An answer to your grid questions

For many utilities, asset planning can feel like a sea of questions without reliable answers. If a transformer fails, can you exactly say what will happen? And when do you think that will be?

In practice, these questions aren’t so black and white. We hear “maybe,” “if,” and “well, that depends”, but, most of the time, asset managers rely on a black and white answers they might not fully trust. They’re doing the best they can with the data they have in front of them.

The asset management approaches that became the status quo over the last decade overly simplify risk modeling in a way that’s often at odds with what actually happens when assets fail. There are gaps between what planners model and what they see in the real world because the models treat assets as isolated and individual components. In the real world, assets are indeed individual components, but only within the context of an interdependent and interconnected system: the grid. To really understand asset risk, an analysis of that whole system is essential. Without it, grid planning ends up looking more like inventory management.

Consider a single wood pole. 

If the pole fails, there’s a fault in the line it’s supporting and there’s an outage. There might be a streetlight on that pole, and there might be a transformer on it. The pole could be in a rural area, and it could feed a single customer. Or the pole could be in an urban community, feeding a range of customer types. All of that factors into the consequences of that pole’s failure.

If there are millions of such poles being managed, a common approach might be to build some kind of decision tree. “If the pole is X feet tall, if it’s mounted with Y, and if it’s in region Z, then the consequence is A.” And so on and so forth until all 5 million poles are classified accordingly, relying on reasonable averages (sometimes informed by tools like the U.S. Department of Energy’s ICE Calculator) to establish the consequence of a pole’s failure. 

These averages fundamentally limit the accuracy of a model like this. How big is the sample size? How relevant is it to this context? How wrong is it?

When that pole fails, the consequences depend entirely on how the feeder is configured. If there’s a fault, a switch will open, a section of the feeder will be de-energized, and a sequence of actions is kicked off to restore power. These are engineering decisions that must consider so much more than just that pole’s attributes, and ultimately determine what the outage really looks like.

Asset managers have always known this, but have reluctantly relied on the best available tools like risk matrices to manage as best they can.

In 2020, Engineered Intelligence’s founders were grid planners working within utilities in North America. They, like most other asset managers at the time, were fed up with the old fashioned tools they had and set off to build the model the market couldn’t provide. This solution became ENGIN, and has since been deployed to utilities around the world. 

To validate the accuracy of ENGIN’s outage modeling, several utilities have compared the analytics to their Outage Management System (OMS) and the results were stunning. When compared to the utility’s OMS actuals, ENGIN outputs were more than 99% accurate.

Accurate and trustworthy asset risk analysis is vital to improve grid planning. Until now, asset managers have had to make decisions with the little data they have. Now, questions have answers and what once felt out of reach is best practice. 

Learn more about ENGIN at engineeredintelligence.com.