AI for Good: The Power Paradox
The primary concern raised over the emergence of AI data centers is sustainability. Where will we get all the power and water to run this high-performance computer equipment? The water issue has already been solved. With modern closed-loop cooling technologies, data centers don’t need millions of gallons of water. Cooling systems only need to be filled one single time, usually with a few hundred thousand gallons, and then no more water is needed to cool equipment for the life of the facility. Data centers built with modern closed-loop cooling use less water than a swimming pool, a golf course, a factory, a restaurant, or a human being over the lifetime of the facility. Plus, AI hardware is being designed to withstand temperatures warmer than a hot tub, reducing cooling demands.
Solving Renewable Energy Challenges
Back to power. According to the Lawrence Berkeley National Laboratory’s report, data centers are forecasted to use up to 66GW of power by 2028. More than 2,000 GW of renewable power is currently waiting to be connected to power grids. This is more than the current installed power capacity in the U.S. as a whole, which is around 1,250 GW. So there is not a shortage of power or renewable energy. The biggest hurdle to widespread renewable energy adoption isn’t generating renewable energy; it’s connecting that energy to the grid and making it reliable. The process is complex and time-consuming. New renewable energy plants and wind farms face a 5+ year median wait time before they are connected to the grid. But guess what speeds up the process of renewable energy integration? AI.
According to The Renewable Energy Institute, “Embracing AI-powered solutions will be essential for achieving a reliable, affordable, and carbon-free energy future worldwide.” AI solutions enable grid operators to manage complexity, improve predictability, and boost efficiency. Legacy power grids were designed for a centralized, highly predictable, unidirectional flow of electricity from coal or gas power plants to consumers. Fossil fuels provide a constant energy supply, while wind and solar energy are inherently intermittent. Engineers must solve for stability in addition to the already complex task of adding more load to the grid system.
Accelerating Grid Interconnection
The first steps to adding new power sources to the grid involve reviewing applications for interconnection and conducting impact studies. Processing these applications requires hours of engineers’ time, and studies require manual simulations that can take 5-6 months or more to complete before new power sources can safely connect to the grid. This, along with a multitude of new renewable energy sources and requests for power, has resulted in the massive bottlenecks and the 5+ year wait times we see today. AI can, and already is, automating parts of this process by analyzing applications, running simulations in parallel, summarizing results, and proposing solutions. Engineers will still oversee the results, but the overall process could change from months to weeks.
One such solution already in effect is Tapestry’s HyperQ. An agentic AI tool that PJM, the largest grid operator in the U.S., deployed to review interconnection applications in an effort to shatter the massive bottleneck. Interconnection applications are submitted in cycles. Operators receive hundreds of applications per cycle, and engineers have a set number of business days to respond to developers, flagging any issues with their applications. The tool made 9,312 assessments across 811 applications in parallel, meaning it compared the site control provisions in new applications against the region’s tariff rules and user manuals, analyzing and flagging deficiencies for human reviewers. A single application can be thousands of pages long, as are the books of rules that engineers must ensure compliance with for each interconnection. This initial part of the application process normally takes engineers away from their regular work for several weeks. HyperQ completed the initial processing layer for the site control portion of the cycle in six minutes and 15 seconds.
Another example is Envelio, a German company that has expanded internationally and helps utilities fully automate precise impact studies so that new resources connect to the grid up to 20 times faster. Machine learning models can run thousands of grid scenarios simultaneously rather than sequentially, drastically cutting down computation time for thermal and voltage limits. They enable operators to create digital twins of the electric grid, simulating real-world consequences under various stress conditions to predict infrastructure bottlenecks and other issues.
Managing Supply and Demand
Our legacy grid was a one-way system. Electricity traveled from power plants to homes and businesses, and any communication was also unidirectional. Meters had to be read manually. SCADA devices reported voltage, line current, and open/closed circuit breaker status to a central dispatch desk. This system worked for fossil fuels. Burning fossil fuels provides consistent energy from a small number of large power plants to communities. Renewable energy is the opposite. It is intermittent, variable, and supply is difficult to predict. It also comes from smaller distributed sources, like solar panels on the roofs of individual homes.
