Price Elasticity of Demand for Electric Vehicle Charging

Posted on August 15, 2024 by Marshall Blundell

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.

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.

Decarbonization and Heat Pumps: How Much Do Incentives Influence Adoption?

Decarbonization and Heat Pumps: How Much do Incentives Influence Adoption?

Posted on October 27, 2023 by Josh Bode

Many States have adopted decarbonization goals, and heat pumps have become a central element of the decarbonization strategies. The underlying logic is simple. As electric grid supply becomes cleaner, shift heating from fossil fuels (oil, propane, natural gas, wood) to highly efficient electric heat pumps. As a result, we have seen an explosion of heat pump programs and incentives.

However, three fundamental questions remain open:

By how much can incentives influence and accelerate heat pump adoption?
Are the policy goals being adopted realistic?
Are these programs throwing money at customers who would have installed heat pumps on their own (free riders)?

Our team convinced a few utility and program administrators to collaborate to answer these critical questions. The first plot below shows the variation in incentive levels when we pool the heat pump volume and incentive data together. The second plot shows the incentives as a percentage of the total costs. Notably, incentives varied from 10% to 90% of total costs.

The different utilities and program administrators had various incentive levels with different patterns over time. The incentives have the effect of a discount sale, lowering the customer-facing price. The variation across utilities and time allowed our team to quantify how much incentives influence sales volume – the price elasticity of heat pumps. The plot below shows the relationship after accounting for utility, income, year or week, and seasonal effect. It follows a classic demand curve pattern. When customer-facing prices are high, the volume of heat pumps is lower. When customer-facing prices drop, the volume of heat pumps increases.

The heat pump price elasticities allow us to:

Develop operational models that allow program administrators to adjust incentives to hit short-term goals;
Assess the influence of incentives on long-run adoption curves (popularly known as S-curves); and
Estimate free-ridership as a function of incentives

What we took is a first step. The quality of the analysis and results improves with more experimentation in incentive levels and more locations.

If you are involved with heat pump programs, consider this an invitation to join our effort to answer these critical planning and operational questions.

Marshall Blundell

Josh Bode

Jesse Smith

Does Residential Battery Storage Help the Grid?

Posted on November 29, 2021 by Josh Bode

Do residential behind-the-meter batteries help the grid? The answer is: unfortunately, not as much as one would hope. The below plot shows my solar unit, two Tesla batteries, my whole home use, and my grid use on four select days.

The first plot is a very good outcome. It shows no grid usage. The home (blue) is exclusively being powered by battery storage (green) and solar (orange). This pattern happens fairly often in the spring when household energy consumption is low and solar production is high. It also means no grid exports, even if the grid needs additional resources. The second case – contributing to the ramp – is disturbingly common. The battery starts charging as soon as the sun is up and is fully charged around mid-day, at which point, all of the solar comes online all at once. From a grid standpoint, it’s contributing to the ramps and is not helping absorb surplus solar. The last two scenarios are less common. The third plot shows some use during off-peak hours (I was charging a electric car), my intentional draw from the grid immediately before the 4-9 pm period, and use of the battery throughout that peak window (with a small amount of exports). It was also a very hot day. You see my AC unit cycling on and off. The last plot shows the full capability of the battery, close to 7 kW, which is rarely seen. The battery went into storm mode and drew power from the grid rather than charge only using the rooftop solar. When operated in default mode, the battery will almost never charge or discharge at its full capability. It means that behind-the-meter batteries are an under-utilized, untapped resource during periods from the grid needs resources the most and during period with excess generation on the grid.

If left to operate on their own, the batteries typically charge as soon as the sun comes up (the wrong time from a grid perspective), often don’t absorb surplus generation, and rarely, if ever, export to the grid when resources are needed most. By design, they operate with the customer in mind, which is an excellent objective. However, it is possible to lower customer bills, provide backup power, and also improve operations for the grid. As saying goes, “we can walk and chew gum at the same time.”

