Oil majors posting record profits are simultaneously pouring capital into
wind and solar. The technology stack behind the green transition grows messier
every year — it stopped being just turbines and panels a long time ago. Digital
tools have become the infrastructure that renewable energy actually runs on.
Without them, even the best-built wind farm is an expensive asset nobody can
properly operate. This piece covers what's working, what's still in testing,
and where things have gone badly wrong.
What the
Market Actually Looks Like Right Now
Renewable capacity additions have been breaking records for several years
running. But raw numbers only tell part of the story — the more interesting
shift is happening at the operational level, in how these assets are managed
day to day.
Companies generating power from renewable sources deal with a specific set
of headaches that conventional generators don't: unpredictable output (a
passing cloud over a solar farm is now a line item on the risk register),
assets scattered across huge geographic areas, and the constant pressure of
real-time grid balancing. That's where digital platforms stopped being
optional. This is exactly what DXC's
renewable energy software solutions are built for (connecting asset
management, forecasting, and market integration in a single environment) which
has become the operating standard for serious players in the space. A wind farm
without that kind of digital layer is essentially a very expensive machine with
no nervous system.
Sun, Wind,
and Bytes
Where investment in renewable tech is concentrating right now comes down to
a few consistent patterns:
- Predictive analytics for
asset management. Scheduled maintenance is increasingly obsolete.
Vestas, one of the largest turbine manufacturers globally, has been
deploying IoT sensor networks that flag bearing or blade issues weeks
before an actual failure — the kind of early warning that used to require
either luck or expensive physical inspections.
- Digital twins. Siemens Energy, working
with Microsoft Azure Digital Twins, has been testing full digital modeling
of offshore wind farms. When physical access to a turbine means chartering
a boat and waiting for a weather window, running simulations on a virtual
copy changes the economics considerably.
- AI-based generation
forecasting.
Google DeepMind and Google Energy announced results from their wind
forecasting work that showed machine learning meaningfully outperforming
standard meteorological models — allowing better delivery scheduling and
more value from the energy produced.
- Distributed energy
resources (DER).
A network of home solar panels, Tesla Powerwall units, and EV chargers
managed as a single virtual asset is no longer a research concept. It's
being piloted commercially in multiple markets.
Prototypes
Worth Watching
AutoGrid — now part of Enel — built a platform called Flex that aggregates
thousands of small distributed sources, from commercial refrigeration systems
to rooftop solar installations, and treats them as one flexible grid resource.
The idea of turning passive consumers into active market participants is
actually working in the field.
Ørsted and ABB have been running trials with autonomous underwater drones
for cable inspection on offshore wind assets. Instead of expensive dive
operations, inspection data gets uploaded directly into the asset management
system and cross-referenced with surface sensor readings. It sounds simple, but
reducing the cost and scheduling complexity of offshore inspection is genuinely
hard.
AI and
Analytics — Separating the Real From the Noise
Artificial intelligence in energy is easy to oversell. Strip away the vendor
presentations and the picture becomes more concrete and more modest.
Forecasting
and Balancing
The core problem with renewable generation is weather dependency. Solar
produces nothing at night; wind turbines stop when the air is still. For grids
where renewables make up a substantial share of capacity, this creates real
balancing headaches. The traditional fix — keeping gas peakers on standby — is
expensive and partially undermines the emissions case for renewables in the
first place.
Better forecasting reduces how much reserve capacity a grid needs to hold.
Engie's Metronome.Energy platform, for instance, produces wind and solar
generation forecasts down to 15-minute intervals. The improvement over
conventional weather modeling has been meaningful enough to affect how
operators plan their market positions — though exact gains vary widely by site
and conditions.
Pricing and
Market Participation
Spot market electricity prices swing from near-zero (or genuinely negative,
which happens regularly in Germany on windy weekends when supply floods the
grid) to several hundred euros per megawatt-hour during demand spikes.
Algorithms that optimize battery charge-discharge cycles against those price
curves are now commercial products, not research projects. Stem Inc.'s Athena
platform uses reinforcement learning to manage industrial storage assets, and
the approach of continuously adjusting strategy based on real-time price
signals has become a standard pitch in the battery storage space. Companies
like Fluence and Powin are operating in the same territory.
Digital
Twins: When a Wind Turbine Has Its Own Avatar
The digital twin concept has found a natural home in offshore energy because
the economics are so stark. An offshore turbine sitting 50 kilometers out
requires a vessel, a weather window, and hours of transit just to get eyes on
it. A synchronized 3D model fed by hundreds of real-time sensors — tracking
load, thermal stress, vibration — changes what "maintenance planning"
means entirely.
NVIDIA Omniverse has several industrial energy demonstrations, though most
remain at proof-of-concept stage. Bentley Systems' iTwin Platform is further
along, with deployments at actual offshore wind operators in the North Sea. The
gap between demonstration and production deployment is still significant.
What digital twins are being used for in practice:
- running simulated operating modes without risk to the
actual asset
- condition-based maintenance scheduling instead of
calendar-based
- training field technicians in environments that mirror
the real thing
- post-incident analysis and emergency planning
- siting optimization before a single foundation is
poured
When It
Goes Wrong: Real Failures Worth Learning From
The industry's promotional materials don't spend much time on the cases
where digital integration created new problems instead of solving them.
