“The essence of risk management lies in maximizing the areas where we have some control over the outcome while minimizing the areas where we have absolutely no control over the outcome.”—Peter L. Bernstein, Against the Gods: The Remarkable Story of Risk
Electric utilities can’t be blamed for being risk-averse. Meeting the 24x7x365 needs of an increasingly power-dependent population is as demanding as it is mission-critical. No one wants utilities taking needless chances–not customers, utility commissions or utilities themselves.
But this aversion to risk has led to a codification of risk management approaches that don’t work that well. Doing what we’ve always done gets perpetuated mainly because there’s perceived safety in an already accepted practice.
Well-worn, “safe” risk management approaches that may not deliver results (but don’t rock the boat) are beginning to come under pressure due to macroenvironmental factors. Climate change, and the severe weather it’s unleashing, has risk managers scrambling. Whether they be fires in the West or flooding in the South and East, the disruptive and damaging forces of nature are now an “equal opportunity disaster” waiting to happen for any utility anywhere.
Set against the reality of increasing climate risk and a bevy of other high-ticket items, including grid decarbonization and energy equity, is a very practical issue: money is tight. Electric utilities can’t afford all the fixes necessary to keep the lights on in every possible circumstance for every customer. Also, consumers aren’t willing to pay what would be needed to achieve this goal.
That’s where risk comes in.
Which brings us back to the quote by award-winning economist, educator and risk management specialist Peter L. Bernstein that starts this piece. It essentially says, ‘place your bets on what you can control and avoid bets on what you can’t.’
A simple yet direct edict. But let’s be honest: do electric utilities live by it? With all due respect, we say no. But, with even more respect, we see and offer a path to yes. It’s one that’s proven, quick to implement and won’t break the bank.
Your offensive lineworkers can’t run a four-second 40–and that’s OK!
So let’s get into this. We’ll use the example of grid investment optimization with the understanding that it can stand in for any number of use cases, including wildfire mitigation, vegetation management and capital deployment for reliability improvement. Utilities today typically take one of two approaches to risk mitigation. For something like vegetation management, they may use a time-based or cadence-based approach where all parts of the system get equal treatment. Or utilities may rank-order each line or circuit of a system based on outage performance. Given limited funds, those lines or circuits above the average—that is, those that experience more than the average number of outages—receive the most consideration for investment. Perfectly logical.
Except for the fact that your engineers know that these dollars aren’t optimized to fix the problem. For example, some of those circuits perform the worst every year, usually because they’re the longest circuits (more opportunity for problems to occur) or run through the most challenging terrain. Nothing’s going to change that.
Put another way, it’s a little like identifying the 10 slowest runners on your football team and investing your time and effort into making them run faster. The problem, however, is that these 10 players are all on your offensive line. They don’t need to be faster. Yes, it would be nice if they were, but if your running back gained another step or two, you’d win more games.
For utilities, the equivalent of winning more games is investing in the lines or circuits that perform the worst against their true potential rather than against the system average.
Aggregating improvements across circuits with the most potential (think Bernstein’s “where we have some control over the outcome”) will increase overall system performance much more than investing in overall worst performers—some of which are out of our control. Unless you consider a full rebuild, you can’t change a circuit’s length, so if a circuit is performing poorly solely because it’s long, that may not be the optimal place to deploy capital to disproportionally improve reliability.
Putting AI to work
How can utilities shift their risk management approach from a rank-order punch list of poor performing circuits to one ranked by true potential improvement and better return on investment?
I discussed the details with utility decision-makers at UA Week 2021 in the session, “Why you’re thinking about ‘risk’ all wrong;” for those of you who didn’t attend, I’ll tip my hand: the answer lies in your data.
Every line and circuit tells a story that can be understood more granularly, especially by artificial intelligence (AI) and with machine learning, as more performance detail and the variables that affect it—everything from location, microclimate, and rainfall to pole heights and vegetation—are identified, understood, analyzed and monitored.
By reorienting risk strategies around a new narrative created by machine learning that is trained on your data—and easily supplemented by third-party data—you’ll be trading in your list of averages that looks a lot like last year’s for a new one that will create value– directing investment where it will measurably improve system performance.
Tom Martin is managing director of data science at E Source.
