Artificial intelligence is fast becoming a defining driver of electricity demand in Europe. As AI deployment accelerates, the key constraint is shifting from computing power to the capacity of electricity grids to absorb large, continuous and localised loads. This blog examines how updating and modernising grid planning, connection rules and energy regulation are emerging as important enablers of AI’s future in the EU.
By Ruben Maximiano and Wouter Meester, OECD Economics Department.
AI’s energy reality
AI is often discussed as though it operates independently of physical systems. In practice, AI depends on vast amounts of electricity. Its future will be determined not only by advances in algorithms and computing power, but also by kilowatt-hours – by the ability of electricity systems to deliver power reliably and at scale.
Training and running frontier models requires continuous and increasingly large volumes of power. According to the IEA, a typical AI-focused data centre already consumes as much electricity as 100 000 households, whilst the largest new facilities could require 20 times more, placing them on par with the consumption of small countries (IEA, 2025).
As a result, an important binding constraint on AI deployment is no longer generation alone. It is increasingly the capacity of electricity systems to absorb, transport and manage large, continuous and geographically concentrated loads without conflicting with other usages. As the recent OECD Diagnostic Tool for Reducing Regulatory Barriers to Solar, Wind and Pumped Hydro Storage in the EU report shows, tackling these also involve better regulations.
The importance of energy to AI roll-out is visible in corporate energy sourcing strategies. Big Tech companies now account for the majority of Corporate Power Purchase Agreements (PPAs) in Europe (see figure 1). Yet the scale and speed of AI deployment are already outpacing what traditional PPAs can guarantee. Hyperscalers are turning to direct investment in generation, including solar, wind and nuclear, to secure long-term supply.
Taken together, these developments point to the conclusion that the next frontier of AI policy is not only about how much electricity is produced, but also about how grids are planned, reinforced and that to a significant extent depends on how grid investment and grid connection rules are regulated.
To address such barriers systematically in the EU, the OECD report Diagnostic Tool for Reducing Regulatory Barriers to Solar, Wind and Pumped Hydro Storage in the EU, identifies the regulatory bottlenecks that slow deployment of renewables in the EU and constrain grid availability, with clear parallels for policymakers seeking to adapt energy rules to enable AI deployment. As this blog is based on this work it refers mainly to EU practices and energy mix.
Global AI and local grids
While global electricity demand from AI remains moderate (expected to reach 3% globally by 2030 and 4.5% in the EU)(IEA 2025, Ember 2025), its impact is highly concentrated. Data centres cluster in locations offering robust fibre connectivity, favourable cooling conditions, low electricity prices, and fast, reliable grid access. This concentration amplifies pressure on local grids and exposes the limits of existing planning and connection frameworks.
Ireland illustrates these risks. In 2023, data centres accounted for around 21% of electricity consumption in 2023 up from 5% in 2015. Much of this has been concentrated around Dublin, where data centres consume roughly half of electricity produced. The resulting strain on the network raised security-of-supply concerns and led to the Transmission System Operator stop accepting applications for new data centres in Dublin until 2028 (Ember, 2025, CRU, 2025). In response, the national regulator is introducing a number of regulatory changes, including requirements for new data centres to install dispatchable generation or storage facilities on site.
The countries with more abundant and affordable electricity and stronger grids have a comparative advantage for the location of data centres. For instance, the Nordic countries have become attractive AI destinations due to abundant energy, strong grids and low-carbon baseload (Ember 2025). More broadly, IEA analysis suggest that jurisdictions offering significantly faster grid-connection timelines could capture up to 20% more data-centre growth by 2030 (IEA, 2025).
How AI stresses electricity systems
These pressures materialise across three interconnected timescales. In the long term, large AI campuses require transmission and distribution networks with sufficient hosting capacity, yet grid expansion and permitting often take 5 to 10 years. This makes anticipatory planning and co-ordination between data-centre siting, grid investment and local generation essential. Just as important is grid optimisation: improving system efficiency through digitalisation and AI-based system management.
In the medium term, inefficient connection rules have become a binding constraint. Long queues, speculative applications and first-come, first-served rules delay viable projects and distort planning. In real time, AI workloads introduce rapid power swings – far faster than traditional industrial loads -challenging frequency stability and voltage control.
Addressing these pressures requires regulatory frameworks that enable not only physical grid reinforcement, but also optimisation through digitalisation, flexibility procurement and stability services, and that allow system operators to invest in software and operational solutions alongside traditional capital assets.
The Diagnostic Tool shows that key elements of the regulatory system that contribute to address these pressures, would include:
- Anticipatory grid investment supported by clear cost-recovery rules.
- Criteria-based connection queues to prioritise ready and system-beneficial projects.
- Hosting-capacity maps to guide efficient siting.
- Flexible access arrangements, including non-firm and hybrid connections.
- Tariff and market design that value flexibility and stability services.
How countries are responding
Countries are increasingly adapting electricity regulation to manage the highly localised grid impacts of AI-driven demand. Governments are experimenting across different parts of the power system. In Europe, Italy is improving locational planning through detailed hosting-capacity maps; Portugal is reallocating unused capacity and simplifying storage licensing; the UK is reforming connection queues by prioritising projects that are “first ready, first connected”; the Netherlands is deploying congestion-management zones and prioritisation criteria; and Finland is integrating data centres into heat-recovery and clean-power strategies.
Despite this diversity, common policy lessons seem to emerge. Grid access can no longer be treated as a simple administrative queue and requires prioritisation based on readiness. Locational transparency is critical to guide efficient investment. Flexibility and digital optimisation must complement traditional grid reinforcement. Finally, grid planning and permitting need to become anticipatory rather than reactive. Countries applying these principles are better positioned to accommodate AI-scale demand while preserving reliability and affordability.
Powering the age of intelligence
AI is reshaping electricity demand at a scale that is now central to economic strategy. Ensuring reliable, affordable and low-carbon supply is becoming a prerequisite for attracting and sustaining digital investment. In the age of AI, competitiveness, autonomy and resilience will increasingly be determined not only by data and algorithms, but by the rules that govern the compute infrastructure and their electricity systems.
The OECD–EU Diagnostic Tool offers governments a practical roadmap to modernise regulatory frameworks and align them with the needs of an electricity-intensive digital economy.
*We will be launching the Diagnostic Tool today, 29th January. You may register here.
References
CRU, “Large Energy Users connection policy”, December 2025, https://cruie-live-96ca64acab2247eca8a850a7e54b-5b34f62.divio-media.com/documents/CRU2025236_Large_Energy_User_connection_policy_decision_paper.pdf
Ember, 2025, Grids for data centres: ambitious grid planning can win Europe’s AI race, https://ember-energy.org/app/uploads/2025/06/Grids-for-data-centres-in-Europe.pdf
IEA, 2025, Energy and AI, World Energy Outlook Special Report
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