The Value of Electricity Reliability: Evidence from Battery Adoption, with David Brown

Notable Quotes

• The economics of improving the reliability of the electricity grid: “We’re concerned about this increasing frequency and intensity of power outages … In principle, we can make the electricity grid really, really reliable. You can install tons of backup generation, onsite fuel storage of, say, natural gas … The key challenge is at what cost. As with anything in economics, we care about the costs and the benefits.” (5:23)
• Consumers with unreliable electricity may invest in private solutions: “We find a really large increase in solar and storage adoption in [power] outage–exposed ZIP codes, relative to ZIP codes not exposed to those power outages. To give you a scale of this number … we find that the outages caused a 45 percent increase in battery adoption … that would not have otherwise occurred.” (13:25)
• Inequities limit access to technologies that improve electricity reliability: “There have been these long-standing concerns of equity in these distributed energy resources—we’re talking solar, electric vehicles, and now battery storage … if we’re concerned that there’s going to be reduced reliability on the electricity grid, and only high-income people can do something to avoid the consequences of that, I think those concerns become even more stark.” (23:15)

Top of the Stack

• “The Value of Electricity Reliability: Evidence from Battery Adoption” by David P. Brown and Lucija Muehlenbachs
• “Socioeconomic and demographic disparities in residential battery storage adoption: Evidence from California” by David P. Brown
• “What Are the Benefits of Electric Vehicles for Climate, Air Pollution, and Health?” by Joshua Linn and Daniel Shawhan

Transcript

Kristin Hayes: Hello and welcome to Resources Radio, a weekly podcast from Resources for the Future. I’m your host, Kristin Hayes. My guest today is David Brown, associate professor and holder of a Canada Research Chair in Energy Economics and Policy at the University of Alberta. His research lies at the intersection of energy economics, industrial organization, and regulatory policy, with a particular focus on the performance of electricity markets.

Today, Dave and I are discussing a paper that he coauthored with Resources for the Future (RFF) University Fellow Lucija Muehlenbachs, who’s at the University of Calgary. The paper’s on the value of electricity reliability to consumers in California who were faced with electricity blackouts. This research has recently been published as a working paper on RFF’s website, so definitely check it out. But, in the meantime, enjoy this chance to hear directly from one of the authors on this intriguing study. Stay with us.

Hi, Dave. Thank you so much for taking the time to talk with me today on Resources Radio.

David Brown: Thanks for having me. I appreciate it.

Kristin Hayes: Of course. We’ll get to the paper in just a minute, but before that, I know it’s always nice for our listeners to know a little bit more about our guests. How did you end up in Alberta working on electricity issues?

David Brown: I did my PhD thesis at the University of Florida, and a lot of my work was analyzing questions related to restructuring electricity markets, motivating investment, and minimizing cost in these markets. I made my way through the job market process—that is, the academic job market—to Alberta. Alberta was a really good place for me, because it had a very similar electricity market to ones that operated in the United States. It was a nice, natural transition to where my dissertation research was.

Kristin Hayes: Why were you drawn to work on electricity issues in the first place? Why is that a sector that particularly intrigued you?

David Brown: In undergrad, I was interested in questions related to energy economics in particular, and electricity markets, back in 2014, was a really active space. There were a lot of renewables and a lot of policy changes. It seemed like a really nice place to analyze questions in the energy sector.

Kristin Hayes: Okay, great. Well, let’s dive into the discussion of this new working paper that you coauthored with Lucija Muehlenbachs. The title of that paper is, “The Value of Electricity Reliability: Evidence From Battery Adoption.” I mentioned at the outset that the research focuses on consumers in California and, in particular, on ones who have had their power cut intentionally by utilities to avoid wildfires that might be induced by electric infrastructure, which has certainly been an issue in the past. Why don’t you start by giving us some context, grounding us in the particulars of the electricity outages that you focused on?

