The AI-powered industrial (r)evolution to unlocking a flexible and resilient energy grid
By integrating AI, particularly through adaptive methods, into their processes and schedules, manufacturers can gain unprecedented, real-time control over their energy footprint.
The global energy landscape has been undergoing a monumental shift towards renewable sources, but this transition already demonstrated the deficit in infrastructure.
Our existing electricity grids, originally designed for the predictable flow of fossil fuels, are struggling to keep pace with the variable and often overwhelming output of renewables. This mismatch leads to inefficiencies, i.e. continuous curtailments of clean energy, and highlights a critical need for smarter, more flexible grid governance, management and planning.
A pivotal solution lies in the hands of industrial manufacturers, who, through the strategic adoption of Artificial Intelligence (AI), can transform from passive consumers into active participants in grid stability.
Empowering Industry with AI for Dynamic Energy Flexibility
Industrial operations, often characterized by significant energy consumption, present a unique opportunity for demand-side energy flexibility.
By integrating AI, particularly through adaptive methods, into their processes and schedules, manufacturers can gain unprecedented, real-time control over their energy footprint.
This isn’t just about energy efficiency; it’s about dynamic, intelligent response, enabling industries to become proactive grid balancers.
AI-driven systems can enable industrial facilities to:
* Compensate for grid fluctuations: When renewable energy sources experience unexpected dips in capacity (due to cloud cover, wind lulls, etc.), AI can intelligently reduce or shift industrial energy demand. This acts as a crucial, immediate buffer to maintain grid stability without relying on traditional fossil fuel peaker plants.
* Absorb excess in renewable generation: Conversely, during periods of abundant and cheaper renewable energy generation, AI can strategically increase energy consumption for non-time-sensitive processes (e.g., pre-heating materials, charging industrial batteries, operating pumps, even mining bitcoins if needed). This effectively “soaks up” surplus power, preventing curtailment and leveraging clean energy at its lowest cost.
This sophisticated level of control moves beyond simple energy saving; it enables a truly interactive and proactive technical restriction management, a dynamic partnership between industry and grid operators.
The true potential of AI in industrial energy management lies in its ability to facilitate participation in competitive demand flexibility markets and ancillary services.
Governing the Grid with AI: Competitive Markets and Ancillary Services
The true potential of AI in industrial energy management lies in its ability to facilitate participation in competitive demand flexibility markets and ancillary services.
Adaptive AI methods can:
* Optimize Bidding and Response: AI can analyze real-time grid conditions, market prices, and internal operational constraints to optimize when and how an industrial facility offers demand reduction or increase to the grid. This allows industries to participate profitably in energy markets.
* Provide Ancillary Services: Beyond simple energy balancing, AI-managed industrial loads can provide critical ancillary services such as frequency regulation, voltage support, and operating reserves. By rapidly adjusting consumption, industries can act as virtual power plants (#VPP), offering essential stability services that traditionally relied on fossil fuel generators.
This proactive approach from the demand-side is not just about reacting to grid needs; it’s about smoothing the transition to a high-renewable energy future. By providing reliable, flexible demand, industries reduce the risk associated with variable renewable generation, thereby unlocking new renewable investments that might otherwise be deemed too risky or too costly due to grid integration challenges.
The Cost of imbalance: IEA data corroborates such challenges
The financial ramifications of an inflexible grid and renewable energy curtailment are substantial, as highlighted by data from the International Energy Agency (IEA):
* Wasted energy and cost opportunity in curtailment: The IEA estimates that every 1 percent increase in curtailment could result in billions of dollars in lost revenue globally.
In key markets like China, Germany, and the United States, curtailment rates for wind and solar have reached between 5% and 15% of total generation. Globally, curtailment rates have been increasing, reaching around 10% in several countries. This signifies a significant amount of clean energy that is produced but not utilized, representing a direct economic loss and an environmental missed opportunity.
* Soaring grid operator costs: Grid congestion management is imposing increasingly heavy burdens on grid operators, costs which are often passed on to consumers.
For instance, TenneT, the Dutch transmission system operator, spent EUR 388 million on grid congestion management in 2022, a six-fold increase since 2020. Similarly, Germany saw its grid congestion management costs exceed EUR 4 billion in 2022, more than tripling since 2020.
These figures underscore the “unbalanced costs” that arise from an inflexible grid struggling to cope with the influx of renewables without adequate infrastructure and flexibility mechanisms. The occurrence of negative wholesale electricity prices in various regions, sometimes as low as -30 USD/MWh, further signals a critical lack of system flexibility.
By integrating industrial demand-side flexibility into their operational models, they gain a powerful, distributed, and clean balancing mechanism.
This shift in strategy also underscores the urgent need for significant infrastructure investment.
Redefining grid management: beyond traditional levers
For too long, grid operators have relied on traditional methods like natural gas and combined cycle plants to balance the grid. However, the energy transition demands a new paradigm. With AI-empowered industries, grid operators can learn to balance the system without exclusively depending on these fossil fuel levers.
By integrating industrial demand-side flexibility into their operational models, they gain a powerful, distributed, and clean balancing mechanism.
This shift in strategy also underscores the urgent need for significant infrastructure investment.
The IEA reports that at least 1,650 GW of renewable capacity is currently awaiting grid connections, a clear bottleneck. An additional USD 200 billion in annual investment is needed globally to upgrade and expand transmission networks through 2030.
Overall, grid investment needs to nearly double to over USD 600 billion per year by 2030 to support clean energy transitions. Strengthening and modernizing grid lines, rather than merely addressing congestion reactively, is paramount. Investing in a resilient and smart grid infrastructure is key to avoiding the costly imbalances that can lead to widespread blackouts, such as those experienced in Spain on April 28th, or similar incidents in the UK.
Prioritizing infrastructure development over the mounting costs of grid instability and blackouts is not just an economic imperative, but a societal one.
IEA also added that, in 2024 alone, $700 billion were invested (between public and private funding) in energy efficiency. The result was a weak 1% progress versus an expected 4%.
What if those investment would have addressed an AI governance of production processes that will be integrated in a profitable interaction with the grid operators to generate future revenue streams?
New infrastructure investments need to work along a grid resilience plan.
The Future is Flexible, Resilient, and AI-Driven
The path to a fully decarbonized and stable energy grid requires a holistic approach. Industrial manufacturers, armed with AI and leveraging adaptive methods, are poised to play a transformative role by offering crucial demand-side flexibility through competitive markets and ancillary services.
This proactive tool from the demand side, combined with robust infrastructure investments, will pave the way for a grid that can seamlessly integrate renewable energy, manage unexpected fluctuations, and ensure a reliable, sustainable power supply for all.
For more insights into the challenges and solutions for a gridlocked energy transition, and the data supporting these points, you can refer to the following sources:
* Solutions for a Gridlocked Energy Transition
* Focus On Avoiding Renewable Curtailments-IEA Prescriptions — Saur Energy International
* Massive global growth of renewables to 2030 is set to match entire power capacity of major economies today, moving world closer to tripling goal — News — IEA
* Will more wind and solar PV capacity lead to more generation curtailment? — Renewable Energy Market Update — June 2023 — IEA
* Grid congestion is posing challenges for energy security and transitions — Analysis — IEA
* Electricity Grids and Secure Energy Transitions — Analysis — IEA
* Prices — Electricity 2025 — Analysis — IEA