Why does this matter? Behind the meter battery storage is a growing, untapped resource, and the need for flexible, predictable resources is growing. The below plot shows the growth in residential behind-the-meter battery storage in California. There are currently about 400 MW, but the magnitude of growing quickly. Roughly 8-10% of new solar installations are also install battery storage at the same time. And the share of solar sites electing battery storage is growing. What can be done to tap into this under-utilized resource? Clearly, it is not enough to have the batteries installed. It is necessary to operate them at the right times and to provide customers incentives to do so.

DSA is involved in several efforts to better use battery storage, including:

A virtual power plant study with over 1,000 residential batteries. The batteries are providing grid response based on day-ahead market prices (after a strike price is hit) and in response to system operator alerts, warnings, and emergencies.
A battery storage pilot. Perhaps the most exciting part of the pilot is that we are using a randomized control trial to explicitly test how different incentive levels and incentive structures affect customer willingness to allow utilities to operate the battery for grid needs. In addition, we are testing daily operations with day-ahead market prices and time-of-use rates, and testing how to modify dispatch algorithms so behind the meter batteries can deliver a predictable, incremental resource. The pilot includes two tracks: one for customers with existing battery storage and for customers who are in the process of installing solar and/or battery storage (another sites). DSA is in charge of all aspects of the turn-key pilot including design, recruitment, event operations, communication with the batteries (or more accurately, the battery API), setting up data tracking and collection databases, and evaluation.
Programming a utility-scale battery to maximize load relief and demand charges for coops
Identifying high-value locations for distribution connected battery storage.
Assessing economic feasibility of utility-owned battery storage operated in response to market prices and T&D needs.

Is Electric Demand Rebounding? An Interactive Dashboard

Posted on June 30, 2020 by Josh Bode

The last few months have been unusual. COVID-19 has led to changes in our commutes, business operations, and home use patterns. It’s also changed how, when, and how much electricity we use. As long as we have social-distancing, efforts to re-open, and concerns about a second wave, forecasting electric demand will be more difficult. There are several fundamental questions:

By how much have electric demand and sales dropped?
Are different parts of the U.S. affected differently?
Is electricity demand rebounding to pre pre-COVID 19 levels? If so, where and how quickly?
How will demand rebound over the remainder of the summer, when most electric systems peak?

We were curious ourselves and decided to answer those questions. In the process, we built a live dashboard to track the effect of COVID on electric demand across the U.S. The plot below shows how electric demand changed over time compared to pre-COVID levels. The dots are individual days; the line shows the 7-day trend. In the U.S., electricity peak demand and sales dropped by roughly 7.5%. The drop is most noticeable in mid-March when the first stay-home orders started going into effect. Starting in May and June (depending on location), electricity use has begun to inch back up.

Electric demand dropped off throughout most of the U.S. The plot below shows snapshots of the change in electrical demand for the first Monday of March, April, May, and June. For some areas, such as Nevada and Texas, efforts to re-open the economy (and casinos) are clearly evident in the energy patterns.

So, where do we go from here? Our dashboard will be updated daily to track how electricity demand and use changes as we attempt to return to normal. The dashboard provides the ability to view specific balancing authorities or the U.S. as a whole. Monitoring the effect of COVID on electric demand is the first step. COVID-19 has made forecasting energy sales and peak demand more difficult, both in the near and long term. The next step is developing short and long term forecasts that adjust as social distancing and economic re-openings evolve. We have some ideas about how to do that well.

California Electric Vehicle Penetration – Granular Maps

Posted on April 5, 2019 by Josh Bode

Earlier this week, the California Department of Motor Vehicles posted a granular data set with vehicle registration details by zip code, vehicle type (gas, electric, plug-in-electric, hybrid, etc.), model year, and make (e.g., Tesla, Toyota, etc.). The data runs through October, 2018. Because it includes all vehicles, it provides insight into the penetration of electrified vehicles overall and by model year. We decided to visualize it, make it interactive, and share it. Enjoy.