David Brown: Yeah, for sure. I think it’s important to have a bit of background. The context in California, as many people know, is that California was faced with these large and deadly wildfires really starting, say, in 2015, and ever since then, and these are really sparked by the drought conditions and the winds in California. In particular, some wildfires were triggered by electric infrastructure. You can think about vegetation hitting power lines, causing sparks. Those sparks fall on the ground, hit the really dry ground, and spark wildfires. Since 2017, this has been linked to almost over 30 wildfires and burning over 23,000 homes and businesses.

In California, the temporary fix is these public safety power shutoffs (PSPS). I’ll try and minimize jargon, but PSPS outages—effectively what they do is rather than have this high risk of electric infrastructure–induced wildfires, they’re going to de-energize large segments of the grid. This really started to pick up in the summer of 2018 when the California Public Utility Commission passed a resolution that basically said, “Okay, all investor-owned utilities in California can use these outage events to de-energize the grid to try and reduce the risk of these power outages.”

Kristin Hayes: Quite intentional, then, and quite by design, which I think is an interesting piece of the puzzle here for this paper. I want to get methodological for just a minute—one thing I noted in looking over the paper is that it adds to the literature on consumers’ willingness to pay to avoid these electricity outages, which is referred to, at times, as the “value of lost load.”

That is a term that I’m hoping we can spend just a little bit of time on. Can you say a bit about the importance of that value again, the value of lost load, and maybe some of the research techniques that are typically employed to help understand that value?

David Brown: Yeah, so the idea of value of lost load (VoLL) kind of gets back to this issue of electricity market design, where we’re concerned about this increasing frequency and intensity of power outages. As we just alluded to in California, there have been large and intense power outages. You think back to February 2021 with the winter storm event in Texas. There are these really serious and growing concerns about reliability and resiliency.

In principle, we can make the electricity grid really, really reliable. You can install tons of backup generation, onsite fuel storage of, say, natural gas. We can underline transmission and distribution lines, which could cause, say, wildfires. The key challenge is at what cost—as with anything in economics, we care about the costs and the benefits. So, what are the benefits? That kind of gets to the fundamental question of how much people actually value electricity reliability.

And that’s kind of where it enters the VoLL—the willingness to pay to avoid power outages—and, as you can imagine, this VoLL has a lot of important implications in electricity markets. Think about reliability standards that determine these capital investments. We’ve got to say, “Okay, how much reliability do we want?” Well, we’ve got to think about the cost and benefits, and there’s a standard one-event-per-10-year regulatory requirement that says we don’t want a lot of power outages. A lot of that reliability requirement is grounded in the idea of what we are willing to pay to avoid power outages. Another classic example is quantifying damages of outages, and, lastly, setting scarcity pricing in wholesale markets.

But despite the clear importance of VoLL, estimates are really, really difficult. Estimates in the literature largely rely on stated preference surveys—even though there’s some high-quality work in that area, they run into some issues of basically asking people questions to elicit their preferences. What we do is we want to fill this gap by providing among the first revealed preference estimates of this key parameter, VoLL.

Kristin Hayes: Let’s spend just a little time on that difference, as well. You mentioned stated preference, and that’s what consumers say that they’re willing to pay to increase reliability, but my understanding is that you and Lucija employed some pretty innovative techniques to show what you referred to as revealed preference. What’s the difference between stated preference and revealed preference, and why does it matter?

David Brown: Stated preference, at a 10,000-foot level, asks people survey questions, these hypothetical questions that are carefully designed, or they provide them with different options that they can choose from. Based upon those questions or those choices, they try and back out key parameters. For example, how much are you willing to pay to avoid a power outage? There are numerous potential biases, and there’s a large literature pointing out these challenges with data preference surveys.

But the one that’s most intuitive to me is this hypothetical bias. Imagine I ask you a theoretical question. If you were exposed to an outage, how much would you be willing to pay? That’s a pretty hard thing to do, at least for me, conceptually, to give you an answer to that question. What revealed preferences do—and this is the kind of ideal solution, but it’s very difficult to do—is rather than looking at what you say you’re going to do, let’s actually look at what you do. We typically believe that we can elicit better estimates based upon revealed preferences.

