Some simple spreadsheet math suggests the outage cost about 100,000 MWh. What does that mean in $$?
There’s been a lot of discussion about the potential economic losses due to the power outages in Northern California at the end of last week. The anecdotes are disturbing: people injured in traffic accidents on unlit roads without traffic signals, hospitals and skilled nursing facilities without power and lots of wasted frozen food. (Also see Andy’s terrific post on the institutional issues.)
I saw a couple people report estimates of the total economic losses, topping out at $2.6 billion. Where do these estimates come from? The basic approach is to figure out (A) how many kilowatt hours of electricity customers wanted to consume but couldn’t during the outage, and then (B) ascribe some economic value to that lost consumption. Think of (B) as the value you would have realized if you’d been able to use electricity. That number is often called the “value of lost load.”
One reason I love studying electricity markets is because the data are so rich and readily available. At my husband’s suggestion, I spent an hour yesterday downloading some data from the California Independent System Operator that could provide an estimate of just how much electricity consumers lost during the shut-offs – in other words, (A).
The chart below show electricity demand every 5 minutes in the 80% of the state covered by the California Independent System Operator. The blue line reflect demand this year and the grey lines reflect demand on the same day of the week last year. In other words, Monday compares Monday, October 7, 2019 to Monday, October 8, 2018.
Monday and Tuesday are the days before the shut-offs began. The Monday lines from 2018 and 2019 look pretty similar, except 2019 is a little higher in the late afternoon hours. Temperatures were very similar across the years: the high in Sacramento was 83 degrees in 2019 and 82 in 2018. Tuesday shows roughly the same pattern, but by Wednesday, it looks like we can see evidence of the outages: 2019 demand is below 2018. The same pattern persists on Thursday and Friday.
Saturday morning in 2019 seems to suggest a little outage hangover, but there are signs later in the day of catch up – maybe people are doing those loads of laundry that they couldn’t do on Wednesday through Friday.
This chart shows daily average instantaneous demand in megawatts by day of the week.
If you take the difference between 2019 and 2018 on Monday and Tuesday, it averages to 385 megawatts, suggesting that demand was on average 385 megawatts higher in 2019 than 2018 on these two days. This is consistent with roughly 1.5% growth year-on-year, which seems reasonable. The difference between 2019 and 2018 on Wednesday through Friday averaged a drop of 1030 megawatts. If you assume that, but for the outages, demand on Wednesday through Friday would have been like Monday and Tuesday and been 385 megawatts higher, this difference becomes a drop of 1,415 megawatts. Over a 72-hour period, this suggests lost load due to the outages of 102,000 megawatt hours.
Essentially, this calculation is using the same week in 2018 to provide the baseline or counterfactual for electricity consumption absent the outages. These are EXTREMELY crude and preliminary estimates. Doing the calculation better could entail using more information, like the last week of September in both years, and, as time goes on, the next couple weeks of October. It would also entail including weather information. The two weeks I’ve looked at were pretty similar Monday through Thursday, but a bit hotter in 2018 by Friday.
My view, though, is that (A) is relatively straightforward to calculate or guestimate. It’s (B) – the value of lost load – where things start to get really speculative.
What does (B) intend to capture, conceptually? One way to think of the value of lost load is what the customer would be willing to pay NOT to lose access to that electricity. For the guy I spoke to at the gym on Saturday, that value was pretty low during the daytime as his solar system has a plug, so he could keep his fridge on and electronics charged for free with an extension cord. For people who use electricity to power medical equipment, by contrast, the value of lost load is really high. And, for people who lost things of value – frozen food, work time, experiments involving frozen specimens – it’s similarly quite high.
Some of the estimates out there in the past couple days used $10,000 per megawatt-hour as the value of lost load. Together with my estimate of (A), that suggests economic losses amounted to about $1 Billion. To put $10,000 in perspective, it suggests that the typical US household, which uses about .025 megawatt-hours per day, would pay $250 to avoid a day-long outage. Would you?
Answers to questions like this are likely to vary based on the situation. For example, I would pay more than twice as much to avoid a 10-hour outage than a 5-hour outage because I start to lose frozen food after 6 hours. And, I’d pay more to avoid an outage of the whole community than one for my block (especially because my dad lives two blocks away and my family can decamp to his house for a very local outage).
In short, we really don’t know what the right number is for the value of lost load. (In ongoing work, some colleagues and I are trying to dig into it, so stay tuned.) And, to make good decisions about the steps we should take to avoid outages, we need better estimates.
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Suggested citation: Wolfram, Catherine. “Measuring the Economic Costs of the PG&E Outages”, Energy Institute Blog, UC Berkeley, October 14, 2019, https://energyathaas.wordpress.com/2019/10/14/measuring-the-economic-costs-of-the-pge-outages/
Catherine Wolfram is Associate Dean for Academic Affairs and the Cora Jane Flood Professor of Business Administration at the Haas School of Business, University of California, Berkeley. She is the Program Director of the National Bureau of Economic Research's Environment and Energy Economics Program, Faculty Director of The E2e Project, a research organization focused on energy efficiency and a research affiliate at the Energy Institute at Haas. She is also an affiliated faculty member of in the Agriculture and Resource Economics department and the Energy and Resources Group at Berkeley.
Wolfram has published extensively on the economics of energy markets. Her work has analyzed rural electrification programs in the developing world, energy efficiency programs in the US, the effects of environmental regulation on energy markets and the impact of privatization and restructuring in the US and UK. She is currently implementing several randomized controlled trials to evaluate energy programs in the U.S., Ghana, and Kenya.
She received a PhD in Economics from MIT in 1996 and an AB from Harvard in 1989. Before joining the faculty at UC Berkeley, she was an Assistant Professor of Economics at Harvard.