Power & Sites · United States

SK Eternix, Mastern to Develop Fuel Cell-Powered Data Center

Credited to TheElec · thelec.net · 6m ago

SK Eternix and Mastern Investment Management will develop a fuel cell-linked data center. The companies will flesh out the business model and assess its technical and economic feasibility.

SK Eternix said Oct. 8 it signed a memorandum of understanding (MOU) with alternative investment-focused asset manager Mastern Investment Management on Oct. 7.

The MOU was signed to develop a hyperscale artificial intelligence (AI) data center powered by fuel-cell generation facilities.

The companies will jointly pursue an independent, or off-grid, data center model in which fuel-cell generation facilities installed on the site directly supply electricity to the data center.

Fuel cells can be installed at high density even on limited sites. Their relatively short construction periods can reduce delays in securing power caused by the expansion of large-scale transmission networks. They have recently drawn attention as a power source for data centers, where electricity demand is surging with the spread of AI.

SK Eternix will oversee the supply of fuel-cell power. It will handle the entire process, from the engineering, procurement and construction (EPC) of generation facilities to establishing the power-supply structure and managing operations and maintenance.

Mastern Investment Management will oversee the overall development project. Its responsibilities will include securing data center sites and permits, reviewing development scale, design, establishing the investment structure, raising funds and attracting tenants.

Power & SitesUnited States

Hyosung Heavy Industries Builds 2-GW HVDC Test System

Credited to TheElec · thelec.net6m ago

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Hyosung Heavy Industries has built a 2-gigawatt (GW) high-voltage direct current (HVDC) performance testing system using its own technology. The company’s official English name is Hyosung Heavy Industries.

Hyosung Heavy Industries said Oct. 8 that it has completed a hardware-in-the-loop simulation (HILS) system to verify the performance and stability of its in-house developed HVDC controller.

HVDC is a technology that converts electricity into direct current for long-distance transmission. It is suited to transmitting large amounts of power over long distances and enables precise control of power flows. An HVDC controller regulates the operation of converter valves that convert power between alternating current and direct current.

As HVDC transmission capacity increases, the systems and operating conditions that need to be controlled become more complex, increasing the number of scenarios that must be verified. A 2-GW system has two to three times as many operating scenarios to test as an existing 200-megawatt (MW) system, making extensive validation necessary before deployment on the power grid.

HILS is a technology that connects physical hardware to a virtual system to verify its performance. It allows users to simulate desired operating environments and conduct various tests indoors. Hyosung Heavy Industries connected an actual HVDC controller to a virtual 2-GW converter valve and power grid. It repeatedly tested faults and emergency conditions that would be difficult to reproduce on an actual power grid.

Hyosung Heavy Industries secured technology covering the entire HILS development process, including system design, installation, testing and operation. The company applied its experience operating and controlling a 200-MW voltage-source HVDC system installed at Yangju Substation in 2024 to the design of the 2-GW test environment.

A Hyosung Heavy Industries official said the HILS system is significant because it provides the company with an independent technological foundation for advancing its HVDC controllers.

Original · TheElec

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Power & Sites · United States

Can we build AI infrastructure faster than we can staff it?

Credited to Data Centre Review · datacentrereview.com · 22m ago

As data centres become denser and more technically complex, Maksim Gavriliuk, Independent Digital Infrastructure and Data Centre Expert, explains why workforce planning needs to start long before handover.

A data centre can be designed in months and built in a few years. Developing an experienced operations engineer takes much longer. That difference is becoming harder to ignore as AI infrastructure expands. Most capacity discussions still revolve around megawatts, rack density and access to compute. Staffing tends to come later, even though experienced technical people are much harder to add quickly.

The shortage is measurable, and it is getting worse

Uptime Institute’s 2026 Global Data Center Survey collected responses from more than 800 data centre owners and operators. It found that more than half were having difficulty finding qualified candidates for open positions, and Uptime has warned that recruitment and retention pressures are continuing rather than easing.

