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How will electricity prices evolve as power systems become more complex, renewable generation grows, and new technologies reshape grid operations? In this episode of Frequency Band, hosts Paul Dockery and Becky Robinson are joined by Jacob Mays and Erik Ela to explore the future of price formation in electricity markets.
Jacob and Erik are two leading experts in power system economics and market design. Jacob Mays is an Assistant Professor at Cornell University, new member of the California ISO Market Surveillance Committee, and electricity market design researcher. Erik Ela is a technical expert who advises organizations around the world, including EPRI and ESIG, on grid operators and power system engineering and economics. They bring valuable insights into ongoing debates around wholesale electricity prices and the market signals that guide investment and operations.
Creators and Guests
What is Frequency Band?
From Fort Peck, Montana to Tijuana, Mexico, the alternating current transmission network in the West oscillates in the narrow band of narrow band 59.97 and 60.02 cycles per second. On Frequency Band, the California ISO will host industry experts to talk about how the physics, economics, and governance of the grid come together to to keep the system in sync.
I'm Paul Dockery.
Becky Robinson:And I'm Becky Robinson.
Paul Dockery:On Frequency Band, Becky and I explore the physics, economics, and governance of the grid from a system operator's perspective.
Becky Robinson:On today's show, we're talking about the future of price formation in spot markets for electricity. Then Paul has a new game for us to try, and we close as usual with frequent awards. Here to share wisdom and expertise is doctor Eric Ela. Eric works on the future of electricity markets as an expert consultant for E cig, the Energy System Integration Group, and consulting technical executive at EPRI, the Electric Power Research Institute. Welcome, Eric.
Eric Ela:Yep. Hi, everyone. Thanks, Becky and Paul, for the, opportunity to join you on the newest and hippest podcast on electric power that I'm aware of. So very excited for today.
Paul Dockery:You didn't need the caveat. You could just say it's the hippest and newest.
Eric Ela:Just generally. Yeah.
Paul Dockery:Just generally. It's super hip. Super fun. Okay. Thank you.
Becky Robinson:We take compliments.
Paul Dockery:Yes. We do.
Becky Robinson:Alright. Great. Great to have you, Eric. We also have professor Jacob Mays Jacob is an assistant professor at Cornell University, where his research group focuses on the design and analysis of electricity markets. Welcome, Jacob.
Jacob Mays:Thanks for having me. I'm I'm I'm not gonna, you know, disrespect my colleagues at NISO with by calling this the hippest, but I I will, say for sure that this is the newest electricity podcast.
Paul Dockery:I mean, okay. But it's okay to have some friendly rivalry with New York ISO. The other ISO, feel like that's healthy. It's healthy competition in the podcast space for system operators. Jacob, you are a new member of the Market Surveillance Committee.
Paul Dockery:This episode comes out right after an episode with Scott Harvey and Ben Hobbs whose shoes you fill. How do you feel about that?
Jacob Mays:I'm excited. We just started in the last couple of weeks, and it's been fun already to start to learn about all the issues in Kaiser.
Paul Dockery:Well, as Becky previewed, we are discussing the future of prices in electricity markets. There's at least some level of debate I've gathered from our prep conversations and the general discourse on all of the, social media platforms that there is a debate about what prices will look like on the grids that operate with a combination of zero marginal cost resources like wind and solar, and opportunity cost based resources like storage, hydro, and batteries. In order to, like, set up our conversation with two experts who have thought a lot about this topic and have gone deep into what this could look like, I'm gonna start with just a mental model of, how I visualize the supply curve. So the way I think about it is a supply, supply demand cost curves. And I visualize our supply curves as like this staircase.
Paul Dockery:The staircase isn't regular necessarily. It's not standard heights and widths for every step. But there is like irregular but gradual steps as it goes from more efficient power plants with low marginal cost to less efficient power plants that are more expensive to operate. And my visualization is at the end of the staircase, there is the ladder to the attic. The ladder has these, not very often used, rarely used, but very expensive resources.
Paul Dockery:And then what we've done as we've added wind and solar to the mix is we've effectively added a basement. And the basement, you can think of it as either there's a really big step from the basement to the rest of the staircase, or maybe there's an elevator that goes from the basement to the bottom of the staircase. That's my visualization of like how we've been evolving this. And one way to think about the future that we're gonna talk about is that we're replacing basically all the stairs in the staircase with an elevator. And the elevator will then go from the basement to maybe the ladder to the attic or it'll just go all the way to the attic.
