidence shows that combining electrification with demand reduction and flexibility can significantly reduce network stress - cutting peak loading and deferring investment. But realising these benefits depends on knowing where demand reduction is achievable, and where it is not.
4. Supporting localised energy planning
Strategic spatial energy planning requires alignment between national infrastructure and local realities. The focus to date has been on local area energy plans but these lack granularity, for example from the inclusion of social housing plans and detail on the potential for changing demand in the local housing stock.
5. Avoiding inefficient decarbonisation pathways
Electrifying inefficient homes without first reducing demand risks locking in higher system costs and greater network strain. It may also lead to homes being turned down for connections, if early adopters are not sufficiently efficient, or if there is a poor understanding of what demand the home will place on the grid.
The barrier
The rise of domestic battery storage, smart controls and demand-side response is an opportunity, but the switch to clean heat brings complexity with it. Grid operators delay, restrict, or block installations at scale due to primary technical and regulatory decisions:
- Between 11% and 17% of the housing stock in Great Britain (3.1 to 4.7 million homes) are looped, sharing a supply cable from the main network grid and making it unsafe to install a heat pump
- Risk of overloading the capacity of the local sub-station and low-voltage feeder cables.
While individual installs can often continue under a ‘Connect and Notify’ pathway, this is not the case with larger schemes. To manage demand, landlords must apply for approval where demand will be pushed beyond 60 Amps or if a larger three phase supply is required, increasing application costs and time, sometimes substantially.
These delays mean that projects are failing to deliver. Landlords must deliver grant-funding to a tight deadline, and a multi-month delay undermines the viability of plans to decarbonise heating.
Implications for policy and industry
The direction of travel is clear: strategic energy planning is becoming more spatial, more dynamic, and more data driven. But the data is not granular enough.
Our social clients know now what is required of many of their homes – in terms of demand reduction and electrification of heat. This becomes even clearer with the publication of heat network zones. Just as network operators want clear investment guidance, so do landlords – and that requires a much clearer process for agreeing plans for decarbonising the housing stock.
For policymakers and system operators, this raises several priorities:
Data sharing process for landlords to gain clarity on where electrification can go ahead, where a wait is required, and where there is no clear plan for clearing constraints
Looped services data is inconsistent and/or missing. Mixed methods are required to improve data held by DNOs and social housing, but better data breaks a barrier
Standardisation of data formats for social housing to request DNO agreement across boundaries – many landlords operate across boundaries
Alignment with local area energy planning to ensure national strategies reflect local conditions, the detail of EPB data and, where available, detailed plans from landlords
Conclusion: from uncertainty to precision
The UK’s net zero transition will not be constrained by ambition, but by the system’s ability to anticipate and manage change.
Electrification of the economy, the growth of distributed technologies, and the push for energy efficiency all point to a future where demand is more volatile, more localised, and more complex. Strategic energy planning must evolve accordingly.
High resolution housing stock data is not a ‘nice to have’ in this context - it is foundational. It enables the shift from broad assumptions to evidence-based scenario planning, from reactive investment to proactive system design.
And it is ready now. Recent NESO-led workshops have shown the pressure on the network operators to respond to connection requests – from transport, water, energy generation and data centres. All are concerned about the transparency of ‘the queue’ to connect. Housing needs its voice heard as much – and perhaps more – than any of those others.
Housing is infrastructure – and energy efficiency is fundamental to our economy in keeping our workers and consumers healthy and their bills affordable. Landlords are now facing a call to deliver on new Minimum Energy Efficiency Standards, and the inability to connect would undermine the potential for these new requirements to deliver on their promise.
In a system where millions of individual homes collectively determine national outcomes, understanding those homes in detail is no longer optional. It is the key to building a grid that is not only capable of meeting future demand, but optimised to do so efficiently, affordably, and at scale.