The Viasat
Attack and Enercon's Blind Turbines
In February 2022, hours before Russia launched its full-scale invasion of
Ukraine, a cyberattack knocked out Viasat's KA-SAT satellite network. The
intended target was Ukrainian military communications. The collateral damage
spread across Europe: roughly 5,800 Enercon wind turbines in Germany lost their
remote monitoring connection. The turbines kept spinning — they failed safe —
but operators lost visibility and control for weeks. Enercon had to dispatch
technicians physically to sites to restore connectivity. The episode made it
clear that a satellite link used for turbine telemetry is part of the attack
surface of a renewable energy fleet.
Texas,
February 2021
The ERCOT grid failure during Winter Storm Uri is usually framed as a story
about frozen gas infrastructure. That's accurate, but the digital management
layer contributed its own problems. The grid's forecasting systems had not
modeled an extreme cold event of that scale. When generation dropped suddenly
and demand spiked, the automated frequency response systems responded in ways
that accelerated rather than dampened the crisis. Over 200 people died. The
event became a case study in what happens when grid management software runs on
assumptions that reality eventually violates.
Offshore
Wind Maintenance Backlogs
Several major operators discovered that deploying predictive maintenance
software without rebuilding their logistics infrastructure just moved the
bottleneck. Data systems flagged components that needed replacement weeks in
advance. Vessels to reach offshore turbines weren't available on the predicted
schedule. Spare parts sat in port while faults that had been predicted became
actual failures anyway. The lesson — that digital tools only work if the
physical supply chain can respond — sounds obvious in retrospect.
Incompatible
Stacks
When Portuguese utility EDP expanded its renewable portfolio through
acquisitions, it ended up with wind assets running on Vestas proprietary
systems, solar assets on a different vendor's stack, and storage systems with
their own management layer. None of them talked to each other natively. The
cost and complexity of integration exceeded initial estimates substantially.
This pattern has repeated across the industry — GE Vernova, Siemens Gamesa, and
Goldwind all use different telemetry formats, and the IEC 61400-25 standard
that was supposed to fix this has uneven real-world adoption.
Cybersecurity:
The Attack Surface Nobody Planned For
Digitizing the grid creates exposure that didn't exist when turbines were
managed by local SCADA boxes with no internet connection. The Colonial Pipeline
ransomware attack in 2021 cost the company around $4.4 million in ransom and
caused fuel shortages across the US East Coast — and that was an oil pipeline,
a simpler target than a distributed wind fleet.
Approaches that have become standard for operators who take this seriously:
- Zero Trust Architecture applied to industrial
control environments, not just corporate IT
- OT/IT network
segmentation
— keeping operational technology isolated from the corporate network even
when that creates management friction
- Protocol-specific threat
monitoring
for industrial communications like Modbus, DNP3, and IEC 61850
- Red team exercises designed around SCADA
attack scenarios, not just IT penetration testing
NERC CIP standards in the US and the NIS2 Directive in the EU set minimum
requirements for critical infrastructure operators. The compliance floor is
rising, and insurers are starting to ask harder questions before underwriting
generation assets.
Where This
Is All Going
A few directions look durable enough to be worth taking seriously.
Platform consolidation. The fragmentation of point
solutions — separate tools for forecasting, asset management, market trading,
and maintenance — is already compressing. What happened in cloud computing in
the early 2010s, when dozens of providers consolidated around a handful of
dominant platforms, is playing out in renewable energy software. DXC, alongside
competitors Accenture, Capgemini, and IBM, are all building or acquiring toward
integrated energy management platforms rather than selling individual modules.
Autonomous operations. The step after predictive analytics
is systems that don't just recommend actions but execute them — deciding when
to charge a battery, when to curtail a turbine, when to bid into a balancing
market. Operators are cautious, reasonably, about handing that authority to
algorithms. But the economics pressure toward it is real.
Vehicle-to-Grid at scale. V2G — EVs returning power to the
grid during peak demand — needs coordinating infrastructure that doesn't fully
exist yet. Nissan's Leaf, Volkswagen's ID series, and Renault's
bidirectional-capable vehicles have demonstrated the technical side. The regulatory
and commercial models are still being negotiated market by market.
Edge computing for critical decisions. Not everything
should route through the cloud. Latency matters for protection systems, and
cloud dependency creates single points of failure. Vendors like Dell EMC, HPE,
and Beckhoff Automation are all developing edge processing capacity designed
specifically for substation and turbine environments. The boundary between what
lives at the edge and what lives in the cloud is one of the more active design
questions in grid modernization right now.
Software is becoming the competitive differentiator in renewable energy —
not in the sense that hardware doesn't matter, but in the sense that two
identical wind farms, operated differently, will have meaningfully different
economics. The digital layer is where that difference gets made. Operators who
treat it as an IT procurement decision rather than a core operational
capability are already falling behind.
Digital
Steps, Irreversible Direction
The green energy transition always needed physical infrastructure —
turbines, panels, cables, substations. What wasn't obvious a decade ago is how
much competitive advantage would end up sitting in software. Forecasting, asset
management, cybersecurity, market participation: these aren't support functions
anymore. Vendors like DXC, Schneider Electric, and Honeywell are competing hard
for that layer — and the operators treating it as a core capability, not an IT
budget line, are the ones pulling ahead.