Paul Dockery:Maybe one or the other. Eric, I'm gonna start with you. You've researched this topic. You've written about it, and been part of the debate, I believe. What do you think about the analogy and what is your current state of thinking as it comes to the debate about what the future of prices looks like?
Eric Ela:Yeah, I really like that analogy, Paul. I think that's one we can start using and, you know, think about exactly what, you know, those different heights are and how do you get from one to the other. But yeah, it's just several years ago I worked on a paper. It had, you know, ISO market design experts as co authors. It had some other, you know, thought leaders as co authors, and, you know, I was kind of the lead author, which meant I was the facilitator.
Eric Ela:I kind of wasn't supposed to have an opinion, so I was just there to kind of, you know, listen to some of the folks. You know, there was really strong views on on you know, very smart people that usually agree with, you know, a lot of things, and, you know, we sort of got into this debate of, you know, that the prices will essentially jump in that elevator, you know, back and forth to the basement. You have a $0 price, and then you have a scarcity condition because all of a sudden the the wind and solar is gone, you ran out of energy from the batteries, and you'd have, you know, shoot up to a thousand dollars or $2,000, and then go back down again. And and even, you know, I've seen those supply curves written in that way. But then the other group was just, yeah, it's it's gonna look, you know, pretty similar to what we did today.
Eric Ela:Not exactly the same because there's a lot of different things. There's a lot of different costs that are driving this, but, you know, it's not really going to make that much of a difference and start talking about, you know, why that is. In reality, they just had different assumptions, I think, right? So, you know, you think about short term. You know, if you add wind and solar to a particular hour that didn't have it before, the price is obviously gonna go down.
Eric Ela:Right? You're adding a zero cost resource that's, gonna, you know, make the marginal cost resource that sets the price. By the way, we're talking wholesale energy prices. I think it's good to clear that. We're not really getting into retail.
Eric Ela:Maybe we can get into that later, but, wholesale energy prices. You know, and we see that, Kaiso midday prices. Right? We've got a lot of solar, and the prices are are low. In the spring, they're low, you know, at noontime, and that's, know, a 100% because we have solar on the system, right?
Eric Ela:And that's just it. But long term, it's much more tricky, right, because you've got, you know, so many other things happening. You've got, you know, retirements happening. You've got forecasts uncertainty. You've got shortage conditions that you aren't quite sure how they're gonna happen.
Eric Ela:You've got transmission congestion that becomes more complicated. There's non optimal decisions being made. So yes, I love the staircase, versus elevator analogy. I think, you know, will a system that's approaching this one hundred percent zero fuel cost resources on an on an annual energy basis, is it gonna have an an elevator supply curve or just a slightly different set of unequal floors, right, with with the stairs? Right?
Eric Ela:And if it's a ladder and and, you know, I guess, spoiler alert, that's that's my view. I think it is the ladder, the the the different stairs. I don't think the elevators
Paul Dockery:Not the ladder to the attic. The ladder as in it continues with the irregular stairs.
Eric Ela:Multiple, you know, multiple floors, you know, that yeah, or I guess you're right. There could be a a it's a step to the top, so they might not be as wide. Anyway, we can get into more of that, but it's I think the question there is what's going to drive the height of those floors.
Paul Dockery:Okay.
Eric Ela:Right? So if there isn't this natural heat rate fuel cost, you know, driver, like the combined cycle floor, the combustion turbine floor, there's no fuel costs to anchor these floors, then, you know, how do you define those floors? Yeah. Right? So I think that's kind of the question that we can get into.
Paul Dockery:So Jacob, I think you've also been part of these debates. Are we are we characterizing the debates right? That there are, like, these two camps of the way things way people think prices will form? And then where do you fall?
Jacob Mays:Yeah. Well, I I think that's basically right, and I like the analogy. I think the the the main thing I would add is that the the way we teach that analogy and the way that people visualize that analogy is it's like a single snapshot. It's like a five minute period, and you're thinking about the supply curve just in that five minute period. And so what's missing is all the intertemporal aspects, especially with storage and non convexities and ramping and the thermal units and things like that, that really complicate what that progression of stairs looks like in reality, if you were able to get the dynamic aspects in there.