Kristin Hayes: That’s fantastic context. With all of that background, let’s talk a little bit more about how you approached this work. As I noted, the title of the paper references evidence from battery adoption. How does battery adoption fit into this revealed preference work? Maybe say a little bit about what data on battery adoption you were looking at.

David Brown: There’s a common approach in environmental economics when there’s not a clear market to value a good, right? There’s not a clear market to value your willingness to pay to avoid, say, air pollution or water pollution. What a lot of people do in the environmental economics literature is try to say, “What are investments that people make to avoid these damages?” I invest in air purifiers to avoid air pollution. I buy water bottles to avoid water pollution. These are called defensive investments or averting expenditures. In a nutshell, that’s basically what we do. We say, “Okay, we adopt this approach to look at what you do. We look at the investments that you will make to avoid power outages.”

Importantly for our contacts, solar panels alone cannot avert power outages. The electricity company has technicians that go and try and reconnect your home during an outage, but you can’t have the wire live, so they actually have to shut off your solar panel. But with batteries, it allows you to island your home, and it allows you to operate during a power outage. What we do is we utilize this as a defensive investment, and we leverage it. I view this as the stars having aligned for us from a data perspective.

We have really unique data. We have publicly available data on solar and battery adoption at the ZIP-code level. Most importantly, for our contacts, we have distribution feeder-level outage data. Essentially, when there’s a PSPS outage, California has published every single distribution feeder on outage and the number of customers that are affected. The last piece of the puzzle is the California Public Utility Commission requires all utilities to publish detailed spatial maps of the entire grid in California, which is really unique. We leverage all these data sets to match outages to locations and to solar and battery adoptions.

Kristin Hayes: That’s really interesting. Do you know why California has placed extra emphasis on making this data publicly available compared to other places?

David Brown: I talk to regulators all the time, and I’m like, “California is the place to look at.” This is because of other regulatory dockets related to distributed energy resources more broadly. With solar, electric vehicles, batteries, there’s a big emphasis on transparency in California about where to locate these resources and how to value their locations and how to leverage that value in order to unlock that value. The California Public Utility Commission has unlocked data to provide more transparency and information.

Kristin Hayes: Very interesting. That’s great. What did you find related to battery adoption? I’m going to leave it at that very broad question. I know there are a number of packaged findings within there, but let me just ask you that. What did you find related to battery adoption?

David Brown: We don’t need to get too into the weeds from an econometrics standpoint, but we effectively implement a difference-in-differences methodology to back out how much these outages impacted adoption, and we find a really large increase in solar and storage adoption in outage-exposed ZIP codes relative to ZIP codes not exposed to those power outages. To give you a scale of this number, we can use this model to essentially simulate the percentage increase in battery adoption in Pacific Gas and Electric, which is what we’re focusing on, and we find that the outages caused a 45 percent increase in battery adoption in Pacific Gas and Electric that would not have otherwise occurred—a very sizable proportional increase in battery adoption.

Before we can talk more about the findings, one other key finding we found was that there’s some clear income inequality. The vast majority of adoptions were adopted in the top 25th percentile of income by ZIP code. These things are expensive—a standard power wall, a Tesla power wall, which is the majority of adoptions in our data, is $13,000 just for the battery. It’s a pretty expensive thing. Then, the solar panels on top of that—a standard five-kilowatt solar system is anywhere from $12,000 to $16,000. Together you’re talking about $25,000 of unsubsidized cost for this system.

Kristin Hayes: You’re right. That’s a lot of money. I just wanted to clarify one thing or make sure that I’m clear on one thing. The batteries really do need to be installed with a solar system so that the battery is actually being fed by the solar when it’s up and running. So, installing a battery on its own isn’t effective as a resilient strategy. Is that right?

David Brown: Yeah, for the most part. I mean, technically, you could install a battery and charge it from the grid, but we very rarely see anything like that.

