Digitisation, Artificial Super Intelligence and Energy
By Mark Workman
The success of DeepMind’s AlphaGo at a Go tournament in 2016 was a feat not thought possible for at least another decade. It highlights the current renaissance in Artificial intelligence (AI), where new digital architectures and algorithms are leading to an envisioning of the potential for Artificial Super Intelligence (ASI) across the economy, including the energy sector – currently manifesting itself in the development of autonomous vehicles.
Information technology is already causing enormous disruptive change in the energy system. For example, the potential to digitise parts of the energy value-chain, combined with the falling costs of decentralised energy systems, is bringing into question the centralised utility business model. ICT organisations are already facilitating smart systems and demand-side response, but the extent of the applications and benefits is little understood by the energy sector, and are only just being mapped out.
The fusion of energy and ICT is marked by three trends:
- Blurring of the boundaries between energy, life-style and economic activity, potentially leading to a more participatory evolution to the future energy system. New businesses are seeking to enter the market with non-traditional and potentially disruptive business models, such as Peer-to-Peer energy and community energy. The benefits, risks and challenges of implementing such alternative low-carbon pathways are poorly evidenced, but the low-carbon transformation may lie somewhere between central coordination and these new market-led pathways .
- Digitisation of the energy system is already happening. Energy companies could enhance this to radically overhaul their operations, potentially (at least initially) consolidating their market position. ERP’s Utility 2050 project found that the basic technologies for new business models are already in place and simply require upgrading to improve their capacity.
- New architectures for digital systems and approaches to programming are changing the actors involved and the relationships between them. This could radically and disruptively change the way that the energy system is configured and managed.
Investment in applied ASI and Machine Learning is shifting from academia to private sector corporations. In 2015, the taxi-hailing firm Uber set up a unit to work on self-driving cars, recruiting 40 of the 140 staff of the National Robotics Engineering Centre, at Carnegie Mellon University.
Advances in digitisation may realise spill-over benefits that could enhance or disrupt the energy sector, including improved sensor technology, graphics processing, robotics, broadband wireless communications, advanced materials, 3D visualization, LIDAR and 3D printing.
Optimism in the potential applications of ASI, though, is countered by the difficulty in getting technology forecasting right and the possibility of regulatory and legal constraints as well as societal mistrust.
The social, technological and regulatory ramifications of these disruptive trends on the energy sector are substantive and evolving rapidly. ERP’s Utility 2050 work is looking at some of these issues but further work could include:
- Addressing the evidence gap on society-led transformation pathways, to inform the regulatory and policy decisions that will assist and accommodate it. For example, enabling a bottom-up, community energy driven transformation would require fundamental changes to the current energy market rules and regulations.
Gaining a system-wide perspective of how ASI might affect the energy sector is very difficult, as some developments may be imported from other sectors, and what work there is in the energy sector is fragmented and fraught with commercial sensitivities.
- Understand the advances being made in Digitisation and ASI development in order to assess how they might impact the various value chains in the energy system.
- Case studies of advancing areas, such as autonomous vehicles, could identify how specific advances in Digitisation and ASI could deliver benefits and opportunities across domains, and the barriers to achieving them.
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