Artificial Intelligence, or AI, is currently one of the fastest-growing technologies. New advancements related to AI are happening at an exponential pace. A caveat to this growth is the lack of knowledge about what this technology is and how it works, in relation to integrating it with existing systems. A simple explanation of AI is machines performing tasks at a level of human intelligence. These tasks include learning, reasoning, problem-solving, automation, and planning. Machine Learning (ML) is one of the key components to building AI technologies. This component consists of algorithms that absorb and study functions from data to make predictions that are not predetermined through programming. This allows the technology to improve with each experience. As the utility industry undergoes a digital transformation, harnessing the power of AI is crucial. The adoption of AI in the utility space will optimize operations, improve resiliency and drive efficiency.
AI is proving to be an emerging part of essential utility tasks. Utilities across the country are implementing AI into safety and maintenance workstreams. AES utilized AI on predictive maintenance for smart meters in their advanced metering infrastructure. AI models were created from the data transmitted by the smart meters to allow anticipation of maintenance and fault determination. Utilizing this technology AES “…was able to eliminate 3,000 non-essential trips (called a “truck roll”), for an annual savings of $1M.” Itron published Exploring AI for Utilities, a report that provides insights from 600 utility executives across the world on the topic of AI and ML. According to the report safety is the number one use of AI and ML in the utility industry currently, accounting for 49% of all uses. In addition, predictive maintenance is a common use case, accounting for 33% of all uses. Utilizing AI for predictive maintenance not only provides benefits to the utility with shortened processes, but also provides benefits to the customers in preventing potential outages.
Utilities are harnessing AI in grid management and operations, both vital workstreams. An example of this is enhancing grid technologies like dynamic line rating. Dynamic line rating, or DLR, allows operators to increase the current through the transmission system, which ultimately increases interconnections, an essential task to facilitate decentralization. DLR integrated with AI optimizes this process based on adjustments from external conditions like wind and temperature. AI was implemented in California to model the predicted impact of distributed energy resources. Utilizing this technology the optimal placement for EV chargers to allow for equitable access while minimizing necessary infrastructure upgrades to the grid, thereby lowering the cost of electricity.
The utility industry is driving innovations leveraging this new technology. Aclara, a manufacturer of smart meters, developed a new meter that is powered by AI technology. These new meters will have 100 times the processing power of traditional meters and will allow grid edge computations. This breakthrough will transform the process of grid planning and load management. Exelon, in partnership with NVIDIA, is developing a drone inspection process utilizing an AI model to detect defects in grid assets. Drones are widely used by utilities to conduct various inspections and initiatives and can be deployed when outages occur to find the root cause in turn restoring power faster to customers.
While AI is a promising technology there are risks associated with introducing it into use cases in the utility industry. AI learns from data provided to it, which can introduce bias into the decision-making process. Ensuring that quality data is being fed to the model is a major struggle for utilities. In addition, AI can cause cyber security concerns, specifically in applications utilizing grid edge devices. If AI is utilized for a grid edge device it opens the door to potential cyber security attacks potentially leaving many without electricity. The recent advancements in AI have shown the need for regulations and ruling on standards. The AI regulatory landscape is ever changing, but the first steps have been put in place to create guidelines for this promising technology. The National Institute of Science and Technology developed the Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. Published in the Summer of 2024 it provides a starting place for identifying risks in AI use cases and the specific action items to mitigate these risks. Implementing security standards is essential for utilities to maximize the benefits of AI while controlling the risks.
As the utility industry continues to embrace digital transformation, AI is emerging as a pivotal force driving innovation and efficiency. One of the most promising applications is in predictive maintenance, where AI algorithms analyze sensor data and historical records to anticipate equipment failures before they occur. This proactive approach not only reduces downtime and maintenance costs but also extends the lifespan of critical infrastructure, ensuring a more reliable energy supply.
Moreover, AI is revolutionizing grid optimization by enabling real-time data analysis and predictive modeling. As the world moves towards cleaner energy solutions these capabilities allow for better management of energy flow, balancing supply and demand, and seamlessly integrating renewable energy sources into the grid. AI-driven energy forecasting further enhances this process by providing utilities with precise demand predictions, which helps in optimizing production and distribution, ultimately reducing waste and conserving resources.
In addition to operational efficiencies, AI is transforming customer experiences by offering personalized services and energy-saving recommendations. By analyzing customer data, utilities can enhance satisfaction and engagement, building stronger relationships with their consumers. Furthermore, as the sector becomes increasingly digitized, AI is playing a critical role in cybersecurity, detecting and mitigating threats to protect vital infrastructure.
The utility industry can harness the power of AI in multiple different use cases. These use cases will provide benefits to both the utility and the customers. The industry is also driving innovations to create new technologies and uses cases for AI. Balancing the power of this technology with the potential risks is essential for the implementation to be a success. Utilities need to take a tailored approach to integrating AI into existing systems to enable quality data practices and solutions that best serve their needs. These advancements emphasize a future where AI is not just an enabler but a cornerstone of a more efficient, sustainable, and resilient utility industry.