The LA Times reports about the hot temperatures in California and the resulting high electricity demand. Permit me to sketch a simple field experiment I proposed that would solve this "blackout" problem. California's electric utilities such as PGE have implemented "Automated Demand Response". Intuitively, this program offers incentives to commercial and industrial electricity consumers to opt in and receive incentives from the utility in return for giving up some access to electricity during "peak demand" days. The electric utility can take the power it doesn't provide to the ADR participants and provide it to other residential customers on peak days.
My proposal builds on the ADR model. I would like to partner with an electric utility and randomize the $ value of incentives offered to participate in the ADR. We would then observe which commercial and industrial electricity consumers accept the offer. This would allow me to estimate a participation equation as a function of the customer's attributes (i.e age of the building, size of building , industrial code) and the $ value of the incentive offered. To young economists, the payoff of this exercise is we would now have credible estimates of the "supply curve" of electricity during peak days. The utility could use this information to cost effectively identify the "marginal" commercial and industrial electricity consumers and target them with the smallest ADR incentive that nudges them to participate but doesn't "overpay" them.
My simple field experiment solves a well defined calculus problem; minimize the cost to the electric utility of minimizing the probability of a blackout on a hot day. Note that I'm not building any more power plants! I'm using incentives and I'm studying data to figure this out. This is child's play if the electric utilities would work with the academic economists! You know where to find me!
