The energy system is under pressure. We’re adding solar panels, charging electric cars, managing peaks, and trying to use more local energy sources – all at the same time. It’s no longer just about delivering electricity from A to B. It’s about constantly adjusting to what’s happening at thousands of points across the grid.
And that means sharing data. Lots of it. The blackout on the Iberian Peninsula wasn’t about a single component going wrong. It showed how failures often stem from missing connections between data sources, operators, and decision-makers. And it reminded us that avoiding outages starts long before anything goes wrong.
The maintenance use case: A sneak peek behind the scenes
One of the most down-to-earth examples is predictive maintenance. Not flashy. Not futuristic. Just smart, boring reliability made better with interoperable data sharing.
There are thousands of components that keep the energy system humming: transformers, switchgear, inverters, cables, meters. Each of them produces signals. Sometimes it’s just temperature. Sometimes it’s noise. Sometimes it’s a slight drop in performance.
When this data sits in silos, owned by different grid operators, equipment manufacturers, service providers, no one sees the full picture. That’s how problems get missed – and how something local can snowball into a system-wide failure.
Now let’s say that data can be shared, securely and meaningfully. Not dumped. Not emailed as a spreadsheet. But made available via a standardized, governed approach: a data space.
Suddenly, maintenance teams can see trends. That one substation that’s been heating up too fast? Flagged. The transformer whose performance has been dropping for weeks? Scheduled for inspection before it fails.
Why interoperability matters (even if you don’t see it)
This is where interoperability earns its keep. In the energy data space, it’s not enough to share data. You need to:
- Access it technically (systems can talk to each other),
- Understand it semantically (voltage means voltage, no matter who measures it),
- Agree on roles and processes (who can see what, when, and why),
- Comply with regulations (yes, GDPR still applies).
That four-part approach – technical, semantic, organizational, legal – is described in IDSA’s position paper Interoperability in the Energy Domain. The paper goes into detail on the connectors, standards, and governance mechanisms required. But the takeaway is simple: without interoperability, the data stays locked. With it, the whole system becomes smarter.
Better maintenance is good business
There’s also a strong business case. Unplanned outages are expensive. Dispatching emergency repair crews costs more than planned visits. And downtime means lost revenue for energy suppliers, grid operators, and in some cases, also customers.
When you can spot problems early, you spend less on repairs. You extend the life of expensive infrastructure. And you avoid cascading failures like the kind that turned a voltage spike in southern Spain into a blackout across two countries.
Plus, let’s not forget safety. Malfunctioning equipment puts people at risk, especially in high-voltage environments. Predictive maintenance means fewer surprises and more protection for workers on the ground.
Who benefits?
- Grid operators get fewer complaints and more stable operations.
- Service companies can offer data-based contracts and smarter maintenance planning.
- Manufacturers can improve products with feedback from real-world use.
- And everyone else – households, businesses, communities – enjoys more reliable energy at lower cost.
No single company could make this work alone. It takes standards. It takes collaboration. And yes, it takes trust. That’s what energy data spaces enable.
One example, many applications
The paper focuses on energy, but the same principles apply elsewhere: logistics, manufacturing, mobility. Any sector that relies on physical assets and complex infrastructure can benefit from sharing operational data – provided it’s done in a structured, governed, and interoperable way.
The energy use case just happens to be one of the most urgent. Because as we shift to renewables and decentralized systems, we don’t just need more electrons, we need better information.
