The growing energy demand from consumers, data centers, electric vehicles and other sources is increasing pressure on existing infrastructure. Electric utility demand will grow significantly over the next 5-10 years. According to a Wells Fargo analysis published in April, after a decade of flat power growth in the US, electricity demand is forecast to grow 20% by 2030.
Some electric utilities have embraced strategic innovations to overcome these unique challenges and drive sustainable growth. Technologies like AI and data analytics are helping utilities enhance maintenance, operations and investment decisions while improving efficiency and resilience. This fast-evolving landscape undoubtedly dictates the need for new solutions like AI and data to promote a sustainable energy supply.
The growing popularity of EVs is transforming how electricity providers approach grid management. With millions of vehicles connecting to charging stations, balancing supply and demand is more complex than ever. This evolution underscores the importance of integrating AI into energy systems.
By leveraging data analytics, providers can predict charging patterns, optimize energy distribution and ensure grid reliability during peak hours. Smart grid technology plays a pivotal role here, enabling real-time communication between EV chargers, power plants and consumers to maintain efficiency. The rise of EVs presents challenges but offers an opportunity to modernize the grid with cutting-edge innovations.
As companies adopt AI, data center use will expand, putting more pressure on electric grids. Wells Fargo expects an additional 323 terawatt hours of electricity demand from data centers in the US by 2030. The forecasted power demand from AI alone is seven times greater than the current annual electricity consumption in New York City, which is 48 terawatt hours. Goldman Sachs projects that data centers will represent 4% of US electricity consumption by the decade’s end.
The surging power demand is causing new challenges for tech companies like Google, Microsoft, Apple and Meta, which deal with massive data collection and processing. These companies will power their data centers with renewables to reduce their emissions. However, solar and wind are inadequate to completely meet the required base load of a data center because the electric demand will increase significantly.
The rapid rise of AI across industries has significantly increased the electricity demand. Energy-intensive processes like data analytics and machine learning require vast computing power, consuming substantial energy resources. IDC predicts that AI energy consumption will grow at a CAGR of 44.7% by 2027.
As data analytics becomes increasingly integral to AI development, the need for high-performance data centers has surged, driving further electricity consumption. For electricity industry professionals, this shift underscores the importance of modernizing infrastructure to accommodate the growing energy demands of AI-driven systems while integrating sustainable practices to support long-term energy efficiency.
Along with rising demand for more electricity, regulators and customers continue to push and expect utilities to reduce emissions. As a result, electric utilities are investing significantly in AI to leverage more distributed energy resources and aggregate renewables into their systems. AI systems can improve the efficiency of existing systems and help manage the integration of renewable energy.
According to Frost and Sullivan, most electric utilities only leverage about 2% and 4% of the data acquired from intelligent devices for analytics to enhance the efficiency of their operations. Meanwhile, more complex and digitally-driven critical assets are expected to increase from grid modernization and distributed energy resources investment over the next five years than in the past fifty years. As the number of new smart assets will increase significantly, utilities must find ways to maximize value and monetize that data.
AI for electric utilities is the future of data collection and analysis. As utilities add new connected devices, additional data streams are included for analysis, giving AI a more extensive dataset to find patterns and make more accurate predictions. More data means advanced forecasting and enhanced data analysis. With more granular data, the system can recognize minor asset performance deviations, leading to better forecasts for electricity demand.
Electric utilities must manage and optimize traditional transmission systems while integrating new technologies. For example, optimizing energy generation and distribution is one primary use of AI to maximize energy generation, transmission and distribution. Utility companies can also use AI to forecast energy demand more accurately based on historical data, weather patterns and other factors.
There are various ways AI and data are helping to transform the future of energy utilities. Let’s examine the ways.
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Proactive management using asset intelligence can drive cost savings and reduce breakdowns. Substation Asset Health AI tools are helping utility companies become more efficient in their operations and maintenance. Substations are complex facilities with millions of data points. AI can analyze sensor data from transformers to spot inefficiencies, predict maintenance needs and reduce downtime. Over the next few years, more uses for substation and transformer digital twin and asset health scoring will emerge.
