How Engineers Are Suddenly Using Common Rust to Power Entire Cities This Week

The electric grid is quietly undergoing a structural overhaul this week, driven by two entirely different interpretations of a single word: rust.

In Pine Island, Minnesota, utility giant Xcel Energy and Google just finalized a definitive agreement to deploy a massive 300-megawatt, 30-gigawatt-hour battery system. It is set to become the largest battery installation by energy capacity ever announced globally. Almost simultaneously, Crusoe, an artificial intelligence data center operator, executed a 12-gigawatt-hour capacity agreement to secure power for its rapidly expanding server farms starting in 2027. Further across the Atlantic, FuturEnergy Ireland announced a 1,000-megawatt-hour facility designed to stabilize the country's highly variable wind generation.

None of these facilities rely on the lithium-ion cells that currently dominate the energy storage market. Instead, they are being built with iron-air batteries manufactured by Form Energy. These systems store electricity by intentionally rusting iron, then reversing the chemical process to release the power back into the grid.

However, deploying gigawatt-hours of experimental hardware across hundreds of miles of transmission lines introduces extreme operational complexity. Physical batteries alone cannot balance real-time fluctuations between thousands of wind turbines, massive data center loads, and regional transmission organizations. The orchestration requires software operating with absolute deterministic precision. To achieve this, infrastructure engineers have turned to the rust programming language, stripping away older, less efficient software frameworks to build ultra-secure, low-latency energy management systems that dictate exactly when these colossal iron batteries charge and discharge.

The convergence of physical iron oxidation and highly efficient code marks a distinct maturation point in the clean energy transition. This is the breakdown of how engineered rust—both the chemical reaction and the software ecosystem—is solving the most stubborn bottlenecks in global power delivery.

The Lithium-Ion Ceiling

To understand why utilities are suddenly buying massive volumes of iron-air batteries, you have to look at the fundamental limitations of the batteries we already have.

Lithium-ion technology, specifically lithium iron phosphate (LFP) and nickel manganese cobalt (NMC) chemistries, dictates the modern energy landscape. These cells are highly energy-dense, meaning they can store a large amount of power in a relatively small physical footprint. This makes them ideal for electric vehicles and smartphones, where weight and size are primary constraints.

On the electrical grid, however, weight and size matter significantly less than cost and duration. Over the last five years, utilities have deployed tens of gigawatts of lithium-ion batteries to capture excess daytime solar power and discharge it during the evening demand spike. These lithium systems excel at providing power for exactly four hours. Pushing a lithium-ion facility to provide eight, twelve, or twenty-four hours of continuous discharge requires buying and stacking exponentially more batteries, driving the capital expenditure to prohibitive levels.

Furthermore, lithium-ion cells suffer from degradation based on their state of charge. Holding a standard lithium battery at a 100% or 0% state of charge for extended periods accelerates the breakdown of its internal chemistry. They require constant cycling to remain healthy.

This presents a serious vulnerability for decarbonized grids. Energy planners operate in fear of the dunkelflaute—a German term heavily adopted by the energy sector referring to a multi-day period with little to no wind and heavy cloud cover. During a severe winter storm, solar panels are covered in snow and wind turbines are entirely still. A four-hour lithium-ion battery is functionally useless if the grid needs continuous backup power for 100 consecutive hours. Historically, the only way to cover a multi-day shortfall has been to fire up natural gas peaker plants.

Reversible Oxidation: The Chemistry of Iron-Air

Form Energy was founded in 2017 by energy storage veterans, including Mateo Jaramillo, a former lead engineer at Tesla Energy. The company's mandate was to build a multi-day energy storage system utilizing the absolute cheapest materials on earth. They bypassed lithium, cobalt, and nickel entirely, settling on iron, water, and air.

The science driving these new deployments relies on reversible oxidation. A single battery module is roughly the size of a side-by-side washer and dryer set. Inside the module sits a stack of individual cells containing thousands of small iron pellets suspended in a water-based, non-flammable alkaline electrolyte.

The operation mechanics are straightforward but highly effective. When the battery discharges to supply electricity to the grid, the system "breathes" in oxygen from the ambient air. This oxygen reacts with the iron pellets in the electrolyte, converting the iron metal into iron oxide—common rust. This chemical reaction releases electrons, generating an electrical current.

When excess renewable energy is abundant and cheap, the grid sends power back into the battery. The application of this electrical current reverses the chemical process. The rust drops its oxygen, reverting back to metallic iron, and the battery "exhales" the oxygen back into the atmosphere.

Unlike lithium-ion, iron-air batteries thrive in deep discharge scenarios. An iron-air system can safely drop to a 0% state of charge—becoming entirely rusted—without suffering structural degradation. Because the active materials are wildly abundant, the installed energy cost is a fraction of lithium-ion. Form Energy claims its systems cost less than $20 per kilowatt-hour, effectively one-tenth the cost of heavy-metal alternatives.

