Balancing energy transition, security, and digitalisation in a changing utility landscape
Professor Phil Hart, Chief Researcher at the Renewable and Sustainable Energy Research Centre at the Technology Innovation Institute, shares his views on how utilities can navigate energy transition, security, and digitalisation through long-term planning, pragmatic energy choices and advanced technologies.
What are the ways in which global utilities can address the challenges and opportunities surrounding energy transition, energy security, and digitalisation?
Key to this is to use a rigorously fact-based approach and not get caught up in narrative and agenda. In some spaces, fossil-based generation is the sensible choice, but the number of spaces where this represents the right choice is diminishing rapidly. Renewables with storage and nuclear can often represent the best economic option, and when considered from the combined perspectives of cost, reliability, and security, these options make very good strategic sense.
Utilities are used to thinking in multi-decadal timescales and, by their nature, provide a service that is both vital to society and of societal benefit. Taking a whole systems long-term view is therefore vital, and while utilities often do that very well, even under the transition pressure we see today, they must continue to include not only the short-term demands of tomorrow but also the needs and effects of what they do and how they provide these services in the long term.
Using all the tools and techniques available is therefore the key. Adopting digitalisation and advanced control strategies can help avoid significant costs by increasing the capability and utility of key assets. Think of dynamic line rating, for example, which could, in some circumstances, avoid or delay the need for expensive transmission line upgrades. In an expensive energy transition, with increasing energy demands, that type of approach will be vital. And most importantly, we should stay the course. Climate change is real, and the energy system is the key contributor. Closing our eyes, hoping things will be OK and ploughing on with the same old technology is not a sensible strategy and would represent a failure of leadership for the sector.
How can utility providers balance the immediate demand from AI data centres without compromising their low-carbon commitments?
That’s a tough question and a key risk. Utility energy companies need a clear view of how AI and data centres will impact their systems. AI will lead to concentrated areas or clusters of very high demand, mostly close to urban centres, so that the AI is close to the user. Utilities will need to proactively prepare these areas for the upcoming wave of demand. In principle, non-polluting energy generation is of course preferred, and, usefully, these are often the quickest to permit and procure. But large solar fields in or near urban areas are a challenge, so an effective grid upgrade is critical.
Utilities might do well to establish strategic areas or zones for data centres, and proactively develop both generation and grid to supply these areas while also working with the telecoms companies to ensure sufficient low-latency, high-bandwidth connectivity. Data centre builders look at timescales of 1-3 years to be up and running, while utilities are more used to decadal thinking, so there is a disconnect that means utilities need to think far ahead. Establishing pre-enabled zones could encourage data centre owners to locate in them preferentially, since if power and comms already exist in abundance at that location, that’s a headache and key risk removed. This approach could also help fulfil the broader political strategy of regions becoming AI hubs.
Water security is one of the critical pillars for utilities, and you have highlighted the benefits of “waste-to-wealth” approaches. How close are we to seeing these circular-economy models implemented at utility scale to support lower-carbon water production?
I think we’re pretty close, or could be if we wanted to be. Technology is either here or about to enter the market that can negate a large percentage of the societal waste problem and create value from it, and this is not just with wastewater but with almost all types of municipal and industrial waste products. In our local area, where we produce a lot of desalinated water, the waste stream of concentrated brine is loaded up with key minerals and metals that we can extract economically and generate significant returns from, and my team will be demonstrating that at scale by year's end.
Converting other wastes into energy or useful products is something we know how to do in both the R&D community and the commercial space — we just need to more widely adopt it and continue to make it more and more efficient. Waste-to-wealth and circular economy models are much more around the economic stage of adoption, related in most cases to scale or lack thereof, and the will to make it happen, than any specific lack of ability to do it.
How is TII utilising generative design and AI-based predictive modelling to ensure both grid stability and flexibility?
Our approaches span the whole space. We have developed modelling systems that allow incredibly fast investigation of where and when utility companies should modify their grids to drive their economics, stability, resilience, and carbon footprint. What used to take months to investigate can now be done in minutes with our systems. Similarly, we have systems than can look at failure modes, and their effects, showing weakness in current grids and predicting how failures could propagate through a network and importantly we can now look at not just the effect of a single node failure (N-1) but can investigate multiple nodes going down (N-x) and understand the cascade effects which is vital information to know when considering large networks, as well demonstrated in the Spanish and Portuguese blackouts recently.
We’ve also got models that look long-term and consider the best methods to achieve a certain goal. Consider the 2050 goals of carbon neutrality — these are fine aims, but the pathways to get there are numerous, and each has costs, investment horizons, and technical challenges, so we have systems that allow you to plan these multidecadal changes to the grid and generation system, signalling when and where utilities should invest for minimum costs and best effect, whilst meeting those overarching goals.
The other ways we could employ AI are numerous, and the ultimate goal would be a power system controlled at the per-second level to maximise reliability, emissions, cost, and stability. Such systems will come, and are well within the capabilities of the TII team, and will be vital as the percentage of renewables increases.