Jacob Mays:But I think, you know, by and large, I'm on the same side as Eric in thinking that it's not going to be just all or nothing, zero zero or scarcity. There's, first of all, we're going to have thermal resources on the system for quite a while, but then also we're going to have storage and other things bidding at values that are between zero and a thousand or 2,000 or whatever the cap is in given market, and that'll have a end up with a supply curve that looks closer today to today's than it does to an elevator. I think it's probably the case, and this is borne out in places like Australia and New Zealand and etcetera, that the volatility is likely to increase. So there's more of the low prices and the high prices, but maybe not much of an effect on the average price. But, So that does imply a little bit of a different shape to the supply curve.
Jacob Mays:Even that, you know, it depends on exactly how much storage we're talking about and how much demand response we're talking about and things like that. So that's some of what Eric was getting to with the, you know, debating about how exactly how how high those floors are and how long those floors are and how smooth the curves are.
Paul Dockery:You're saying that storage resources are gonna keep bidding and they will not bid at the cap or the floor. They'll end up disciplining prices somewhere in the middle. Why do you think that? I'm I'm I'm curious about that logic and where storage will fall in all this.
Jacob Mays:Well, so storage will fall based on what it what it views on in any given day or any given hour or any given five minute period. The the operators of storage resources are making an estimate on what the future prices are going
Paul Dockery:to look
Jacob Mays:like and maybe adjusting for some round trip efficiency losses, maybe adjusting for some degradation, but they're basically looking at their intertemporal opportunity costs, which means how much would we make if we held on to our energy and sell that later versus selling it now. And so when they're making those estimates, they're they're taking in, you know, forecasts of what the demand is gonna be, forecasts of what the solar and wind output is gonna be, you know, all these other factors, transmission congestion that might affect how much price what prices will look like, and and then maybe doing some sort of risk adjustment and then putting in an offer and a bid on how much they're willing to sell electricity for and how much they're willing to buy, to charge. And so that's, you know, that's going to change on a day to day, hour to hour basis based on what their, you know, views of what the prices are going to be. And, you know, there's there's a lot of situations where, you know, we don't know exactly. Maybe there's some potential for scarcity.
Jacob Mays:We might have a reserve shortfall or or a penalty of some a penalty price of some kind, but we don't know. So if that's gonna have some chance of happening at 7PM, then a battery at 3PM is going to be pricing into their offer. You know, what's the probability that that we're going to hit that that, that that spike at 7PM and put in an offer that's above zero, but, you know, not not a full scarcity price because it's not guaranteed to happen.
Becky Robinson:And I saw an interesting chart on that. It was a paper from a couple years ago. I think it was American Clean Power Association that put it out, but it was it was looking at the ERCOT market and looking at sort of what what percentage of storage is bidding basically at the cap versus less than something, you know, some I think their their cap was $5,000 that they were looking at, you know, sort of the $10 range at the top. Right? So so what percentage is bidding less than the 4,990, if if I'm remembering right?
Becky Robinson:And, it was a significant chunk. Right? I mean, it it and it shows significant chunks on both, but I think it's something like 30% was bidding less than sort of that at the cap basically bidding, which I thought was really interesting. Right? To your to your point and your question, Paul, of of and to Jacob's point of, like, what do we expect people to just sort of, you know, with these resources that are looking to capture those price spikes?
Becky Robinson:Are they all just gonna sit at the cap or is there gonna be some in between and and how are they coming up with those numbers? So I thought that was an interesting, data point for even on a system of today or a system of a couple years ago, and and now they have way more storage than they did in whenever that study was a couple years old. So, it'd be interesting to think about how that's progressed.
Paul Dockery:So that makes the mental models way more complicated than my simple staircase, because now we're talking about like the escalator has a changing amount of, gradient depending on how much wind and solar there are in the system. It's terrible analogy. We're gonna cut this out in post. It does does start teasing out the question of, okay, I think, Eric, I think you started talking about these like long term prices versus short run prices. The way we think about planning, for the resource mix is using, like, production cost models, which I think basically use my mental model.
Paul Dockery:What are the other ways we think about prices other than just a staircase that is, to Jacob's point, a snapshot in time? And what what are good and what are cautionary tales from following those modeling techniques for the future?
