Latest news: AI in the power industry

SP Energy Networks, a Scottish electricity transmission operator, will develop a ‘digital twin’ of the UK’s electricity network, to model and test digital solutions to manage increased electricity demand on the real-life network. 

The digital twin will be the first energy project to be financed by the Engineering, Physics and Sciences Research Council’s Prosperity Partnership Fund. 

It is being created in partnership with Scottish universities including Strathclyde, Glasgow, Heriot Watt and St Andrews. 

Known as ENSIGN (ENergy System dIGital twiN), the digital twin system will use artificial intelligence (AI) to detect ways to optimise capacity and develop green energy solutions on the network on the path to net zero emissions. 

The project seeks the best solutions to handle increased electricity demand on an energy network platform, hosted by Strathclyde University, and will address issues such as the decarbonisation of heating and industry and the use of hydrogen. The work will be modelled by other universities involved in the initiative. 

SP Energy Networks network planning and regulation director Scott Mathieson stated: “The pace of change in the energy industry is like nothing we’ve ever seen. So it’s vital we can stress-test tech and services to identify the best solutions and ensure the network remains fit for purpose as we move towards an all-electric future. 

“This new ‘digital twin’ will allow us to simulate innovations and understand the potential benefits of new services in a whole new light, giving us meaningful insights that will directly impact what we do in the real world.”

Since ChatGPT’s release in November 2022, generative AI has entered public discourse across the world. According to GlobalData, over a million social media posts about artificial intelligence (AI) have been made across Twitter and Reddit in the last year.  

The Future of Life Institute’s open letter to pause AI development led to multiple major news outlets publishing features on the existential threat that AI could pose to humanity. ChatGPT’s instant internet virality is possibly its biggest benefit to the tech industry so far.  

Generative AI has been rolled out to provide customer relationship management solutions, software development and even storytelling. But some sceptics cannot ignore that the timing of generative AI’s hype is fortuitous for the tech industry.  

Managing director at TS Lombard, Dario Perkins, explains in a recent webinar that Big Tech was “the part of the stock market that suffered the largest declines as central banks started aggressively raising interest rates”. 

Perkins also attributes the “drying up” of tech investment to the closure of several banks with close ties to the tech sector, such as Silicon Valley Bank, and describes ChatGPT’s public release as “very clever marketing”.  

With the metaverse winter closing in and quantum computing funding slowing down due to an emerging lack of practical uses, generative AI instead began experiencing a funding frenzy whilst funding in AI overall dropped. On 13 June, French company Mistral AI broke the European record for a seed round funding, receiving a total of $260m in four weeks.  

AI has also boosted hiring for tech companies who Perkins notes were seeing “fairly big job losses in contrast to other sectors- in contrast to other sectors who were still looking to add jobs”. GlobalData research consolidates that the tech sector’s hiring in generative AI increased approximately 600% from March 2023 to June 2023.  

Alongside the rush of news and social media coverage, and the increase in hiring, a recent working paper by MIT proposes that the use of ChatGPT was able raise efficiency by almost 40% and greatly improve the quality of work by lower achieving students.  

GlobalData’s tech sentiment polls indicate that AI was perceived as the most disruptive technology in the last quarter of 2022. Despite this numerous companies such as Apple, JP Morgan, Deutsche Bank and Verizon have all banned the use of the generative AI in 2023.  

Nuclear science, towering 200m power plants, and millions of miles of electrical cable are some of the things making the power industry one of the most process driven on Earth. Power companies at every stage of the value chain must make decisions regarding how to best use and maintain their assets to balance supply with demand. They are therefore natural candidates for the integration of AI, which has the potential to enhance generation, transmission, distribution, and the experience for end users. 

The power industry is often considered as being stuck in its ways. Not out of stubbornness, but due to the difficulty in digitally transforming age-old infrastructure and training an ageing workforce. The question is therefore how can power companies integrate AI into their business models? 

Equipment and engineering 

AI implementation in the power industry starts with equipment. Most power plants and electricity grids typically have lifespans extending decades. Therefore, while new AI-powered equipment can act as a key enabler, most power companies simply use add-ons such as sensors, probes, meters, and thermal imaging equipment to collect real-time data, and third-party AI software to process this data. This practice is commonly known in the industry as predictive maintenance. 

AI-based predictive maintenance algorithms and software can detect faults and repair them before an asset breaks down. Industrial automation companies such as Emerson, ABB, and GE are leaders in providing predictive maintenance solutions such as vibration monitoring, infrared thermography, and lubricant oil analysis. 

Harnessing the lifeforce of stars 

Some of the most exciting AI developments in power come in the field of nuclear energy, which attracts attention due to its high energy density and low emissions profile. Applications of AI in nuclear fission revolve around predictive maintenance of equipment and the safer disposal of radioactive waste. However, AI has the most potential to enact change in nuclear fusion. The emerging energy source is largely still in the research & development phase, with few industry experts expecting the availability of commercial fusion reactors before 2030. Research groups often share fusion reactors, limiting the time available for experiments. AI can speed up the R&D timeline through modelling and simulations. In February 2022, DeepMind collaborated with the Swiss Plasma Center (SPC) to develop a deep reinforcement learning system for tokamak nuclear fusion simulations.