Kristin Hayes: Okay. Interesting. Well, I definitely want to come back to your last point about the income distribution. But first, let me turn to something else that really caught my eye as I was looking through the paper. You and Lucija note that, and I’m going to quote here, “The sum costs of new distributed energy technologies are rapidly changing, making it important to also capture the implications of uncertainty such as the additional value of waiting to adopt.”

I’m going to pull you back into the methodological weeds again here for a second, but I’d love to hear just a little bit more about why that uncertainty matters and how you handled the uncertainty around the trajectories of battery costs in your research.

David Brown: In our setting, this kind of gets at the heart of why we developed this in-the-weeds dynamic structural model. I’ll try and keep it at a high level, but you could think about this issue of the decision to invest in solar or solar plus storage in general. It’s a really complicated decision. It’s a durable good, and there’re a lot of things you’ve got to consider. There are up-front costs—as I alluded to, anywhere from $15,000 to $30,000 for this system. You’ve got subsidies, and those subsidies are changing over time. It has impacts on your bill. So, you consume power from your solar panels, you can arbitrage on different time-of-use rates using your solar when it’s not on an outage. You have this warm glow, this, “Hey, I’m cool, I have solar panels, I have a battery. I can brag about it to my friends.”

Lastly, installing a battery can avoid consequences of future outages, but layer on top of all of these factors, you don’t know how these things are going to evolve going forward. I have a decent understanding of what’s going to happen today on all these factors, but I don’t know how they’re going to evolve, and then I have to make the decision, Do I invest now, or do I wait a year to adopt this technology? That is why we developed this dynamic model, which essentially allows us to simultaneously model the investment decision, but also build into the model the uncertainty that I face going forward.

Kristin Hayes: Okay. Does the policy landscape come into play? You mentioned subsidies earlier on, too, and certainly I know subsidies—particularly when they’re at the scale that they have been in the past and are likely to be in the future—can make a significant difference, too. So, does the policy landscape also come into play, and have recent bills actually reduced some of the uncertainty around the availability of those policy incentives?

David Brown: There are two key relevant subsidies for residential solar and storage. The first is this federal income tax credit. It was 26 percent and 30 percent of the money you can get back on the capital cost of the technology, but after these events, which primarily occurred in 2019 and mid-2020, California started to really shift to this Self-Generation Incentive Program (SGIP). Essentially, it provides really big subsidies for battery storage targeting households that are impacted by these PSPS outages. So, absolutely, there are big impacts of these different subsidy policies on batteries. These subsidy policies, especially the SGIP, were announced and changed after these large PSPS events.

Kristin Hayes: Right. Okay. Well, let’s sum it all up. This has been fantastic, by the way. You’ve done a really great job of explaining both the context and the methodology. I’m going to sum it all up, using this modeling framework that we’ve been discussing, what do you find in terms of the estimates of the value of loss load, hearkening all the way back to our earlier discussion of the value of loss load, and how does that compare to previous research?

David Brown: Of course, everything depends upon different tweaks of the model, but we’re talking about $3,000—and I’ll provide context, because most people will be like, “What the heck is this number?” But it’s $3,500–$7,000 per megawatt hour, and, to provide you with some perspective, the average retail price of electricity—so, every single unit of electricity you consume, if you scale that up to a megawatt hour—is $160. It’s a pretty big scale factor above the standard price we pay for electricity. To provide you another comparison, I mentioned these wholesale price caps. These things are basically maximum prices that they can charge in any given hour for wholesale electricity. In Alberta, it’s $1,000, where I live. In Texas, it’s $5,000. Texas lands us right in the middle of our range of VoLL.

You ask the question of the existing literature, and, as I mentioned, stated preference unfortunately is all over the map. There are some estimates as low as $50 and some estimates as high as a $100,000. We’re smack dab in the range of US estimates, which tend to vary between $1,000 and $9,000.

Kristin Hayes: Can you say just a little bit more about why those are so radically different—so much radically higher than what people are actually paying for electricity? It seems—I don’t know if counterintuitive is the right word—but here we are, people are concerned about rising electricity prices, when in reality it seems like this is showing that they’d be willing to pay a lot more to make sure that they have electricity. I’m not sure I’m thinking about that the right way, but I’m wondering if you can say just a little bit more about how to think about that difference between the revealed preference versus the actual electricity prices.