Manufacturing in the American Rust Belt

The geographic epicenter of this hardware rollout carries heavy industrial irony. Form Energy's primary manufacturing facility, dubbed Form Factory 1, is located in Weirton, West Virginia. The 550,000-square-foot plant was constructed on the historic site of a former Weirton Steel mill, deliberately placing next-generation battery manufacturing directly in the center of the American Rust Belt.

The facility is currently operating with a workforce of nearly 400 people, but the timeline for expansion has been violently accelerated by the artificial intelligence sector. Initially, Form Energy designed the Weirton factory to ramp up to an annual production capacity of 500 megawatts by the end of the decade. However, the immediate surge in power demand from AI data centers has forced a total recalculation.

Data centers represent the fastest-growing source of electrical demand in the United States. In the Electric Reliability Council of Texas (ERCOT) grid alone, consumption between early 2025 and late 2025 jumped drastically, driven largely by new compute infrastructure. Tech conglomerates attempting to maintain 24/7 carbon-free energy pledges are finding that the local grid simply cannot supply uninterrupted clean power for gigawatt-scale server farms.

This dynamic spawned the Crusoe agreement announced in March 2026. Crusoe utilizes a "Bring Your Own Capacity" model, where it finances and secures its own localized power generation and storage to avoid waiting in a utility's ten-year interconnection queue. Their 12-gigawatt-hour purchase order for iron-air batteries ensures that even when the wind stops blowing, their AI training clusters will not lose power.

Facing an order book that vastly exceeds their initial projections, Form Energy is currently executing a "densification process" at the Weirton plant, re-engineering the factory floor to squeeze more megawatt output per square foot than originally drafted. The modules rolling off the line are being shipped directly to the Great River Energy pilot site in Cambridge, Minnesota, which is serving as the first true commercial proving ground for the technology.

The Invisible Orchestrator: Grid Software

While thousands of shipping-container-sized iron batteries physically anchor the grid, the intelligence layer controlling them presents an entirely separate engineering hurdle. A modern electrical grid is not a static entity; it is a hyper-complex, highly volatile internet of things (IoT) network.

In a traditional grid, power flows in one direction: from a centralized coal or nuclear plant to the consumer. In a highly decarbonized grid, power flows in all directions simultaneously. Solar panels on residential roofs inject power back into the system, electric vehicles draw variable loads, wind farms spike and drop based on weather fronts, and massive battery parks sit waiting to absorb or deploy charge.

Balancing this real-time chaos requires Energy Management Systems (EMS) that ingest millions of data points per second. The software must monitor the frequency of the grid, evaluate the real-time pricing of wholesale electricity, assess local weather forecasts to predict solar output, and calculate exactly when to trigger the chemical rusting process inside a battery module. If the software lags by a few hundred milliseconds, a frequency dip could trigger cascading transformer failures resulting in a blackout.

Historically, control software in the energy sector relied heavily on older legacy codebases or dynamic languages like Java and Python. These languages are highly accessible and allow for rapid development, but they come with severe structural penalties in high-stakes environments. Python, for example, is interpreted at runtime and consumes significant processing power just to execute basic commands. Java relies on a mechanism called "garbage collection" to manage memory; the software periodically pauses its execution to clean up unused data.

In a web application, a 50-millisecond garbage collection pause is unnoticeable. In a grid dispatch system attempting to stabilize a failing frequency wave, a 50-millisecond pause is catastrophic.

This brings us to the second definition of rust. To solve the latency, safety, and energy efficiency problems inherent in legacy software, grid operators, battery integrators, and system developers are increasingly writing critical infrastructure utilizing the rust programming language.

Originally incubated by Mozilla and later supported by a foundation backed by Amazon Web Services, Google, and Microsoft, the language was built specifically for systems programming. It provides the bare-metal speed and raw performance of C or C++, but it enforces strict memory safety rules at the compiler level.

In C and C++, developers have manual control over memory allocation. This freedom routinely leads to memory leaks, buffer overflows, and undefined behavior—vulnerabilities that account for roughly 70 percent of all high-severity security flaws in major software systems. For national critical infrastructure like the power grid, a buffer overflow is not just a software bug; it is an open door for state-sponsored cyberattacks.

The rust programming language eliminates these vulnerabilities through a unique concept called ownership and borrowing. The compiler tracks exactly where data lives in memory and automatically drops it the exact microsecond it is no longer needed, completely negating the need for a garbage collector. The result is software that never pauses, runs with absolute predictability, and operates with an iron-clad defense against memory-based cyber intrusions.

The Energy Efficiency of Code

Beyond security and latency, there is a massive economic and physical incentive driving this software transition: power consumption.

Running massive server clusters to calculate Stochastic Dual Dynamic Programming (SDDP) algorithms—the complex math used to determine the optimal dispatch schedule for hydrothermal and battery resources—requires immense amounts of electricity. When dealing with a 300-megawatt, 100-hour battery system like the one Xcel Energy is building in Minnesota, the dispatch optimization models must simulate thousands of potential future weather scenarios simultaneously.

A comprehensive study analyzed the energy efficiency of 27 different programming languages by running ten identical benchmark problems. The findings were stark. Compiled languages like C and the rust programming language proved to be roughly 50% more energy-efficient than Java, and up to 98% more efficient than Python.

