The UK's ability to capture growth from the AI boom and the electrification supercycle is limited by access to electrical power. Beyond the physical shortage of capacity, a major constraint is weak visibility of where usable capacity exists at node and asset level. This causes projects to cluster at constrained points while spare capacity elsewhere is missed.
This paper estimates the annual economic cost of poor grid capacity visibility across Great Britain for connections from 11 kV to transmission, covering data centres, EV hubs, utility solar, grid-scale storage, electrified heat, commercial and industrial real estate, and public estates. We quantify four cost categories:
- Delayed and deterred investment, where private sector projects start later or not at all because usable capacity is unclear;
- Network reinforcement inefficiency, where avoidable or oversized works arise from sub-optimal siting;
- System balancing and constraint costs, where curtailment and losses rise when assets connect at constrained nodes;
- Additional developer costs, where application churn, studies, design rework and land negotiations add avoidable spend that is passed through to customers.
The central estimate of the total annual cost of poor capacity visibility is £2.71 billion, within a range of £1.25 billion to £4.69 billion. Of the four cost categories assessed, delay and deterred investment and network reinforcement inefficiency account for most of the total, at £1.20 billion and £1.08 billion respectively in the central case. System balancing and constraint costs add £133 million, and additional developer costs contribute £302 million. These costs are ultimately borne by consumers through higher bills and through lost or delayed growth.
The solution is to urgently enable access to datasets that can enable accurate grid capacity visibility. We recommend:
- A clear commitment from DESNZ and Ofgem to identify and close all data gaps needed for accurate grid assessments within 12 months.
- A priority lane for enabling access to critical gaps within the next 3 months.
- Accuracy assessments, including clear quality metrics (for example, completeness, timeliness, and error rates), published methodologies, and periodic independent audits so users can trust the data and identify issues quickly.
- Interoperability and accessibility: data needs to be interoperable between datasets and so should include common unique identifiers of any assets referenced in a dataset.
- Network data incentives: amend network data incentives to focus on incentivising networks to enable access to underlying data, rather than building digital products themselves.
