TO BE PUBLISHED February 2027
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The conference will consider next steps for the adoption of artificial intelligence across the UK energy sector, with a particular focus on the GB electricity system. Areas for discussion include the transition towards AI-enabled electricity system planning and operation, priorities for wider deployment, governance and regulation, questions around investment and operational accountability, and implications for energy security, system costs and consumers.
AI for Clean Energy Strategy, risk-based operation & grid autonomy
It will bring together stakeholders and policymakers to examine priorities for the Government’s forthcoming AI for Clean Energy Strategy, also drawing on DESNZ’s Vision for an AI-enabled clean energy system and accompanying call for evidence, alongside the independent review The Grid We Need Now led by the Government’s AI Champion for Clean Energy, Lucy Yu. Delegates will consider the review’s proposals for moving towards more risk-based operation and planning of the GB electricity system, and options for governing greater AI-enabled autonomy, as well as approaches to translating gains from flexibility and system efficiency into benefits for consumers.
Operational applications, assurance & moving from trials to deployment
The planned agenda will examine practical applications of AI in forecasting, system balancing, congestion management, network and asset planning, predictive maintenance and demand-side flexibility. Ofgem’s work on AI assurance and its AI Reg Lab will be assessed, as well as early operation of the AI Technical Sandbox pilot and implementation of the Energy Digitalisation Framework. Attendees will consider the testing, explainability, auditability and human accountability needed to demonstrate that AI applications are suitable for operational use, and the data quality, interoperability and secure access to information required to support deployment, alongside DESNZ’s proposals for an energy smart data legislative framework.
Discussion is also expected on approaches to procurement and investment, regulatory incentives and cost recovery, and the conditions needed to move successful applications from trials into business-as-usual deployment across critical energy infrastructure.
Distributed assets, workforce readiness & sector coordination
Further planned sessions will consider the role of AI in supporting efficient integration of renewable generation, storage, electric vehicles and other distributed assets, alongside requirements for workforce capability and organisational readiness. Delegates will also discuss the implications of rising demand from data centres - including priority grid connections and on-site generation for strategic projects under the recently announced British Data Centre Covenant - for network capacity, system costs and the role of AI-enabled flexibility.
The agenda will also examine the roles of government, Ofgem, NESO, network operators and technology providers in coordinating longer-term adoption while maintaining resilience, cyber security and public trust. Approaches to measuring the impact of AI adoption on system costs, productivity and consumer outcomes will also be considered.
Overview of areas for discussion
- risk-based operation & AI autonomy:
- probabilistic operation and planning - risk metrics, thresholds and security standards - forecasting capability and phased regional rollout
- defining levels of grid autonomy - approval authorities and delegation limits - escalation and human accountability
- data architecture & interoperability:
- common data models and standards - granularity, latency and quality - secure access to network and operational datasets
- energy smart data - accreditation, consent and third-party access - interaction with wider energy data initiatives and network data requirements
- assurance, liability & supplier risk:
- Ofgem AI Technical Sandbox and Reg Lab - high-impact use cases - assurance thresholds, monitoring and audit trails
- third-party models and cloud services - due diligence, contractual liability and model updates - managing supplier dependencies and cyber risk
- investment, procurement & scaling:
- proposals for a national AI for Grid testing and deployment capability - coordinated access to data and testing facilities - routes from controlled trials to operational use
- innovation funding versus business-as-usual investment - regulatory incentives and cost recovery - business cases for network-wide deployment
- procurement of AI solutions - data and IP terms, portability and supplier lock-in - measuring realised savings and operational value
- operational integration & workforce:
- control-room use of AI - integrating forecasts and recommendations into operational processes - override, fallback and incident response
- AI, data and power-systems skills - training staff to challenge outputs - change management and responsibilities across functions
- flexibility, consumers & evidence:
- automated EV, battery and heating flexibility - tariff interoperability and asset coordination - consent, opt-outs and consumer protection
- AI optimisers at scale - herding, gaming and algorithmic collusion - system-level testing of interacting AI agents
- tracing efficiency gains through to bills and service quality - baselines, distributional impacts and metrics for consumer benefit
- data centre demand - priority connections and on-site generation under the British Data Centre Covenant - network capacity, flexibility and cost allocation
