Editorial Note: This is the last in a series of three articles by Brandon Raso about the impact that location intelligence is having across the operations of electrical utilities. Location intelligence uses next-generation GIS technology and analytics to deliver actionable insights that utilities could not previously access.

In my prior article, I discussed how Advanced Grid Management enables utilities to do real-time optimization that was not possible before. It enables organizations to not only see what’s truly happening now but also anticipate what might happen next. The latter of those two – predictive analytics – is what I want to focus on in this final article about the impact of location intelligence on utilities. Location intelligence is at the heart of a utility’s ability to create ultra-detailed, accurate digital models of their infrastructure and operations. These digital models – which go by many names, including “digital twins” – enable utilities to do something that they have always wished was possible: answer the question “What if…?”

What if a storm surge knocks out transmission to this sector of the grid?

What if there is a 100% increase in the number of EVs among our customer base over the next two years?

What if lightning strikes start a fire near these circuits?

What if we place the gas main here versus another location farther from the high occupancy areas?

What if there is a one-year break in the drought and vegetation grows quickly because of above-normal precipitation?

The predictive analytics that are possible with digital models driven by location intelligence give utilities the tools to answer these questions not simply with guesses but with data-driven insights that have never been available before. These what-if insights enable smarter, faster decision-making at every level of the organization, from the executive leadership team to operations to the GIS department to field workers.

Climate change is one of the key challenges that this kind of predictive analytics will help utilities respond to. The impact of climate change on utilities has a multiplicity of layers and variables to it, which makes risk analysis incredibly difficult to use traditional processes without the help of digital models. As an example, let’s look at the types of variables that are at play for electrical utilities in the Pacific Northwest, where I live. Fire dangers have increased in general because of hotter, drier summers, but there are also other complicating factors such as die-offs of Douglass Firs driven by climate change that increase fire danger in certain areas. Ice storms are also becoming more common in the Northwest, where heavy ice can wreak havoc on trees as well as transmission lines. Luckily, hurricanes are not a concern in the PNW, but storms in recent years have had hurricane-force winds that utilities should factor into their scenario planning and emergency response. Low water levels in rivers and lakes that would ordinarily be used to fight wildfires, may impact the ability to control fires in certain areas as well. And the cycle of wildfires during the summer followed by heavy rain during the winter creates a high risk of landslides that can impact infrastructure and access during critical situations when they are most needed. And major cities like Seattle and Portland that are ill-equipped for the removal of ice and snow are difficult environments for utilities to plan emergency response during storms.

As you can see, adapting operations for the impact is about much more than just preparing for wildfires. These are multi-layered challenges no matter the geographic region, and utilities have never before had modeling robust enough to play out the scenarios before emergencies as well as in real-time during emergencies. Right now, utilities have limited tabletop-style scenario planning based on past events that they then augment with human reconnaissance by rolling trucks during emergencies. In these situations, field workers serve as invaluable eyes and ears who gather information that can then be used to adapt plans to facts on the ground. Predictive modeling represents a quantum leap forward. When utilities get to the Advanced Grid Management phase of their digital transformations, the digital models of their infrastructure and operations can be continuously bolstered with location-based information from mobile devices including field workers’ mobile tablets, geospatial imagery, and the growing number of IoT sensors that are deployed on poles, water pumps, equipment junctions, gas lines, transformers, remote equipment huts and so much more. Utilities are also beginning to use other technologies such as drones, laser-based scanning devices and augmented reality headsets. These devices create richer and richer data sets that enable enhanced risk modeling and asset management over time.

This enhanced modeling is critically important for all utilities, but particularly for electrical utilities because their models for generation, transmission and distribution are being transformed by renewables, distributed energy assets and microgrids. One of the biggest emergency-planning takeaways for electrical utilities from Hurricane Ian in 2022 was the need to prevent widespread outages far from an area directly impacted by a storm. Florida experienced prolonged outages across an enormous geographic area that was untouched by the storm itself. A noteworthy exception to that was a community near Fort Myers whose power was managed through a microgrid that shielded it from the outages that affected so much of the rest of the region. Microgrids that utilize DERs for generation and batteries for distributed power storage will be a vital tool for building more resilience into the grid. What-if modeling will be indispensable for the complex risk modeling that utilities will need to do to determine where to deploy microgrids and how to manage them as part of a far more distributed T&D infrastructure.

So much of what I have talked about is about the future of our industry, but I should take a moment to return to where I started with this set of articles. I started with something that is far more humble than the advanced technology of microgrids and DERs and drones: a utility pole. In that article, I talked about the difficulty of knowing exactly where a certain pole is. I discussed how often that information is incorrect in a utility’s database and how often that throws curveballs at field workers sent out to do construction or maintenance. In that scenario, field crews equipped with mobile devices use location intelligence-powered apps to confirm and correct infrastructure information as they work. If your organization wants to create digital twins of your infrastructure and operations, you should empower these field workers to be stewards of the information that is the foundation of these models. You also need to give them a stake in the data by ensuring their voices are heard throughout the process of building the models and use cases. No one knows this infrastructure better than this vital part of your organization. The human element too often gets overlooked in discussions of all of this technology, but it is indispensable for this technology to have the impact it is poised to have on the future of our industry.