Grid Data Fabric Use Cases for OT

Grid data fabrics. We’ve already covered what they are and how they work. Now let’s talk about what they can do for you.

As discussed previously, a grid data fabric works by serving as a foundation from which disparate, siloed data sources of many types, volumes, formats, and metadata can be bridged and gathered. With a grid data fabric’s federated approach to data management and curation of unified business metadata, utilities will find it much faster and easier to unlock the full power of grid orchestration.

Let’s look at some grid orchestration use cases that a grid data fabric can unlock:

For proper scenario planning, utilities must satisfy two requirements. First, they need to bring together real-time grid OT data with sensor, environment, and enterprise IT data – each of which typically resides within a silo. A data fabric is needed to break through any silos and bring together the disparate data sources.

Second, utilities must also be able to snapshot or archive their real-time grid and environment data. To understand the importance of this, consider that AI- and ML-powered grid applications add value by helping users make predictions about the future. The AI and ML engines guide those predictions by bringing together data on past events, and the conditions and factors that made them happen. Thus, any utility that wants to leverage AI and ML must have an archive of historical “training” data from which the applications can learn.

A grid data fabric can manage internal and external data, thus providing that source of training data from which AI and ML can learn.

The data fabric can also help with other use cases like scenario analyses and grid simulations – both of which require an understanding of how the grid behaves in response to certain conditions.

Utilities with situational awareness of Distributed Energy Resources can use load forecasts and grid OT data to enable look-ahead analysis. Look-ahead analysis is incredibly valuable to modern utilities with high DER penetration, as it allows them to anticipate future grid conditions and behaviors.

For example, on very sunny days, utilities with DER-heavy grids are at risk of backfeed violations, caused by abnormally high voltage levels due to excess generation. Look-ahead analysis enables grid operators to predict potential violations and drive future optimization decisions to avoid them. With access to the right data, look-ahead analysis engines can determine the most appropriate optimization measures. The engines can even take into account operational and economic data plus DER program considerations to ensure feasibility and minimize the total cost of operation. With this techno-economic optimization, utilities can schedule DER dispatch decisions in advance, preventing future violations and taking full advantage of DER flexibility.

Look-ahead analysis use cases like the above can only be realized by integrating DER asset and program data, forecasting data, grid OT data, economic and market data, settlement data, and external data such as weather. This is typically a huge undertaking, as each of those information types reside in silos that complicate accessing and utilizing them. But a grid data fabric can seamlessly connect and provide access to all these data sources, making it exponentially easier to utilize them for look-ahead analysis.

Power Transmission & Distribution have traditionally worked in relative isolation, with little collaboration or communication with one another. That’s changing as the energy transition progresses. Orchestrating the sustainable energy grid of the future requires gaining a systemwide view and coordinated operation across Transmission & Distribution and all the way to the edge to manage the multidirectional flow of energy. Only then can utilities unlock advanced, real-time Transmission & Distribution use cases like:

• Outage response and restoration
• Emergency response (e.g. load shed and distributed black start)
• Coordinated Volt/VAR optimization
• Planned outage scheduling
• Operational situational awareness
• Integrated switching

To do so, utilities need to bridge data silos and connect disparate applications and systems like Energy Management System, Advanced Distribution Management System, and Distributed Energy Resource Management System. And the quickest, easiest, most effective way to do that is with a grid data fabric.

For more information on how the GridOS® Data Fabric can help you unlock grid orchestration, download our whitepaper on the topic.