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By Cary Springfield, International Banker
An AI (artificial intelligence) data-centre developer can secure land, chips, capital and customers, then wait years for a grid connection capable of serving the site. The World Economic Forum (WEF) calls one proposed response “everything-to-grid”: coordinating flexible loads, storage and distributed generation so that electricity consumption and supply can respond to network conditions. For data centres, the opportunity lies in turning some computing demand and backup infrastructure into controllable grid resources, provided the resulting savings outweigh the value of uninterrupted computing.
The concept allows electric vehicles (EVs), buildings, factories, batteries and data centres to operate as active participants in the power system, particularly by reducing consumption or shifting it to another time, storing electricity or even returning it to the grid when local demand is high. Its value comes from mobilising electricity at the place and at the moment when it is most useful. Where grid operators permit, a data centre that can reliably reduce its power consumption during periods of strain may secure an earlier grid connection, benefit from lower peak-demand charges or earn revenue by supporting the grid.
“The issue is not simply the availability of energy, but whether it can be mobilized when the grid needs it most,” the World Economic Forum explained. “Everything-to-grid energy closes that gap. Every building, vehicle and device becomes a place that can store power, return it and help balance supply and demand in real-time, turning the grid into a network of intelligent nodes.”
Such capabilities could thus turn power flexibility into a highly useful asset, especially given the scale of the world’s ongoing data-centre buildout. In its 2025 Energy and AI report, the International Energy Agency (IEA) estimated that an AI-focused data centre could consume as much electricity as 100,000 households, while the largest projects under construction at the time could consume up to 20 times as much.
Gartner expects that demand to continue rising. The research and advisory firm projected that worldwide data-centre electricity consumption would grow from 2025’s 447 TWh to 565 TWh in 2026, an increase of 26 percent, with AI-optimised servers accounting for 31 percent of the total. “Surging demand for compute-intensive AI workloads is driving unprecedented data centre power growth, while AI capacity is now constrained by power availability, making data centre power security the new battleground for scaling and protecting margins in the global AI race,” said Linglan Wang, director analyst at the technology intelligence firm. Separately, the IEA estimates that global data centre electricity consumption will rise from 485 TWh in 2025 to around 950 TWh by 2030, with consumption by AI-focused facilities tripling over the same period.
Data-centre capacity is geographically concentrated, with nearly half of US capacity clustered in five regions. National electricity supply may appear adequate while particular counties, substations or transmission corridors face severe stress. “In the United States, data centres account for nearly half of electricity demand growth between now and 2030,” the IEA explained in its April 2025 report, Energy and AI. “By the end of the decade, the country is set to consume more electricity for data centres than for the production of aluminium, steel, cement, chemicals and all other energy-intensive goods combined.”
Connection times further compound the problem. In its 2025 Energy and AI report, the IEA estimated that grid constraints could delay around 20 percent of global data-centre capacity planned for construction by 2030. At the time, the agency noted that new transmission lines in advanced economies could take four to eight years to build, while waiting times for transformers and cables had doubled over the preceding three years. Tight supplies of gas turbines could also complicate plans to bypass the grid through on-site power generation.
These delays create costs beyond the technology sector. Grid upgrades may be recovered through customer bills. Where generation and network capacity are scarce, serving new data-centre demand may delay connections for factories, housing and wider electrification projects. Communities can also resist projects if they believe data centres will raise electricity prices or receive preferential access to limited grid capacity. An April 16 IEA report observed that data centres create distinct affordability concerns because “they have large, concentrated power loads and scale up rapidly, often triggering the need for new generation assets and grid investment”.
AI expansion increasingly depends on substations, transformers, generation, storage and control software that can coordinate electricity consumption. Although electricity systems have traditionally been organised around large generators feeding power through transmission and distribution networks to end users, the “everything-to-grid” framework adds two-way movement and real-time coordination. A parked electric vehicle, a commercial battery, a refrigerated warehouse or a data-centre backup system can thus respond efficiently to price signals and grid conditions.
