Amid surging electricity demand and slow-paced infrastructure growth, modern energy infrastructure faces a swathe of challenges. While high demand is nothing unprecedented for modern grid operators, the speed at which demand is growing is remarkable — and it's projected to keep growing.
Just last year, the International Energy Agency (IEA) reported that global energy consumption rose by 2.2%, representing a sharp increase from the average 1.3% increase between 2013 and 2023. This growth is also unevenly distributed, with data centers eating up an increasingly larger portion of the available production. In just the United States, the Department of Energy projects that data centers could go from consuming 4.4% of total American electricity production in 2023 to up to 12% by 2028.
At the same time, just adding renewables isn't enough. Over 1,500 GW of renewable projects worldwide face delays because there aren't enough wires to deliver the power they generate. Energy grids throughout the US and the world are under extreme pressure as demand growth far outstrips the speed of new infrastructure construction.
Decentralized power with complex demands
Most of the energy we use today comes from distributed energy resources (DERs) like hydroelectric dams, wind turbines, or solar panels. The state of California leads in DER adoption, with rooftop solar installations growing 34% year over year in 2023.
Investments in DERs are projected to increase sevenfold worldwide by 2030 and reach up to $846 billion within a decade, with an additional $285 billion allocated to energy storage systems. This shift toward decentralized generation brings tremendous benefits, like enhancing the resilience of our energy infrastructure, but it also demands real-time coordination far beyond what operators are used to. Traditional grid management methodologies cannot dynamically balance this kind of complexity while maintaining stable voltage levels.
Part of the problem, part of the solution
While AI is one contributor to this heightened demand and complexity, given the energy needed for data centers that run high-powered LLMs, it can also be the key to navigating this new paradigm.
Since it can process information at a speed and scale far beyond a human level, machine learning can be deployed to analyze usage data and dynamically adjust systems when needed. AI offers immense potential when it comes to identifying emerging instability patterns, diagnosing root causes, and using data to suggest corrective actions.
Case studies for AI and energy management
AI's transformative potential lies in shifting energy management from reactive to predictive. Consider the case of Ercros, a chemical manufacturer in Sabiñánigo, Spain. For them, AI analysis of their plant's power consumption proved to be more than an immensely valuable asset: It helped them avert a disastrous outage.
Production at Ercros requires flawless voltage quality on an 11 kV line. Continuous monitoring devices transmit real-time data to a Siemens cloud analytics platform, where AI algorithms compare this information to past benchmarks.
During operation, the system detected abnormal reactive power consumption and harmonic distortion. "Thanks to the warning, we could see that we had a problem with a capacitor battery and a 5th harmonic filter," said Roberto Díaz Juan, head of engineering and maintenance at Ercros. The alert recommended filter protection upgrades to prevent external transients from tripping the system.
AI's predictive capability extends beyond industrial facilities to entire distribution networks. Elvia, Norway's largest grid operator, confronted a different challenge: managing low-voltage networks across a vast area that also sports the world's highest adoption rate of electric vehicles and heat pumps.
Elvia's solution centered on Gridscale X LV Insights from Siemens Xcelerator, which constructed a comprehensive digital twin of their low-voltage infrastructure, factoring in different kinds of data sources. Now in place, the platform provides complete grid transparency, identifies overload risks before customers experience disruptions, and optimizes hosting capacity without physical expansion. "With increased flexibility and oversight over our grid, we can now manage our low-voltage system in a much more efficient manner — from planning to operations and maintenance," said Erik Jansen, head of grid operations at Elvia.
Leveling up digital transformation
A fundamental shift in operational capability is needed to address tripled demand, integrate decentralized renewables, and prevent cascading failures. Gridscale X from Siemens Xcelerator can help American companies evolve, with a modular software architecture that seamlessly and quickly integrates within existing IT/OT landscapes for end-to-end grid visibility and control.
By deploying AI and smart algorithms, Gridscale X acts as an energy co-pilot: it continuously analyzes complex grid data, identifies potential instability, diagnoses root causes, and recommends prioritized, actionable solutions.
Protecting energy systems, from individual plants to whole regional grids, is no easy task — unless you've got the right tool for the job. As the energy industry evolves, smarter AI deployment will be key for industry players seeking to reach the next level in their digital transformation journeys.
Learn how AI-powered solutions from Siemens can help power your next leap forward.
This post was created by Siemens with Insider Studios.