Neara is a physics-enabled digital twin that allows you to go from just seeing what is to understanding what if. By bringing historically fragmented data and workflows into a geometrically precise model that behaves like real infrastructure, Neara enables you to simulate interventions and prioritize investments with structural precision in any risk context across your network, whether you’re building a thirty-day storm hardening plan or a five-year resiliency road map. Neara unifies your GIS and network records, LiDAR and satellite imagery, engineering data specs, environmental operating conditions, and inspection data into a single model. This means you can see how assets interact with terrain, weather, and vegetation, not only as they are, but as they would be in severe weather, increased load, or amid changing designs. When you understand how your infrastructure performs in real-world environmental conditions, how asset failures and interventions affect one another across the network, and how OpEx and CapEx decisions add up over time, you can build defensible, structurally precise plans and see how they impact the network before you spend capital or deploy crews. To show how these capabilities come together in practice, we’ll look at three scenarios. First, planning for severe weather and network hardening. Second, adding new capacity with consideration for wildfire exposure. And third, evaluating intervention trade-offs in different risk mitigation scenarios. Let’s take a look. When you’re planning ahead to maintain reliability and increase resiliency during storm season, you need to answer questions like, Where do I have the highest outage risk in hurricane-force winds? Where can I add a guy wire instead of replacing a pole for the same reduction risk? How do I determine the most effective ways to deploy response teams based on accessibility? Here, we’re looking at fallen risk alongside pole utilization and worst-case NESC loading scenarios. Surfacing both risk drivers in a single view enables us to easily highlight spans with multiple points of vulnerability. Neara calculates fallen risk by subdividing the canopy into individual trees. Then it runs a physics simulation to identify which direction the trees would have to fall to not only hit the network, but actually pull it down. What we’re looking at here is the worst-case arcs colorized by line impact alongside overfall distance. Now let’s add pole loading to this view. Finite element analysis shows which components are under greatest strain and where failure is most likely. Because the model is connected to the network as a whole, we can simulate a cascading failure to see how far the impact spreads. I’ll run the simulation, and Neara will tell me how many poles to either side of the first failure would go down as a result. From here, I can evaluate different options to contain the cascading failure. I’ll simulate adding a guy wire here, and you’ll notice that Neara dynamically reruns the simulation, and we can see that the second pole no longer fails when the one next to it goes down, meaning that we’ve reduced both the likelihood and the downstream impact of this failure. And the same model can incorporate flood conditions to show clearance and access as waters rise and recede, allowing crews to confidently plan switching operations, determine where and when teams can safely reenergize spans, and direct teams for follow-up inspections. By visualizing vegetation alongside mechanical risk and environmental factors, you can pinpoint the most consequential risks and decide where to dispatch crews, when to prioritize structural upgrades, and where an integrated approach will reduce the risk of multi-structure outages. Next, imagine you need to accommodate a 50% increase in demand without increasing ignition risk. This requires understanding how capacity additions impact wildfire risk drivers and vice versa. This raises questions like, What’s the optimal height for new poles to maintain wildfire-safe clearances at higher line ratings? How do I route a new network build to minimize vegetation program costs? Where can we use covered conductors without creating new capacity bottlenecks? Here, we’re looking at a baseline model. Now we can change the design to simulate adding more capacity. For example, as we start designing a new route in this area, we can quickly understand the potential clearance constraints and line rating bottlenecks of siting additional capacity in any given location or evaluate whether to build on one side of the street or the other. From here, we can simulate increasing temperature and wind and look at the max sag and sway on the conductors to assess how high temperatures or adding more current to the line affects clearance so we can identify areas that are most likely to increase ignition risk. So instead of running separate veg studies, clearance checks, and capacity models, you see design and clearances together and understand exactly how operating lines or routing new builds changes wildfire exposure. Having design capabilities in the same view as the vegetation analysis means that updated clearance information flows directly into your vegetation team’s workflow, so no downstream consequences slip through the cracks. You can evaluate every action through both objectives, so your load growth plan is wildfire safe by design. Finally, let’s look at risk and value optimization, or RVO, in Neara. Every utility already applies risk-based asset management principles. Neara enriches your risk models with physics and spatial intelligence to easily augment risk drivers and consequence models, calculate the impact and benefit of multiple classes of interventions, and enable full auditability and transparency of every decision you make. Whether you’re planning for next year or building a multiyear resiliency plan, Neara can help you answer questions like, How can I balance spending across competing risk-reduction initiatives for wildfire safety, reliability, and environmental risk within a fixed budget? Where is risk most concentrated across the network? Which interventions reduce the most risk for the least spend? Using RVO, you can test investment strategies before you spend any money. Let’s say we have a $1.5 billion budget and pressure to remove wildfire risk. If we allocate everything there, the risk drops quickly at first and then slows. RVO shows that curve in real time so you can see exactly where dollars stop buying meaningful reduction. Realistically, you want to balance wildfire risk cost with safety or reliability. As we adjust these sliders, Neara calculates risk cost saved per dollar across interventions like veg trimming or installing covered conductors and surfaces the reduction risk across lenses. Here, we’re running a benefit-cost ratio analysis to determine where we should prioritize installing covered conductors to reduce the likelihood of high-consequence wildfire risk. Neara can create a complex calculation to assign each span a wildfire risk score using variables like asset data, vegetation encroachment risk, and the expected consequence cost if an ignition occurs. We compare that risk score to the cost of installing a covered conductor, both at a single span level or grouped across nearby spans, to show where the benefit-cost ratio actually makes sense. Then we can compare that against the cost of intensified vegetation trimming in the same area to see which intervention buys down more wildfire risk per dollar and where a covered conductor program is truly worth scaling. With that clarity, teams can organize work into investments by geography, work type, or priority. Neara can auto-suggest a mixed scope based on your thresholds or surface costs and performance trade-offs for each option. Nira helps utilities prepare, predict, and optimize by turning raw data, disparate workflows, and fragile spreadsheets into a cohesive, physics-enabled digital twin that informs every decision across your network so you can move faster, align across silos, and invest with confidence.
