The energy transition has hit against a brick wall – our electricity grids can’t keep up. In the Netherlands, the UK and many other countries, new factories, homes and solar parks are being delayed because grids are congested and expansion can take years.
According to Netbeheer Nederland, in the Netherlands, over 11,900 businesses, public institutions, and thousands of homes are stuck waiting for grid connections, slowing housing and investment growth. The International Energy Agency recently warned that grid bottlenecks could slow down the entire transition, resulting in problems such as the unavailability of electricity and an unreliable system resulting in possible disruption of society and the economy.
This is not just a technical challenge – it’s an organisational and planning problem. And this is where artificial intelligence (AI) comes in. Not just as the latest buzzword, but as a practical tool to help us plan better, operate smarter and use the grid we have more efficiently. Here are four reasons why AI is becoming essential:
1. AI can help speed up grid expansion
The biggest obstacle in the energy transition is that there is not enough capacity. Grid operators are under pressure to reinforce infrastructure and do that quickly, but projects are often delayed. There are lots of reasons for this – budget constraints, planning issues, the availability of the right materials and staff. And another reason is that forecasting is underestimating how quickly demand grows. In the Netherlands, new housing projects and solar parks have been delayed because of faulty predictability in, for instance, building permits. In the UK, hundreds of projects are stuck in the queue because of these inaccurate predictions.
AI can build more realistic demand forecasts by combining diverse signals: historical consumption, building permits, planned industrial sites and regional electrification trends. The real value is not just a slightly more accurate number, but fewer surprises that throw entire investment programmes off track. That means operators can plan expansions with more confidence, regulators can approve them with more certainty, and customers can connect sooner.
2. From costly ‘firefighting’ to predictive ope-rations
Keeping the grid stable with variable wind and solar is becoming more expensive. German redispatch costs exceeded €4 billion in 2023. Operators often step in late, paying to curtail generation or reroute power at the last minute.
AI forecasting tools can predict congestion days in advance, which allows operators to schedule preventive measures rather than costly emergency ones. Anomaly detection models can identify when a transformer or line is behaving unusually, flagging issues before they cause failures. And ‘digital twins’ – virtual models of the grid – let operators test corrective actions safely, before applying them in the real world. This changes the operating model from reactive to predictive, which can save money, reduce outages and improve trust in the system.
3. Making flexibility work at scale
EV chargers, heat pumps, and home batteries are connecting at scale. Left unmanaged, they can overload local networks. In congested areas of the Netherlands, DSOs (distribution systems operators) have already had to ration new connections for businesses.
AI can coordinate when devices use electricity, aligning them with grid needs and price signals. For example, EV chargers can shift charging to off-peak hours and heat pumps can pre-heat buildings when renewable generation is high. Field trials show that such coordination can cut peak loads by double digits. This isn’t about controlling people’s homes but about giving incentives and automation that turn flexibility into a system resource. Done well, it delays costly reinforcements and keeps the transition moving.
4. Turning data into real decisions
Operators across Europe are drowning in data from smart meters, weather forecasts, sensor readings, market data. But too often, this data ends up in dashboards rather than decisions.
AI can help to cut through the noise. It can help operators decide which substation to reinforce first, how much reserve power to buy, or where flexibility contracts will have the most impact. The difference is moving from raw data to actionable recommendations embedded directly in planning tools and control rooms. The companies that succeed won’t be those with the most data, but those who embed AI into daily decision-making.
Putting the right guardrails in place
The use of AI in energy infrastructure has to be handled carefully and it has to happen in a responsible, ethical way. The ‘how’ it all works matters as much as the ‘why.’ In order to put guardrails around AI and ensure the approach is responsible, there are three essential principles:
- Explainable models
Operators need to know why an AI forecast or recommendation was made. In practice, this means using models that can highlight the main drivers (e.g. weather, new connection requests), not just black-box outputs.
- Fallbacks and human oversight
Every AI system in the control room must have a safe fallback e.g. reverting to traditional load-flow calculations, and decisions must remain in the hands of trained engineers.
- Data protection by design
Household-level consumption data should be anonymised or aggregated before use. Where possible, operators can use federated approaches – keeping data with the source while still training useful models.
By building these guardrails in from the start, AI can be trusted as a support system, not a risk.
Call to action
The energy transition is being slowed by very practical barriers: overloaded grids, project backlogs and operational stress. AI won’t remove political debates or permitting hurdles, but it can give operators the foresight and tools to help them work faster and smarter.
For governments, regulators, and grid companies, the priority is no longer whether to use AI, but where to embed it first: planning grid expansions, managing congestion, unlocking flexibility and turning data into action. AI is not a distant vision – it’s a practical lever for unblocking today’s transition. Without it, Europe’s clean-energy goals risk slipping further out of reach.
How Valcon can help
At Valcon, we don’t just deliver algorithms – we enable transformation. Here’s how:
- Holistic capability
We combine AI technology with deep data integration, embedding our experts directly into your planning and operations teams. That ensures models don’t live in a lab – they live in your processes. - Grounded in operations
Our teams include implementation specialists, data scientists and transformation managers. We work with your controllers and schedulers to ensure AI recommendations flow into real tools – crew schedules, connection prioritisation, reserve planning – not just PowerPoints. - Business process integration
We help rewire workflows so that AI outputs become decision points: from forecasting to planning to flex deployment. We tie adoption to KPIs – like reduced delays or lower redispatch cost – and monitor performance to refine models in real time. - Governance and change management
We help set up transparent model validation, human sign-off workflows, data protection protocols, and training tracks – so AI adoption is safe, trusted, and sustained.
With Valcon, AI becomes your operating system: integrated, explainable, scalable – so you don’t just pilot, you deliver.
If you would like to speak to someone at Valcon regarding the energy transition and AI, please contact: [email protected] and [email protected]