We’ll be visualizing vehicle penetration data from Massachusetts and New York relatively soon. If you know of other states that provide granular vehicle data, drop us a note and we’ll add them to our list.

Summer Demand Response Changes at PJM

Posted on March 21, 2019 by Jesse Smith

PJM recently released an updated 2019 Peak Load Forecast, the primary change being the inclusion of approved Peak Shaving Load Adjustments for summer-only demand response programs (report and supporting data available at: http://www.pjm.com/library/reports-notices.aspx).

Demand Side Analytics has prepared a report for the Consumer Advocate of PJM States which provides a high-level overview of the PJM change and explores implications for program administrators. We focus on three primary areas: 1) understanding load forecast adjustments and the implications for participation and timing, 2) Offer Strategy and Considerations, and 3) Price Suppression Effects. The full report is available here.

Peak Shaving Adjustments

Historically demand resources such as demand response and energy efficiency have entered the market as supply and been eligible to compete alongside traditional supply side resources (power plants) in a competitive auction to fulfill the resource requirements for the region. Demand response resources such as utility direct load control of central air conditioners have recently encountered difficulty participating in the market due to PJM’s “capacity performance” definition of generation capacity. A Peak Shaving Adjustment (PSA) offers a fundamentally different means for demand response to participate in the Reliability Pricing Model (RPM). Instead of being treated as supply that is capable of fulfilling resource requirements, a Peak Shaving Adjustment enters the market on the demand side. The characteristics of the shaving are used to create modified peak load forecasts.
In the report we discuss the factors that affect how a Peak Shaving resource will affect the Variable Resource Requirement, key design components as adopted by PJM, and the implications of barring dual participation.

Offer Strategy

The peak shaving “pledge” happens before the auction so there’s some uncertainty with regard to the value of a commitment it is made. If you have a state program/resource, or are contemplating developing one, how do you balance maximizing the load forecast adjustments while maintaining cost effectiveness? For example, would it be better to shave 100 MW for three hours on all days hotter than 95 degrees, or shave 50 MW for 5 hours on all days hotter than 90 degrees? In the report we explore the effects of:

System Load characteristics – how the amount of summer vs. winter peaking risk affects compensation, and considerations of event frequency vs. duration.
Weather – varies from year to year, but commitments are based on THI thresholds. If predicting performance based on median weather what is the risk in extreme weather years and the cost/benefit calculus of underperforming.
Customer rotation – how frequently can customers reasonably be called without fatigue?

Price Suppression

The resource clearing prices in the PJM BRA are a function of zonal demand and the cost of resources available to meet those demands. Reducing peak capacity requirements generates value both by avoiding the costs associated with the load being shaved, and potentially by lowering the price for the remaining capacity that still must be procured. This second component is the price suppression effect. In reality the VRR is not a curve, but a staircase with tread width the size of power plants. Thus there is no guarantee that reducing peak will reduce the clearing price. Using PJM BRA sensitivity analyses from prior years, we estimate the slope of the supply curve for different market segments and provide bounds on the potential price suppression effect.

Washington State Distributed Energy Resource Planning

Posted on November 21, 2017 by Josh Bode

There has been a rapidly growing level of interest in distribution planning and how to integrated distributed energy resources (DERs). The growth of DERs is fundamentally changing the nature of transmission and distribution system forecasting, planning, and operations. However, the current state of transmission and distribution planning and of DER integration into planning vary widely from utility to utility. For this project, our team conducted an inventory of current utility distribution planning practices and capabilities in Washington. The results were presented at Workshop on November 20th to a broad range of stakeholders.

Price Elasticity of Demand Analysis for LED Lighting

Posted on August 15, 2017 by Jesse Smith

Demand Side Analytics recently designed and analyzed an LED pricing trial for Efficiency Maine Trust. The study involved the two largest retailers in the state and provided some valuable program design information on managing free-ridership, setting incentive levels, and capturing off shelf product placement. Full report can be found at the link below:

LED Lighting Pricing Trial Results