The grid requires meticulous control and a delicate balance. Supply must equal demand at all times, or power service will fail. Grid operators are skilled at balancing load, which requires predicting both generation and demand. Improved short-term forecasting and longer-term forecasting, predicting sharp spikes in demand from hours to days ahead, allows operators to balance supply and demand in real time and schedule reserves to avoid system stress that could trigger blackouts. With renewable energy in the mix, load balancing becomes much more complex, and if a utility cannot predict how much energy will be available tomorrow, it must keep fossil fuel plants operating for backup supply.
According to CSIS’s Energy Security and Climate Change Program, AI improves forecasting through advanced pattern recognition, incorporating complex nonlinear relationships between demand and factors such as weather, holidays, economic activity, and human behavior. Unlike traditional methods, its computational power can enable predictions at more granular levels, such as for individual neighborhoods or even individual buildings.
AI solves these challenges by transforming passive electricity networks into intelligent, self-balancing smart grids. AI algorithms continuously ingest millions of data points from meters, weather sensors, and substations. Smart grids use this data to dynamically reroute electricity to minimize transmission loss, predict hardware failure, and balance loads in real time.
For example, smart grids can temporarily reduce consumption in some places or shift EV charging to prevent overloads. By dynamically and flexibly meeting demand, operators reduce dependence on fossil fuel plants while maintaining resilience.
Forecasting Availability
Variable renewable energy is weather-dependent, and as more of it is integrated into the grid, control rooms need better tools to accurately forecast supply. As it is with demand, if a regional utility grid cannot predict how much renewable energy will be available tomorrow, it must keep polluting fossil-fuel plants running as an active backup.
AI eliminates this unpredictability by providing hyper-accurate solar and wind energy generation forecasts. According to researchers at MIT, AI algorithms provide the complex information management needed to forecast within just a few hours which plants should run, while also ensuring that the frequency, voltage, and other characteristics of the incoming power meet grid requirements. According to CSIS, “AI tools are advancing weather prediction, where deep-learning models can emulate numerical weather prediction and downscaling to deliver high-resolution, probabilistic solar and wind forecasts at low computational cost.”
Another crucial component of integrating renewable energy is battery storage. Because the energy is intermittent, it must be stored and used when wind and solar sources are unavailable. AI enhances battery efficiency by predicting optimal charging and discharging times based on weather forecasts, grid demand, and electricity pricing. This predictive capability allows batteries to store excess energy during low-demand periods and discharge it when demand peaks.
AI tools are also being used by wind and solar farms. According to the Renewable Energy Institute, AI enables real-time power output optimization, adjusting turbine settings like blade angle and rotation speed in response to changing wind conditions to maximize energy capture. AI is particularly valuable for offshore and remote wind farms, where monitoring and maintenance can be logistically challenging and expensive. By enabling remote monitoring and automated adjustments, AI reduces the need for on-site staff and allows operators to manage turbines remotely.
One AI tool currently in use is Google’s DeepMind wind power forecasting. DeepMind applied advanced neural network architectures to 700 megawatts of wind power capacity across the central United States. By training the AI on historical turbine telemetry and complex meteorological models, the system successfully predicted wind farm power output 36 hours ahead of actual generation. This enabled Google to make optimal hourly delivery commitments to the regional power grid a full day in advance, increasing the economic value of their wind energy by roughly 20% and proving that renewables can reliably displace fossil-fuel baseloads.
Renewable Energy Dominance Requires AI
While AI computing requires more energy, it is also essential to making a carbon-free energy distribution system a reality. The complexity of modern power grids, combined with real-time weather forecasting, is a task only AI can help us manage effectively. Humans must ensure these systems are monitored, controlled, and protected as they are rolled out across the globe to integrate renewables, eliminate power outages, and minimize waste.