Price Elasticity of Demand Analysis for LED Lighting

New Evidence on the Price Elasticity of Demand for Electric Vehicle Charging

If the U.S. is to address climate change, a key mechanism will be a transition to a decarbonized transportation sector. California has been leading the charge through policies aimed to increase electric vehicle (EV) adoption. A challenge of widespread adoption is integrating increased electricity demand from electric vehicles with the grid. In collaborative work with San Diego Gas & Electric (SDG&E), we estimated customer responsiveness to a dynamic, real-time electricity rate at commercial level 2 EV charging stations in multi-unit dwellings and workplaces. The rate is designed to mitigate the impact of EV demand on both distribution grid and system costs.

The key output of our analysis is estimates of the price elasticity of demand for electricity at charging stations that were subject to SDG&E’s Vehicle Grid Integration (VGI) rate. To our knowledge, these findings represent some of the first evidence of the responsiveness of EV charging demand to real-time prices. They also represent some of the only publicly available evidence on the price responsiveness of EV charging demand at commercial level 2 charging stations in multi-unit dwellings and workplaces. (Many of these stations are installed in bi-lateral agreements between site hosts and vendors, which retain the data.) The VGI rate has several features that enable us to recover credible causal estimates of the price elasticity of demand. Notably, the rate is made up of several components that vary at the hour- and distribution-circuit-level:

• A nominal base rate;

• A commodity component that is the hourly CAISO day-ahead wholesale market price;

• A system event adder based on CAISO demand; and

• A local event adder based on circuit-level demand.

Because not all sites were subject to local events, we can not only compare across time within a site (within variation) but also compare sites that were and were not experiencing events in the same hour (between variation). A second important feature is that there are also some site hosts that elect to pay the cost of charging on behalf of drivers, which we refer to as rate-to-host sites. Drivers at these sites have no incentive to curb consumption in response to price. We can therefore use these sites as a placebo test of our model. Our model attempts to account for the fact that events and high prices are not randomly assigned and therefore could be related to charging behavior in unobservable ways that result in biased estimates. A precisely estimated finding of minimal price responsiveness at rate-to-host sites where charging is free for drivers would bolster our confidence that our model has accounted for the potential endogeneity of price and events. Spoiler alert: we do estimate a precise zero effect at these sites!

The table below presents estimated price elasticities for each site type. The table includes coefficient estimates and standard errors from three separate Poisson regressions: rate-to-driver, workplace estimates are presented in column (1); rate-to-driver, multi-unit dwelling estimates are presented in column (2); and rate-to-host estimates are presented in column (3). These estimates pool data from program years 2022 and 2023. Our main findings are as follows:

• At workplace sites, we estimate an elasticity of -0.337. This indicates that, on average, drivers decrease their charging by 3.4% for each 10% increase in prices. 

• At multi-unit dwelling sites, the price responsiveness is similar, with an estimated elasticity of -0.37. These estimates are both statistically significant at the 1% level. 

• At rate-to-host sites where charging is free at the port for drivers, we find there is insufficient evidence to conclude that drivers at these sites were price-responsive; we can also rule out price elasticities below -0.051 based on the coefficient estimate and standard error.

Estimated Elasticities (%) for PY 2022 and PY 2023 Combined

  (1) (2) (3)

  Rate-to-Driver Rate-to-Driver  

  Workplace MUD Rate-to-Host

ln(Price) -0.337*** -0.370*** 0.0265

  (0.0623) (0.0474) (0.0385)

Observations 21,489,394 12,577,911 12,325,088

Ports 1317 729 709

Sites 92 70 51

Pseudo-R-Squared 0.3343 0.1683 0.3832

Note: *** p<0.01, ** p<0.05, * p<0.1. This table reports coefficient estimates and standard errors from three separate Poisson regressions. All regressions are estimated using port-by-hour observations for October 1 2021 through September 30 2023. Standard errors are two-way clustered at the site and hour-of-sample level. Estimated effects are at the port-level and include fixed effects for port, date, day-of-week, weekend-by-hour-of-day, and temperature bin. Rate-to-host results in column (3) are reported for MUD and workplace combined because there is a single MUD rate-to-host site. Fixed effects in column (3) are interacted with MUD/workplace status. All specifications include controls for event anticipation and rebound hours; we do not report coefficients on controls.

These results are exciting for several reasons. Firstly, to our knowledge, these represent the first publicly available estimates of price responsiveness of EV charging demand at commercial level 2 charging stations. If more residents of multi-unit dwellings are to own EVs, as is the hope in a future of widespread adoption, many more such charging stations must be installed. Understanding the price elasticity of demand in this setting is critical to evaluating the effectiveness of rates and policies designed to shift electricity demand at these locations.

Secondly, the availability of rate-to-host sites to serve as a placebo test is a unique feature of this setting that we were able to leverage as a test of our preferred model. If we were to find statistically significant price elasticities at rate-to-host sites, where there is no reason to expect drivers to respond to price, we would be concerned our estimates for rate-to-driver sites were biased.

Finally, the degree of price responsiveness is striking. These drivers are more responsive than the average residential electricity consumer, implying that electric vehicle loads are easier to shift than typical household loads. A meta-analysis of short-run price elasticity of electricity demand for electricity yielded an average estimate of -0.22 (Zhu 2018). Modern applied research into consumer response to gasoline price fluctuations does find very similar estimates of price responsiveness. Recent short-run estimates of the price elasticity of gasoline demand have included -0.37 for U.S. drivers (Coglianese, et al. 2017), as well as between -0.27 and -0.35 (Levin, Lewis and Wolak 2017). While there may not be a deep connection between these charging elasticities and gasoline price elasticities (the estimates use different sources of variation and the available substitutes in each case are quite different), the similarity is remarkable.