GIS gives utilities a repeatable means of mitigating risk and minimizing surprises.
The probability that something bad will happen makes us think of our protectors: insurance companies. Insurance companies accumulate the combined risk of policyholders. Insurers lose money if bad things happen. They make money if bad things don’t happen.
Some say utilities are risk averse. It’s true that utilities historically are conservative. They avoid taking actions that could trigger unwanted consequences. The problem is, the cost of remaining conservative rises constantly. Given recent economic troubles, utilities must learn to avoid negative consequences while also avoiding overspending to do so. The situation gets worse when you consider utility infrastructure ages faster than many utilities can afford to replace. So as facilities enter disrepair, hazards abound. Reliability suffers.
Power failures, gas main breaks, fires, substation flooding, transformer leaks contaminating sensitive wetlands, sabotage, cyber-attacks, theft of current…it’s enough to keep any executive up at night.
How can anyone manage these risks?
Utility Risk and Location
Insurance companies have always dealt in the business of risk modeling. They understand a simple truth: The more you know, the more accurately you calculate risk. If an insurance agent assesses the risk of a building fire, that agent looks at maintenance records, code violations, and that greasy rag hunkering beside the furnace. The agent might even take a look at neighborhood crime statistics, gang activity, nearby graffiti, and suspicious fires in the area. Demographics, like income and age distribution, also get checked. Anything that could contribute to a building fire gets scrutinized and documented. The more they know, the more they understand their risk of having to pay out. The higher the risk, the costlier the premium.
Condition and location determine risk. Simple.
As utilities mitigate risk, they must look at both. They need to know condition and location of facilities and which hazards lurk nearby. They need to run a proximity analysis—one of the most common spatial-analytic functions in GIS. Only then they can perform a weighted overlay analysis (another common spatial-analytic function) to combine risk factors.
A Transmission Example
Recently, I visited a sophisticated transmission-operations control room. The staff constantly performed real-time power flow studies. They did contingency analyses and ran scenarios of what might happen if a certain transmission line, for instance, tripped out. I asked one dispatcher which of two lines had a higher risk of failure. The utility didn’t look at that, he said. Line One caused more concern than Line Two, overall. But the dispatcher had no insight into the relative risk of failure of one line over the other.
But think: If Line Two had higher risk of failure (much like a creaky building with a greasy rag by the furnace), then that could change operational decisions. The dispatcher agreed but stated the control room didn’t have that kind of data or the means to determine relative risk.
Ironically, they actually did.
GIS can take common utility data and turn out sophisticated risk analyses. That is, GIS can do more than make maps. It can take a whole host of organizational and external data from the web, run advanced spatial analytics, and display results in the control room or over a tablet—anywhere. GIS can do for utilities what assessors do for insurance companies every day. GIS can scour internal databases and the web for datasets, including maps of lightning strike history, floods, wildfire zones, earthquake faults, high-crime areas, and real-time weather services.
Utilities can then see where a critical transmission line crosses potential hazards, like a river or nearby steep slope. They can assess areas where it might be easy to fix a transmission line compared to one where they need a helicopter. Utilities can combine this data in the GIS to discover what parts of the system are more at risk than others. They can disseminate this knowledge throughout the organization. A gas company can assess high-pressure transmission lines crossing heavily traveled highways. They can see where regulator stations are at risk of flooding during a 100-year storm.
Insurance companies hate surprises more than anything else. Surprise is another term for the failure of a risk model. Surprises happen when someone forgot to include a factor, like finding that greasy rag, in the model. A transmission surprise might be learning that your line is under threat of a wildfire. This could surprise the operator only if fire zones weren’t included in the risk analysis.
I encountered my share of surprises when I ran operations for a power company. I discovered something about risk, though—that it can be simple. Know where your stuff is. Determine its condition. Identify all threats. Then perform advanced spatial analytics in your GIS to combine all factors, using common spatial tools (e.g., proximity, buffering, overlay, heat maps, cluster maps, and so on) to build your risk scores. Then, like the insurance companies, you have a standard, repeatable means of mitigating risk, minimizing surprises, and sleeping soundly.
Learn more about how ArcGIS for Electric helps utilities determine risk and assess distribution-system vulnerability here.