Several technologies are required, including storage, which is becoming cheaper and more varied. LFP (lithium iron phosphate), a lithium-ion chemistry, dominates new battery-storage deployments because of its relatively low cost, thermal stability and cycle life. Sodium-ion and flow batteries may suit other applications, including those prioritising material availability or longer discharge periods.
Power electronics represents another vital layer, with inverters and advanced semiconductors governing how electricity moves between batteries, buildings and networks. Higher conversion efficiency allows more stored energy to survive the round trip, while advanced inverters can help stabilise voltage and frequency.
And advances in orchestration software are enabling aggregators to combine thousands, or perhaps even millions, of separate devices into a virtual power plant that responds as if the assets represent a single power station. The WEF also noted that market arrangements are beginning to reward storage owners for services such as capacity, rapid response and frequency control, rather than paying only for the electricity supplied.
Australia has demonstrated rapid expansion of distributed storage, although full grid coordination remains less developed. The Clean Energy Council (CEC) recorded 183,245 home-battery sales during the second half of 2025. By August 2026, the Australian government reported that more than 500,000 batteries had been installed under its Cheaper Home Batteries programme since its launch in July 2025. Under the federal programme, on-grid systems must be technically capable of participating in a virtual power plant, although owners are not required to enrol to receive the federal discount.
But data centres need flexibility as well as generation capacity. While more power plants and transmission lines remain essential, greater flexibility can make existing infrastructure work harder and reduce the need to size every asset for the highest-demand hour.
Data centres contain several potential sources of flexibility in this regard. Batteries already installed for backup can provide short bursts of support, while cooling systems can pre-cool facilities or alter their operating patterns within safe limits. Some computing workloads can also be delayed, slowed or moved to another location without affecting urgent customer tasks. And on-site generation can be coordinated with storage rather than operating independently.
A peer-reviewed Nature Energy field trial published in December 2025 found that software-based workload orchestration reduced the power draw of a 256-GPU (graphics processing unit) cluster in Phoenix by 25 percent for three hours while maintaining AI quality-of-service guarantees. A separate June 2026 study reported that a 130-kW (kilowatt) cluster in London responded successfully to 22 simulated and live dispatch events. These experiments demonstrate technical feasibility, although neither establishes that the same performance can be achieved across an entire hyperscale facility drawing hundreds of megawatts of power.
Another modelling study found that flexible AI demand reduced combined grid investments and operating costs by between 3 and 21 percent across the scenarios tested. The benefits varied considerably by location, network congestion and the share of computing that could be deferred or transferred. As such, greater flexibility did not always reduce the required generation capacity.
Controlled flexibility must also be distinguished from unexpected disconnections. On 22 July 2026, nearly 4,000 MW of data-centre load in northern Virginia disconnected from the PJM grid and switched to backup generation, requiring operators to manage the resulting changes in voltage and frequency. In September, PJM proposed new requirements for large computing loads to remain connected during certain grid disturbances.
For lenders and investors, flexibility becomes a project variable rather than a secondary engineering detail. Its value may come through faster grid connections, lower demand charges or contracted revenue from capacity and ancillary services. Those benefits must be weighed against curtailment obligations, computing-service guarantees, battery degradation, warranty restrictions and cybersecurity risks.
Private generation may accelerate projects, but a collection of self-contained energy islands could leave wider power systems with duplicated capacity and weak coordination. On-site gas generation may accelerate some projects, although certain configurations require batteries or other fast-response equipment to manage rapid changes in AI demand. They also expose projects to fuel-price, permitting and emissions risks.
Everything-to-grid offers a more integrated route. Data centres can combine on-site power and storage with grid participation, providing support during stressed periods and drawing electricity when the system has spare capacity. The IEA argues that, with suitable incentives, next-generation AI facilities could become grid assets and help relieve system pressure.
The data-centre boom is forcing digital companies to engage directly with the machinery of the power system. Generation and transmission investments will carry much of the load, but those assets usually cannot be expanded as quickly as computing capacity can be deployed. Flexibility can narrow that timing gap.
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The post Everything-to-Grid: Can Flexible Power Ease the Data-Centre Bottleneck? appeared first on International Banker.