Using AI, utility companies can predict when their equipment fails or needs maintenance. Machine learning can analyze large amounts of data from various sources, such as oil temp, oil pressure, load, usage stats, weather data, IR scans and historical maintenance records, to predict potential breakdowns before they occur. This approach minimizes downtime, reduces costs and improves the overall reliability of energy infrastructure. This type of smart maintenance is already in use for water utilities, where AI and data help predict leaks.
AI-powered smart grids promise to revolutionize power distribution. AI algorithms can analyze data from smart meters, line sensors, switches, smart relays and connected devices to understand real-time electricity demand and predict consumption patterns using historical and real-time data. Utility companies can model the electric grid as a connected graph with structures and buses as “nodes” and transmission line transformers as “edges.” These models simulate specific grid outages and address emerging challenges like detecting anomalies, technical issues, cybersecurity breaches, or power theft. AI provides utilities with the information they need to manage load balancing and reroute power to areas of need, if necessary.
Moreover, with new data streams from more connected devices, utilities will increasingly adopt digital twins for better visibility and scenario planning. With digital twins, AI can help manage smart grids (electricity supply networks using digital communications technology) to detect and react to electricity demand in real time. Smart grids with AI can detect faults or disruptions and identify the exact location to minimize downtime.
AI is pivotal in integrating renewable energy sources into national grids, providing a forward-thinking approach to sustainable energy policy. Leveraging predictive analytics for accurate weather forecasting provides more precise predictions of energy supply fluctuations. AI can optimize renewable energy production from solar and wind farms, and machine learning algorithms can help control and adjust renewable assets to maximize generation under different conditions. This capability is crucial for optimizing the deployment and use of solar and wind energy, where production can be unpredictable.
AI can also help maintain grid stability by providing real-time data analysis that supports better decision-making for grid operators. It can detect faults and anomalies in transmission lines to improve maintenance and avert outages. These insights ensure a reliable energy supply despite renewables’ inherent variability. Leveraging AI in energy operations could mean a more resilient grid, reduced reliance on fossil fuels, and significant strides toward carbon neutrality.
AI and advanced analytics can inform better capital expenditure decisions and optimize investment portfolios. Expansion is expensive for companies like utilities with vast infrastructure, and management needs to maximize its investment dollars. A good example of how AI can reduce the cost of expansion while providing decision-makers with the most cost-effective plans is in telecommunications. AI and data are helping inform telecommunication rollout plans within hours, not months, and models generate multiple options with costs and revenue for each.
Utility companies are also applying AI to streamline workforce operations. Machine learning optimizes field technician routes and schedules, improving the time for asset maintenance. In addition, computer vision and drones empower utilities to inspect remote infrastructure. Soon, ChatGPT assistance will show field employees how-to videos on performing maintenance activities and provide information about the asset they are working on.
Addressing business challenges in the electricity sector can be a significant roadblock to adopting advanced technologies like AI. The industry grapples with high operational costs, legacy infrastructure and regulatory complexity, which can divert resources from innovation.
For instance, while AI in energy has the potential to revolutionize operations — through data analytics, predictive maintenance and smart grid technology — companies may struggle to prioritize these advancements amid pressing financial and compliance pressures. Overcoming these business challenges is crucial to fully leveraging AI technology and driving the energy sector toward a more efficient, resilient and sustainable future.
Financial pressure can hinder electric utilities’ ability to innovate and invest in new technologies by limiting their available capital for research and development. Utilities may prioritize projects with quick returns on investment to discourage them from pursuing innovative technologies with longer development cycles and potentially high upfront costs to satisfy investors and regulators. Utility rates are often regulated, meaning significant investment in new technologies might require complex approval processes and are difficult to justify to regulators, especially if it could lead to high customer bills.
Trends are reshaping the industry and changing the priorities of innovation projects. For example, there is a growing need to flexibly accommodate 2-way power flow, which requires new equipment and management systems. Regulators will expect reliability and resilience to improve before approving advanced capabilities. In this challenging environment, maintaining and improving reliability requires determining the reliability benefit of a project per dollar spent and prioritizing projects accordingly.