Retention is part of the same problem. Uptime reports that staff turnover remains a persistent challenge, with employees often lured away by competing data centre companies. As new AI facilities come online, operators are increasingly competing for the same experienced people, as well as for power, equipment and suitable sites.

Technical roles have consistently been among the hardest to fill. Uptime’s workforce research has repeatedly identified operations, mechanical and electrical roles as particularly difficult to staff, while also highlighting growing demand for controls and monitoring specialists. Those are precisely the people who sit closest to the day-to-day operation of increasingly dense infrastructure. General technical experience helps, but it does not remove the need for people who understand how these environments behave in practice.

There is also a demographic issue. Uptime has warned for several years that mature data centre markets in the US and Western Europe face significant retirements among experienced technical staff, with particular pressure on senior operational roles.

The loss is difficult to measure simply in headcount. Experienced operators carry knowledge that rarely appears completely in procedures: how equipment normally sounds, which alarms are genuinely unusual, when a reading deserves escalation or how a system behaves after years of operation. Some of this can be documented. Some of it only develops through experience.

Uptime previously estimated that global data centre staffing requirements would increase from around two million full-time equivalents in 2019 to nearly 2.3 million by 2025. High-density and AI-related developments are now adding to demand in a market that was already struggling to recruit.

So the industry is trying to expand its workforce while some of its most experienced people are approaching retirement. Replacing the numbers alone will not replace their experience.

AI facilities need different skills

The problem is not solved simply by recruiting more people into existing roles because the work itself is changing.

Uptime’s 2026 survey found that modal rack densities are continuing to rise, with a growing number of operators reporting peak racks of 30 kW or above. AI training deployments can run well beyond that, and rack-scale architectures at the top end of the market go considerably higher again. In that part of the market, liquid cooling is becoming a core infrastructure consideration rather than a specialist exception.

That changes the operational workload. Teams may need to deal with coolant quality, leak response, cooling distribution units and equipment that connects the IT environment much more directly to the facility cooling system.

Electrical operations are changing as well. AI clusters introduce concentrated loads and operating conditions that are different from those found in many traditional enterprise data halls. At the same time, the distinction between IT and facilities responsibilities becomes less clear when a cooling issue on the facility side can immediately affect high-value compute equipment.

This is one area where I think training often lags behind design. Organisations can install new technology faster than their operating model adapts to it. A team can therefore be fully staffed on paper and still lack some of the skills required by the infrastructure it has been asked to run.

Training has to start before handover

Recruitment alone will not close this gap. Operators also need a better way to develop people already inside the industry and to bring in technicians from adjacent sectors.

Structured training is part of that. So are visible career paths. A junior technician is more likely to stay if there is a credible route into senior operations, engineering or management, rather than an expectation that shift work remains essentially the same for years.

Operational documentation matters for the same reason. Clear procedures, defined competencies and structured assessment reduce the amount of knowledge that depends on informal handover between experienced staff and new recruits.

Reviewing a data centre operations standard this year has made the same point clear to me. A significant part of the skills problem is also a knowledge-transfer problem. Organisations may have experienced people, but that experience has limited value to the next generation if it remains largely in individuals’ heads.

I have seen a similar issue in markets where data centre construction has moved faster than the development of local operations teams. The facility can be completed on schedule while the people expected to run it are still building confidence with unfamiliar systems. Closing that gap takes structured knowledge transfer, clear responsibility and time. Hiring more people at the end of the project does not solve it by itself.

Staffing belongs in capacity planning

Data centre developers already plan years ahead for power, land and long-lead equipment. Staffing should enter the planning process much earlier than it often does today.

The question at the design stage should not only be whether enough power and cooling will be available when the facility opens. Operators should also know what skills the facility will require, where those people will come from and how long they will need to become competent in the systems they will operate.

That becomes especially important with AI infrastructure because technical change is occurring faster than the industry can rely on experienced hires alone.

A data centre may be physically ready before its operating team is. At that point, the missing capacity is no longer electrical or mechanical. It is operational.

Planning for people earlier will not remove the skills shortage, but it reduces the risk of discovering it only after the infrastructure has already been built.