Eric Ela:Yeah. And I I think it's exactly what Jacob was talking about. So, know, I I I've had, you know, I've run these, you know, there's lots of production cost models out there, tools, research tools, and they, you know, essentially replicate the ISO market model, right, as as close as they can. So they're doing economic dispatch and commitment, and sometimes they're building in, you know, the forecast error that happens between a day ahead time frame and in real time, and then they, you know, will calculate LMPs for the entire so I've had, you know, times where we've put in, because you can do whatever you want, Put in the inputs and see what happens, and we put in hundred percent zero fuel cost systems to say, okay, what happens? You know, what do the prices look like?
Eric Ela:And and they do turn out to be, in in the traditional commercial tools, zero or price cap. And it's it's exactly because it does not really capture the thing that Jacob was talking about. And, you know, more generally talking speaking of what it doesn't capture is the bidding behavior. Right? So it's representing, thermal resources by putting in a heat rate and a fuel cost, right?
Eric Ela:And so it's hard enough to try to, you know, figure out what and some try to do this, and I think, you know, some can do it okay, but it's it's very hard to to replicate, you know, the human in the loop strategizing what, you know, these bids are gonna be. So it's hard to do that for a thermal resource. Doing that for something like storage that has really no starting point for all of that is is extremely difficult. Right? And then, you know, we'll put in, okay, if you're gonna have this 100% clean energy system, there has to be a large amount of, you know, responsive demand, including, you know, retail customers.
Eric Ela:And so when you're running a model like that, what do you put in for what their, you know, price responsiveness is? You know, like, you got me. I mean, that's a that's a big like, we've we we can't even figure out the value of lost load. Think about, like, what the cost is on a whole curve for for flexible demand. So but then with storage, right, you don't have these are deterministic models, generally speaking.
Eric Ela:So you're putting in, you know, when the storage is setting the price, it's basically the opportunity cost of the, you know, the known price that the model has. It doesn't have the the possibility that, you know, in reality, like Jacob was saying, you know, there's a x percent chance that there's going to be a scarcity condition at, you know, 7PM when the sun goes down. And so that percentage is, you know, going to reflect their their offer to supply energy at, you know, 5PM or 4PM. But in the tools, there's not an easy way to do that. And we've tried ways, but it's not super easy.
Eric Ela:So they show this, you know, big elevator outcome in the simulation results, and you know, so in reality that's not the case. The capacity expansion models are another way that you've seen some of these results, and I think there the issue is that's sort of putting everything all in together at once, and by definition, it's creating this optimal system that's going to have, you know, prices that come out of it that are going to justify all the investments, you know, essentially perfectly. Right? Because it's building these in the model because it needs them. So, you know, the the duals, we'll see how many times we, use that term, but, you know, we'll come out to basically justify all that.
Eric Ela:You won't you don't get, you know, negative prices. You don't get zero prices really that much, and and you you don't get scarcity prices in those conditions. So, you know, across those two tools, I think there's, you know, this missing reality that we need to think about just in what, you know, the fact that we're not going to have an optimal perfect system and that, you know, trying to model what these, you know, agents are going to be representing as what their offer is is difficult to do. Not impossible to do, but difficult to do.
Paul Dockery:Jacob, one of somebody in your group, Caleb Smith, put out a recent paper talking about different ways to model the future systems and did some work about thinking about production costs that take into account some of the sincerity. You wanna push on this a little bit and figure and carry us forward into what these systems need to do in the future, the way to think about it?
Jacob Mays:Sure. So I think I you know, like Eric was saying, if you take the standard tools that are available, they're using a deterministic model. They might have different assumptions about what the forecast error is in the day ahead time period. But in any production cost model, you have to specify exactly how frequently you're doing commitments, what is the the forecast error when you do that, and then how do you follow that up with economic dispatch, models that are producing these prices, what's the frequency, what's the look ahead period that you use in in your model, And, there's all sorts of, you know, modeling decisions that you have to make that affect the the value of the results that you get in or get out. So the the the quality of the price forecasts or the price distributions or or what have you.
Jacob Mays:So what we did at that paper was to to compare a few different, methods. First, a capacity expansion model, then, you know, different, deterministic models with perfect foresight where you can kind of see the future and you know when the scarcity is going to occur. Deterministic models with imperfect foresight where you're, you're wrong about exactly how much wind and solar and demand there's going to be. And then a stochastic program where you have a bunch of scenarios and and, you're solving in in each hour a program that's kind of reflecting the uncertainty in the in the wind, solar, and load. And, and that kind of embeds some of those opportunity costs, that storage bidding behavior, if you get the stochastic program, and you and you have a good characterization of the uncertainty in the system.
