Energy systems are changing from rigid, electromechanical structures to programmable, data-driven networks. This transition has increased the complexity of operations and raised issues of stability and vulnerability to hackers, but also presents an opportunity to dramatically increase the efficiency of the energy infrastructure while reducing its carbon footprint.³ This will require the adoption of entirely new operational principles drawing inspiration from modern communication networks and the resilience they offer. The result should be a more flexible, integrated and democratised energy landscape.

Future Horizons:

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5-yearhorizon

The emergence of pico- and nano-grids

The rise of local, decentralised systems such as ultra-small-scale pico- and nano-grids at the building level begins to showcase their advantages.

10-yearhorizon

The scaling challenge focuses attention

Aggregating local decentralised networks into larger grids — from the university campus scale to the city scale, for instance — becomes a key challenge for the clean energy transition.

25-yearhorizon

The first fully integrated, bottom-up energy system

By linking smaller, decentralised grids, entire cities and even countries become fully transformed into bottom-up clean energy systems that have the potential to achieve net-zero goals.

Just how these new energy grids will evolve is an open question. One option is the development of decentralised, bottom-up systems, or "microgrids",⁴ which are more adaptable and can be aggregated over time. Proponents argue that they offer a more modular and resilient form of connectivity. The alternative is large, integrated and networked systems that benefit from economies of scale and encourage resource-sharing. In many parts of the world, these will continue to be favoured.

Both will require sophisticated models that can simulate complex behaviour, control multifaceted systems and predict future outputs. These “digital twins” are evolving rapidly, enabled by the analysis of large datasets and AI.

However, significant barriers still prevent wider implementation, including the inaccessibility of data for security reasons and a reluctance to test AI in critical infrastructure where failures are unacceptable. Building trust in AI and ensuring it is understandable and safe through “explainability” initiatives or insistence on “human-in-the-loop” protocols are major challenges.

Digital energy innovations - Anticipation Scores

The Anticipation Potential of a research field is determined by the capacity for impactful action in the present, considering possible future transformative breakthroughs in a field over a 25-year outlook. A field with a high Anticipation Potential, therefore, combines the potential range of future transformative possibilities engendered by a research area with a wide field of opportunities for action in the present. We asked researchers in the field to anticipate:

The uncertainty related to future science breakthroughs in the field
The transformative effect anticipated breakthroughs may have on research and society
The scope for action in the present in relation to anticipated breakthroughs.

This chart represents a summary of their responses to each of these elements, which when combined, provide the Anticipation Potential for the topic. See methodology for more information.