An AI (artificial intelligence) data-centre developer can secure land, chips, capital and customers, then wait years for a grid connection capable of serving the site. The World Economic Forum (WEF) calls one proposed response “everything-to-grid”: coordinating flexible loads, storage and distributed generation so that electricity consumption and supply can respond to network conditions. For data centres, the opportunity lies in turning some computing demand and backup infrastructure into controllable grid resources, provided the resulting savings outweigh the value of uninterrupted computing.
The concept allows electric vehicles (EVs), buildings, factories, batteries and data centres to operate as active participants in the power system, particularly by reducing consumption or shifting it to another time, storing electricity or even returning it to the grid when local demand is high. Its value comes from mobilising electricity at the place and at the moment when it is most useful. Where grid operators permit, a data centre that can reliably reduce its power consumption during periods of strain may secure an earlier grid connection, benefit from lower peak-demand charges or earn revenue by supporting the grid.
“The issue is not simply the availability of energy, but whether it can be mobilized when the grid needs it most,” the World Economic Forum explained. “Everything-to-grid energy closes that gap. Every building, vehicle and device becomes a place that can store power, return it and help balance supply and demand in real-time, turning the grid into a network of intelligent nodes.”
Such capabilities could thus turn power flexibility into a highly useful asset, especially given the scale of the world’s ongoing data-centre buildout. In its 2025 Energy and AI report, the International Energy Agency (IEA) estimated that an AI-focused data centre could consume as much electricity as 100,000 households, while the largest projects under construction at the time could consume up to 20 times as much.
Gartner expects that demand to continue rising. The research and advisory firm projected that worldwide data-centre electricity consumption would grow from 2025’s 447 TWh to 565 TWh in 2026, an increase of 26 percent, with AI-optimised servers accounting for 31 percent of the total. “Surging demand for compute-intensive AI workloads is driving unprecedented data centre power growth, while AI capacity is now constrained by power availability, making data centre power security the new battleground for scaling and protecting margins in the global AI race,” said Linglan Wang, director analyst at the technology intelligence firm. Separately, the IEA estimates that global data centre electricity consumption will rise from 485 TWh in 2025 to around 950 TWh by 2030, with consumption by AI-focused facilities tripling over the same period.
Data-centre capacity is geographically concentrated, with nearly half of US capacity clustered in five regions.
Data-centre capacity is geographically concentrated, with nearly half of US capacity clustered in five regions. National electricity supply may appear adequate while particular counties, substations or transmission corridors face severe stress. “In the United States, data centres account for nearly half of electricity demand growth between now and 2030,” the IEA explained in its April 2025 report, Energy and AI. “By the end of the decade, the country is set to consume more electricity for data centres than for the production of aluminium, steel, cement, chemicals and all other energy-intensive goods combined.”
Connection times further compound the problem. In its 2025 Energy and AI report, the IEA estimated that grid constraints could delay around 20 percent of global data-centre capacity planned for construction by 2030. At the time, the agency noted that new transmission lines in advanced economies could take four to eight years to build, while waiting times for transformers and cables had doubled over the preceding three years. Tight supplies of gas turbines could also complicate plans to bypass the grid through on-site power generation.
These delays create costs beyond the technology sector. Grid upgrades may be recovered through customer bills. Where generation and network capacity are scarce, serving new data-centre demand may delay connections for factories, housing and wider electrification projects. Communities can also resist projects if they believe data centres will raise electricity prices or receive preferential access to limited grid capacity. An April 16 IEA report observed that data centres create distinct affordability concerns because “they have large, concentrated power loads and scale up rapidly, often triggering the need for new generation assets and grid investment”.
AI expansion increasingly depends on substations, transformers, generation, storage and control software that can coordinate electricity consumption. Although electricity systems have traditionally been organised around large generators feeding power through transmission and distribution networks to end users, the “everything-to-grid” framework adds two-way movement and real-time coordination. A parked electric vehicle, a commercial battery, a refrigerated warehouse or a data-centre backup system can thus respond efficiently to price signals and grid conditions.
Several technologies are required, including storage, which is becoming cheaper and more varied. LFP (lithium iron phosphate), a lithium-ion chemistry, dominates new battery-storage deployments because of its relatively low cost, thermal stability and cycle life. Sodium-ion and flow batteries may suit other applications, including those prioritising material availability or longer discharge periods.
