AI and the energy grid are becoming closely connected as grids grow more complex and electrification increases. AI can help save billions by improving grid operations, making energy systems more flexible, and solving key energy challenges.
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According to recent research, the combination of AI and the energy grid could be worth $13 billion. That number will likely skyrocket, given the vast potential for expanded integration across multiple use cases.
Grid and energy infrastructure are increasingly complex as digitisation and decentralisation trends continue to gain traction. Simultaneously, power systems need to accommodate multi-directional electricity flows between the grid and users, with the latter buying more and more solar panels and EVs. The result is less predictable energy flows.
In this scenario, which points toward the future of energy, artificial intelligence (AI) and machine learning are indispensable tools to manage the challenges of modern energy infrastructure.
At the end of 2023, over 2.5 TW of solar, wind and storage projects were awaiting connection to U.S. grids. Simultaneously, the number of grid end points keeps growing.
Utilities and energy companies are also wondering how to best use the vast data troves they have access to. Smart meters, wind turbines, and EV charging stations collectively generate billions of data entries annually. For example, the global fleet of wind turbines alone produces over 400 billion data points per year.
Within said data, there may be hidden ways of increasing cost-efficiency, shortening connection backlogs and more.
As Hannes Pfeifenberger, a principal at Brattle Group consultancy, puts it in an interview with Reuters Events:
“We can get at least 50% more out of the existing grid with grid enhancing technologies, power flow control devices and high-performance conductors that can help RTOs address the rapid load growth that’s coming and do so at a lower cost.”
AI’s ability to analyse data, identify trends and create new insights is core to realising that 50% increase.
For utilities, a range of opportunities already exist to use the power of AI to improve performance. Some of the main use cases include:
1: Enhancing grid resilience and flexibility: Improving grid resilience and managing flexibility by stabilising the grid through smarter, data-driven decisions in real time.
2: Making EVs work with the grid: Ensuring EV adoption doesn’t overwhelm the local grid by analysing charging patterns and recommending optimal charging times or even automating the process to balance demand.
3: Predictive maintenance: Identifying potential faults before they happen. Examples include machine learning algorithms to reduce outages and vibration that can cut downtimes.
4: Peak load shifting: Dynamically shifting energy consumption to less busy times, easing grid strain.
5: Managing and controlling grids: Use data from sensors, smart meters, and IoT devices to monitor and control energy flow to improve grid management and optimise power delivery.
6: Demand response and forecasting: Forecast electricity usage and prices and set dynamic pricing to manage demand fluctuations effectively while optimising operational costs.
7: Customer service: Enhancing customer interactions through intelligent apps and chatbots and offering personalised energy insights.
8: Dynamic line ratings: Improving real-time modelling of transmission line capacity to reduce renewable energy curtailments and increase grid resilience.
9: Renewable energy forecasting: For example, Google’s DeepMind has improved wind power output forecasting, boosting its financial value by 20%.
10: Optimising Distributed Energy Resources: Coordinating decentralised DERs and aggregating them into virtual power plants (VPPs), balancing supply and demand in real-time to stabilise the grid.
The adoption of AI in the energy grid needs proper preparation to maximise the positive effects.
One is finding the best possible use case fit. Said differently, you could probably pound in a screw with a hammer, but if you use the screwdriver, it would probably work a lot better.
Some of the main things energy companies and utilities need to be aware of include:
AI’s potential in the energy sector is transformative. It offers potential solutions to address the challenges that energy systems face today. However, this potential is reliant on addressing key issues, like cybersecurity, data handling, and AI bias. Addressing these issues is not optional in a critical infrastructure industry like energy – it is critical.
It is worth keeping an eye on AI being but one of many tools that can help manage the complexity of an energy grid with millions of connected assets. DERMS, advanced smart EV charging, AMI infrastructure, and many other complementary systems turn the grid into a dynamic, resilient system capable of handling continuous electrification and decarbonisation.
In this context, it may be useful to think of the energy grid as a vast orchestra, where AI serves as a featured instrument, but one that acknowledges the conductor, the composer, and its fellow musicians.