David Brown: Definitely. I think it’s tough. The $160, for example, is every unit of electricity—this is what I’m paying on average across the United States. But VoLL—we’re talking about outages, right? If you have a power outage, a lot can happen. You lose power for days, your food goes bad, you have really serious life inconveniences. In the context of California and Texas, people have medical equipment that relies upon electricity, and this can cause serious health consequences and potentially death. I think it’s important to differentiate your consumption value and then your value to avoid a complete blackout. I totally understand your question, but I think they’re two different things a little bit.

Kristin Hayes: That’s great. That’s a good way for me to think about it, and I’m sure helpful for our listeners, too. So I guess I’d like to close our discussion of the paper by revisiting the income distribution finding and the reality that, and I’m going to quote here again, “That recent outages amplify the growing disparity in the adoption of emerging energy technologies.”

You certainly gave a good explanation of the cost of these technologies, and it seems inevitable that at those price ranges, they would be adopted more by high-income consumers. So, honestly, I’m not too surprised at that finding. But I guess I wanted to ask you a bit of a forward-looking question on this, and whether your research actually provides any clues as to how utilities or policymakers or other decisionmakers could actually begin to ameliorate that disparity a bit more?

David Brown: Yeah, so there have been these long-standing concerns of equity in these distributed energy resources—we’re talking solar, electric vehicles, and now battery storage. I think it’s particularly important here, because it’s not just a financial concern. A lot of these concerns are that if you don’t have access to solar panels, you can’t internalize the financial and environmental benefits as a household. Well, our context is, I would say, even more concerning—if we’re concerned that there’s going to be reduced reliability on the electricity grid, and only high-income people can do something to avoid the consequences of that, I think those concerns become even more stark.

So, what could policymakers do? They recognize these challenges and that this is a serious problem. There are ongoing efforts. As I mentioned, in California, they have this Self-Generation Incentive Program, this SGIP, and, for example, they have subsidies targeting battery adoption. This started in the summer of 2020. It really ramped up by trying to target low-income households and people with medical conditions who may be particularly vulnerable to these outage events. I have a companion paper that essentially correlates battery adoption and income and other socioeconomic factors, and it suggests that really well-targeted subsidies—for example, these SGIP subsidies—can start to overcome some of those gaps, because they have a really small line item in the SGIP that’s really targeting low-income people. I do find that those really targeted subsidies are helping break the barrier—and these subsidies are really big, by the way. They actually can cover the vast majority of that $13,000 cost of a battery.

Kristin Hayes: Okay. Well, that’s great. I like ending on a somewhat optimistic note that there are, in fact, ways that that gap can be closed a little bit by some constructive policymaking. So, that’s great.

Dave, I think we’re at the end of our time, and I did want to close our discussion with our regular feature, Top of the Stack. What would you want to recommend to our listeners? It could be media of any type, whether on this topic or otherwise, that you’d want our listeners to consider.

David Brown: I’m going to give a nod actually, since I’m going to send an email to my students about a Common Resources blog post of RFF’s after this podcast, because I had a conversation with my students about the environmental implications of electric vehicles, and it just so happens that Joshua Linn and Daniel Shawhan posted a blog, and I found it a few days ago, on the benefits of electric vehicles for climate change, air pollution, and health. So, I’m going to fire that off to my students after this podcast, actually.

Kristin Hayes: Thanks, Dave. That’s very nice. Well, we’ll happily link back to our own content, too. Certainly, as noted at the beginning, your paper will be available as a working paper on the RFF website, and perhaps you could also share with us your—I don’t know if your other paper that you were referencing is actually available, as well, but we’d be happy to link to that, too, since it sounds very relevant to our discussion. We’ll make sure that we link to those items for our listeners to continue the learning.

David Brown: Great. Thank you very much. I appreciate it.

Kristin Hayes: Well, Dave, thanks so much and great to talk with you.

David Brown: Yeah—you, too.

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