Power electronics represents another vital layer, with inverters and advanced semiconductors governing how electricity moves between batteries, buildings and networks. Higher conversion efficiency allows more stored energy to survive the round trip, while advanced inverters can help stabilise voltage and frequency.
And advances in orchestration software are enabling aggregators to combine thousands, or perhaps even millions, of separate devices into a virtual power plant that responds as if the assets represent a single power station. The WEF also noted that market arrangements are beginning to reward storage owners for services such as capacity, rapid response and frequency control, rather than paying only for the electricity supplied.
Australia has demonstrated rapid expansion of distributed storage, although full grid coordination remains less developed. The Clean Energy Council (CEC) recorded 183,245 home-battery sales during the second half of 2025. By August 2026, the Australian government reported that more than 500,000 batteries had been installed under its Cheaper Home Batteries programme since its launch in July 2025. Under the federal programme, on-grid systems must be technically capable of participating in a virtual power plant, although owners are not required to enrol to receive the federal discount.
While more power plants and transmission lines remain essential, greater flexibility can make existing infrastructure work harder and reduce the need to size every asset for the highest-demand hour.
But data centres need flexibility as well as generation capacity. While more power plants and transmission lines remain essential, greater flexibility can make existing infrastructure work harder and reduce the need to size every asset for the highest-demand hour.
Data centres contain several potential sources of flexibility in this regard. Batteries already installed for backup can provide short bursts of support, while cooling systems can pre-cool facilities or alter their operating patterns within safe limits. Some computing workloads can also be delayed, slowed or moved to another location without affecting urgent customer tasks. And on-site generation can be coordinated with storage rather than operating independently.
A peer-reviewed Nature Energy field trial published in December 2025 found that software-based workload orchestration reduced the power draw of a 256-GPU (graphics processing unit) cluster in Phoenix by 25 percent for three hours while maintaining AI quality-of-service guarantees. A separate June 2026 study reported that a 130-kW (kilowatt) cluster in London responded successfully to 22 simulated and live dispatch events. These experiments demonstrate technical feasibility, although neither establishes that the same performance can be achieved across an entire hyperscale facility drawing hundreds of megawatts of power.
Another modelling study found that flexible AI demand reduced combined grid investments and operating costs by between 3 and 21 percent across the scenarios tested. The benefits varied considerably by location, network congestion and the share of computing that could be deferred or transferred. As such, greater flexibility did not always reduce the required generation capacity.
Controlled flexibility must also be distinguished from unexpected disconnections. On 22 July 2026, nearly 4,000 MW of data-centre load in northern Virginia disconnected from the PJM grid and switched to backup generation, requiring operators to manage the resulting changes in voltage and frequency. In September, PJM proposed new requirements for large computing loads to remain connected during certain grid disturbances.
For lenders and investors, flexibility becomes a project variable rather than a secondary engineering detail. Its value may come through faster grid connections, lower demand charges or contracted revenue from capacity and ancillary services. Those benefits must be weighed against curtailment obligations, computing-service guarantees, battery degradation, warranty restrictions and cybersecurity risks.
Private generation may accelerate projects, but a collection of self-contained energy islands could leave wider power systems with duplicated capacity and weak coordination. On-site gas generation may accelerate some projects, although certain configurations require batteries or other fast-response equipment to manage rapid changes in AI demand. They also expose projects to fuel-price, permitting and emissions risks.
Everything-to-grid offers a more integrated route. Data centres can combine on-site power and storage with grid participation, providing support during stressed periods and drawing electricity when the system has spare capacity. The IEA argues that, with suitable incentives, next-generation AI facilities could become grid assets and help relieve system pressure.
The data-centre boom is forcing digital companies to engage directly with the machinery of the power system. Generation and transmission investments will carry much of the load, but those assets usually cannot be expanded as quickly as computing capacity can be deployed. Flexibility can narrow that timing gap.
Go to top
The post Everything-to-Grid: Can Flexible Power Ease the Data-Centre Bottleneck? appeared first on International Banker.
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