Artificial intelligence (AI) will play a key role in intertwining the power, transport, industry and building sectors while helping to decentralize the power industry as energy systems across the world move toward decarbonization, according to a new report by the World Economic Forum in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena).
The report says AI has the potential to accelerate a reliable and low-cost energy transition, including integrating renewable energy resources into the power grid, supporting proactive and autonomous electricity distribution systems as well as opening revenue streams for demand-side flexibility. The technology can also help accelerate new clean energy and storage uses.
AI can simplify the management and cost of energy sources for organizations while managing a large amount of data and increasing the efficiency of those sources. This new report highlights ways to implement the technology in a way to standardize a renewed energy production.
Currently AI is primarily used in pilot programs in the energy industry and use in predictive maintenance, but the report says there is much upside to AI to help accelerate a renewable energy transition worldwide and the goal should be to implement its widespread and standardized use.
The shift to decarbonization is not simple or inexpensive. BNEF’s New Energy Outlook 2020 predicted 56% of power usage could be from wind and solar sources by 2050. But getting there would require a $15.1 trillion investment in wind, solar and batteries by that time. While that will also shrink the size of the power plant by 80%, BNEF says, it will also increase the complexity of managing power systems.
Despite the costs, implementing widespread use of AI in the industry could have a positive financial impact long term.
BNEF says without intervention, power grids would not last as long and the use of transformers could be cut by as many as 10 years costing the industry as much as $188 billion over the next 30 years. The use of AI may help reduce costs by keeping equipment in optimal ranges. BNEF also says without use of AI resources, costs could rise by 6% to 13% by 2040.
The World Economic Forum report lists nine principles to make AI a widely used and reliable resource for the transition to renewable energy. The principles are broken down into three areas – designing, deploying and governing AI use.
Designing AI includes automation, sustainability and the design.
AI systems need to be increasingly automated and decentralized, and across other fields such as mobility, heat and industry, to aid in efficiency. The systems need to be energy efficient and operated in ways that limit the carbon footprint of the AI itself. The report says AI should be designed to be easily used by everyone so that it can set a foundation for a variety of tasksacross fields.
Enabling AI includes data, incentives and education.
The report says the transition should develop data standards and sharing processes to increase the availability of AI and the quality of information across platforms. Currently there is no data or information sharing standard and that is limiting increased use of AI for many organizations. Also, the report says there are no current incentives to increase the use of AI in the marketplace and the field needs to find ways to place economic and social value on the systems beyond use by regulators. By doing so, there needs to be an educational element to encourage consumers and workers with the value of AI regarding technology and skill development.
Governing AI includes risk management, standards and responsibility.
There needs to be criteria to regulate AI, use its technology and account for problems that may arise. Additionally, there should be compatible software standards and interoperable interfaces to make AI more efficient to implement. Finally, the report suggests there are ethical standards and responsible use practices to make sure AI is beneficial to all and there are no societal harms. These include OECD’s five core AI principles ofinclusivity, fairness, transparency, robustness and accountability.