Utilities need quick, informed decision-making for investment strategies to remain competitive. However, the biggest challenge utilities will face is an unreliable data pipeline. Legacy systems are not flexible, and many do not support data science or analytics. These systems are also large and overly complex, meaning utilities must retain a high-level skill set to keep everything working.
The challenge for utilities is their current data platforms won’t meet the needs of tomorrow. Utilities can build the central data lake, get started and collect a fair amount of accurate advanced metering infrastructure (AMI) data. However, utilities are asset-intensive, relying on hundreds if not millions of assets to stay operational. Moreover, utility assets are getting smarter, requiring operating models to evolve. As utilities become more digital, it is apparent that existing systems are not a long-term solution. A digital twin of a smart grid network can optimize the technology to get value out of the data.
Electric grids are prone to decentralized data. Now, utilities are integrating new elements into the grid, like electric vehicles, heat pumps, or batteries, and distributed generation from prosumers with self-consumption facilities, adding to the data complexity. In addition, the current trend is to connect new smart grid elements to the Internet of Things (IoT) technology.
These devices are optimal from a scalability, cost and performance standpoint but some lack the reliability and robustness of those used by industrial protocols in other environments such as SCADA. However, if adequately addressed, all data fed into digital twins can accurately represent the physical setting — this is critical because when it comes to the power grid, even a minor problem in data can lead to significant errors for the utility.
Cybersecurity on the power grid has been a critical issue for electricity operators for many years, and a smart grid value chain makes it more complex. Grids include multiple actors (energy producers, transmission system operators, distribution system operators, energy operators and end users), each wary of providing data outside their ecosystem for competitive, legal, or regulatory reasons. A digital twin can address these independent connections and avoid continuity of service or national security threats with strict cybersecurity mechanisms.
The most significant opportunity in the next 10 years for the energy transition may be Vehicle-2-grid (V2G). Electric vehicles can go beyond transportation to become integral components of our energy infrastructure by supplying stored energy back to the grid when needed. This benefit makes EVs attractive to buyers, but more EV charging will strain the grid.
AI can encourage and optimize the expected influx of EVs and provide a powerful example of how load-level information can help benefit EV drivers and utilities alike. Here’s how it works: AI allows for energy disaggregation, which is a complex way of saying that it provides visibility into the fine details of a house’s minute-by-minute energy use of critical loads. Visibility into EV charging (80% of which takes place at home) provides information that can assist the homeowner, the utility and the grid.
Existing grids can not accommodate high demand. As a result, if ten homeowners on a street start charging their EVs with fast chargers simultaneously, it is likely to surpass the capacity of the distribution transformer for that street. Even with the release of long-range battery cars with level-2 fast charging, staggering the charging by a few hours would still overload the grid.
Utilities can address this issue by using AI to identify homeowners with EVs and incentivize them to adopt time-of-use pricing. Two utilities offer programs to help EV customers save money when charging their EVs without burdening the grid. At Alabama Power, for example, they incentivize customers with level-2 chargers to charge at night by giving a 2-3 cent kilowatt discount. Florida Power and Light started a program with free level-2 charger installation and night and weekend charging for 31 dollars.
As the number of EVs increases, AI can become an arbitrage and forecasting tool to manage EV charging without stressing the grid. In exchange, EV owners get access to dynamic pricing, offering low pricing when there is excess capacity and super high pricing when there is higher demand.
These options are financially advantageous to the utilities and EV owners, but they are options that utilities can’t confidently present to customers without the sort of granular information AI provides. Utilizing AI-enabled visibility to offer EV-friendly rates or discounts on chargers helps utilities economically manage costly peak demands.
By integrating renewable energy sources like wind and solar with advanced grid management solutions, operators can better balance supply and demand, reduce transmission losses and minimize environmental impact. These innovations contribute to a more sustainable energy future and offer significant operational efficiencies and cost savings. For grid operators, leveraging AI and data is a strategic move toward achieving a cleaner, more efficient and more reliable energy system.
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