Dynamic languages require excessive CPU cycles for runtime type checking and memory management. Because the rust programming language utilizes zero-cost abstractions and strict compile-time checks, the resulting binary file executes only the exact machine instructions required. By transitioning backend processing services to this ecosystem, energy integrators can drastically shrink the footprint of the hardware required to run the grid itself. Fewer servers running cooler processors directly translates to lower operational costs and a reduced carbon footprint for the control layer.

This efficiency becomes even more critical at the edge of the grid. Inside the battery enclosures at the Great River Energy pilot site, microcontrollers manage the valves, track electrolyte levels, and monitor leak detection sensors. These embedded systems are severely power-constrained. Running a heavyweight operating system on edge nodes generates heat and draws parasitic load from the battery. Deploying firmware written in the rust programming language allows engineers to squeeze maximum performance out of cheap, low-power microcontrollers while guaranteeing the system will not crash due to memory corruption.

Bridging Software and Geopolitics

The pivot toward these specific physical and digital architectures is heavily insulated by global geopolitical strategies.

The lithium-ion supply chain is overwhelmingly dominated by China. Massive entities like CATL and BYD control the global market for battery cells, refining, and precursor materials. The United States and Europe have spent years attempting to build domestic lithium supply chains, but the capital requirements and permitting hurdles for new mining operations make catching up exceedingly difficult.

Iron-air technology allows Western utilities to bypass the lithium bottleneck entirely. The materials required—iron, water, and air—are globally ubiquitous. Form Energy's systems can be sourced, manufactured, and deployed entirely within North America. The Xcel Energy project, the Crusoe AI data center deployments, and the Dominion Energy sites slated for 2026 represent a deliberate decoupling from foreign supply chains.

Similarly, the transition to open-source, highly secure software frameworks is viewed as a necessary defensive posture. As the grid becomes increasingly digitized and reliant on third-party integrations, the attack surface expands. Developing control systems in a language mathematically proven to resist memory exploits is no longer just a best practice; it is becoming a baseline requirement for securing critical grid assets against international intrusion.

Unresolved Engineering Challenges

Despite the massive capital influx and signed capacity agreements, the large-scale deployment of iron-air batteries and their associated control networks faces several immediate technical hurdles.

First, the chemistry itself has vulnerabilities. The iron-air cells utilize an alkaline electrolyte. In alkaline environments, carbon dioxide from the ambient air can easily dissolve into the liquid, forming carbonates. Over time, carbonate buildup can degrade the efficiency of the cell. Engineers must deploy specialized scrubbing mechanisms or filtration systems to ensure the air "breathed" by the battery does not slowly poison the electrolyte over thousands of cycles.

Second, the structural integrity of the ceramic separators inside the cells remains a long-term variable. While Form Energy conducts rigorous multi-fault scenario testing and maintains strict secondary containment protocols for the electrolyte, the physical stress of reversing the oxidation process continuously over a projected 20-year lifespan is difficult to perfectly simulate in a lab. The pilot program at Great River Energy will serve as the primary stress test for these components operating under harsh real-world conditions, including extreme Minnesota winter temperature fluctuations.

On the software side, the barrier to entry remains the steep learning curve of the architecture itself. Writing energy dispatch algorithms or inverter control logic in the rust programming language requires engineers to explicitly manage lifetime annotations and satisfy the compiler's strict borrow checker. The energy sector has traditionally relied on rapid prototyping in Python to test new market bidding strategies or weather models. Forcing grid operators to adopt highly disciplined, lower-level systems programming requires a cultural shift in how utility software is developed.

The Trajectory Through 2030

The timeline over the next 48 months is distinctly laid out. Throughout the remainder of 2026, the focus remains firmly on commissioning the first wave of systems. The Great River Energy site will reach full commercial operation, delivering the first empirical data on round-trip efficiency and response times at a megawatt scale.

By 2027 and 2028, the massive projects will come online. Google's 30-gigawatt-hour facility with Xcel Energy will begin providing firm, 24/7 power. Crusoe's 12-gigawatt-hour deployment will activate, providing a blueprint for how AI companies can independently sustain their power requirements without relying on fossil fuel baseload. FuturEnergy Ireland will connect its 1,000-megawatt-hour facility to the EirGrid, testing whether long-duration storage can alleviate the costly curtailment of the country's offshore wind farms.

Simultaneously, we will see the continued rollout of sophisticated EMS platforms natively executing on next-generation software architecture. Companies like FlexGen, which recently acquired the battery commissioning firm CES to expand its HybridOS power plant controls, will face increasing pressure to manage these 100-hour discharge cycles with zero margin for computational error.

The grid is entering a phase of heavy industrial modernization. The transition relies entirely on exploiting the fundamental properties of rust—utilizing the cheapest metal on earth to store power across multiple days, and deploying a rigorously secure programming ecosystem to control it all. How well these physical and digital systems integrate over the coming year will largely dictate whether the grid can sustain the simultaneous pressures of heavy decarbonization and unprecedented digital demand.