The UK’s energy system is evolving to deliver a future of cleaner, cheaper and more secure power. James Houlton, Managing Director for Energy & Utilities at our associate member Mesh-AI, explores the thinking and approaches needed if we are to hit the key milestones for transformation.
There is limited time to reach Clean Power 2030 (CP30). Just over 58 months, to be precise.
To achieve a 95% clean power grid, the entire energy ecosystem must transform conventional approaches to energy demand, supply, and management. Companies across the energy system are striving to meet governmental targets for net-zero emissions – but also face long procurement processes and even longer connection queues.
So while the clock ticks down (here’s our own countdown clock) a faster, smarter and better coordinated approach is critical to manage the scale, uncertainty, and complexity that characterises today’s energy landscape.
Digitalisation needs to be seen as mission critical and integral to how we reach CP30, and it needs to be clearly communicated. To that end, there are several things we need to do across the energy system:
- We need to make decisions and act instead of redefining the problem;
- We need to rapidly change our mindset and culture for change;
- We need to focus on fixing our slowest parts.
All these are a lot easier said than done, so how are we proposing to use data and AI to meet these objectives?
The macro challenge: A new paradigm for energy demand and supply
Let’s first establish our starting point. The traditional energy model, where supply dynamically meets predictable demand, is shifting to an environment where demand must adapt to fluctuating renewable energy sources, such as wind and solar.
This shift requires new strategies and new ideas. The grid faces the challenge of quadrupling network capacity in less than a decade, while integrating an ever-growing mix of distributed energy resources like electric vehicles, solar installations, energy storage and electric heating sources.
Shifting to a start-up mindset
The success of the clean energy transition will be as much about how it is delivered as about what is delivered. Almost all organisations know what they need to build to reach CP30, but doing so in the time left is a much more difficult obstacle.
Leaders across the energy sector must prioritise agility and adaptability over rigid, incremental progress and process. A start-up mindset and speed, combined with enterprise-scale resources, is essential to make rapid advancements without sacrificing reliability or resilience. This isn’t a ‘move fast and break things’ approach, more being agile and responsive to solving problems.
How to think differently
While advanced data and AI technologies can help us manage this complex landscape and solve challenges, the journey from experimentation to practical AI integration has often been slow for some.
We have seen some organisations get stuck in the Proof of Concept (POC) phase without scaling up the solutions into production – leaving some in a ‘POC graveyard’”’. For AI to make a meaningful impact, companies must thoroughly validate and embed these technologies into their operations, focusing on tangible outcomes rather than standalone projects.
Organisations need to consider what the priority challenge is and the minimal viable product to solve that challenge. How does this solution fit into your vision of the future? Is your landscape and environment fit to support this solution, with the right structures, processes and skills? Finally, how can this be scaled to solve other challenges?
The importance of data foundations
This focus on AI as the solution to many problems has underlined the importance of quality data that is accessible across the ecosystem. Data connectivity and visibility across all levels of the energy system – from generation to customer use – are critical for whole-system intelligence.
With quality data at hand, predictive modelling, demand forecasting, and scenario planning are made possible – all of which are essential for balancing renewable sources with demand shifts. This ‘connected visibility’ across both internal and external data sources allows for collaboration across sectors and efficient, responsive decision-making.
By connecting players from different parts of the energy system to share data, we can solve whole-system problems, rather than just our own. With a collective, better coordinated approach, we can spread the risk and cost by solving problems together.
Prioritising pace over perfection
To succeed under this ticking clock, it’s crucial to think about what we can change now and take immediate action. Leaders need to focus on foundational principles and addressing the slowest part of their processes, not optimising what is already fast.
Investing in organisational transformation, particularly in upskilling teams for data and AI proficiency, ensures that innovations can be owned and managed internally, creating sustained momentum.
The mission to achieve clean power by 2030 is a challenging but achievable goal, provided the sector can adapt quickly enough. Energy leaders must shift from traditional, cautious approaches to bold, dynamic strategies. By embracing speed, data-driven innovation, and whole-system collaboration and coordination, we can make clean power a reality within the decade.
Mesh-AI invites all members of Energy UK to join it on 20 March to explore how cutting-edge advancements are shaping an AI Powered Future. Sign up for the Data & AI Symposium.
