Adapting to Power Blackouts in Cold Winter Texas
The Microfoundations of Price Elastic Demand for Natural Gas and Electricity
It is cold in Texas in late December 2022. The demand for power is high and winter storms are affecting supply. This combination leads to power blackouts and this causes extreme risks for those who lose access to this key input in staying warm and being comfortable.
Can basic ideas from Econ 101 be used to reduce power blackout risk?
I want to return to the themes I wrote out in this blog post in the aftermath of the Texas Freeze in February 2021.
The adaptation recipe I sketch out here is an opt in design. I do not support forcing anyone to do anything.
Step #1; At the start of each calendar year, the natural gas and electricity providers use their Big Data to identify commercial, residential, and industrial customers who are large consumers of natural gas and electricity. The word “large” might be relative to others located in the same zip code or industry.
Step #2 The energy providers has an address and email for each of these “large” consumers. The energy provider uses this information to invite the consumer to “opt in” to participate in critical peak pricing program.
Each invitee would be offered an upfront payment of X dollars for each of the next 4 years in return for being willing to face a higher critical peak price for energy on certain days of the years.
Step #3 We are diverse. Each person and firm knows their own ability to adapt to facing higher energy prices on a few days per year. Those who are the most nimble in adapting would accept this offer.
Step #4 Note that “nimbleness” is not a fixed trait. A consumer of energy can invest in many energy efficient durables such as insulation and other investments to make one less reliant on energy. If the contract pays off for a few years (X dollar payment), then this investment is more likely to be cost-effective.
As the utility signs up more critical peak consumers (especially those who are large baseline consumers), then on critical weather days the aggregate demand for electricity will be more price sensitive and fewer blackouts will emerge.
Note that this is a problem in information revelation. Those with the least losses from participating in critical peak pricing reveal themselves when offered this $ incentive to sign up. A good field experiment could test this out to measure the cost minimizing supply curve of energy efficiency.
Basic economics under uncertainty can be used to stop blackouts during extreme weather times.
Lazy climate economists who assume a constant damage function of the form;
economic damage = f(extreme heat, extreme cold) ignore that the f() function depends on the contracts that the electric utilities actually offer (i.e do they offer critical peak pricing incentive contracts?). This is the Lucas Critique logic. The f() functions shifts as a function of incentive contracts.