Jacob Mays:So, so that's one way of, you know, trying to go about getting those opportunity costs into the price formation process as it's represented in a in a production cost model. You know, there's another class of, you know, agent based simulation type type methods where you try to have a a you know, at at each hour, you have the the agents in the system, the batteries or or or what have you, solving their own model and kind of implicitly getting their own prediction of what the prices might be and feeding that back into a bidding bidding tool that then goes into a market clearing logic in the production cost model. And so that's another way of going about it. But to your point, I think, you know, it's not just that opportunity cost calculation, it's also modeling complexity increases with the duration of storage that you're considering or the look ahead period that you have to consider how far out into the future you're having to consider. The kind of the the best example of this might be in the in the large hydro systems, say, Brazil, New Zealand, etcetera, where they're looking at ten years, and you're modeling out, you know, what are the what's what's the the rainflow conditions going to look like long term?
Jacob Mays:How does that affect the reservoirs? And how does that affect the opportunity costs of using some of our stored water to produce energy now? And, you know, nobody in The US, you know, has to do quite that duration yet. But but, you know, the the more storage we have on the system, the the more of an impulse there is to those longer look aheads.
Becky Robinson:So, Jacob, can we unpack that a little bit? So I I I'd love listening to you talk, but you and you said a lot of things there, but where you started, I think, you were talking about trying out different types of, scenarios or tests in in these studies. And so thinking about deterministic models, both with perfect foresight and imperfect foresight. So I'll I'll translate that to where you where you know exactly where the uncertainty what the uncertainty is gonna be, and you're right, you're correct about it, versus with imperfect foresight, maybe you're wrong, right? You see, you're still sort of targeting something very specific, but that turns out to be wrong versus stochastic models where you introduce some randomness to it, if that's a fair way to describe that.
Becky Robinson:And I guess, and then you were talking about how that embeds opportunity costs. So can you say more, like what are these different things? How are these different setups, playing out? Like what do you see as you as you compare these things to each other? And what do we learn about opportunity costs there?
Jacob Mays:Sure. So, the distinction is between the, so in a deterministic program with an imperfect foresight, this is where we make a forecast, but it's going to end up being wrong. But we make decisions on the basis of that incorrect forecast. And so our commitment decisions, our placement of storage are going to be suboptimal, going to be a little bit off because we're making them with the, you know, to the best of our ability with the knowledge we have available, we're making them on that basis. And then in a stochastic program, we're saying, okay, here's, you know, 10 possible views of the future.
Jacob Mays:Trajectories for wind and solar and load, all of those are correlated in, you know, their own complicated ways and we're trying to make a decision at present that is going to be best positioning the system to respond to any one of those 10 scenarios. And so then there's kind of a probabilistic calculation at work where the decision to discharge from storage or the decision to charge into storage is trying to position the system in a way that's, you know, hedging against those 10 possibilities and kind of implicitly calculating, and I'll talk about dual values again, you can use the dual values and say, okay, well, there's an opportunity cost implicit in there, and that's what's driving the storage behavior that comes out of a stochastic program like this.
Paul Dockery:I have to warn everyone, we're gonna play a game later, and the use of duals is gonna penalize you in the game we play. So you we we do need to bring these duels down to a concept that the rest of our audience can understand, at least for the game, that we're gonna play in the future. But I wanna I wanna tease out a little bit because the staircase is in a lot of ways the deterministic look at the future. And I think what I read Caleb in Caleb's paper, Cabe Smith, and we'll put the paper in the show notes, he I think he if I recalling this right, and I think you were, you were a co author, Jacob, so correct me. Those production cost model that that we started this show with had the most volatility in its outcome for the prices that it came up with.
Paul Dockery:And when you started doing more of the stochastic programming, there was a less volatile price outputs of that model simulation. Do I remember that right? And what does that mean for our mental model about the future? Instead of thinking of it as staircase, what should we how else can we think about it?
Jacob Mays:Well, so I I think it it depended on the scenario or how they they ranked in terms of volatility, but it it did, it is the case that, you know, intuitively, we think that there are these situations where if you have, let's say, a reserve shortfall penalty that's $750 per megawatt hour, then and there's some probability that you're going to have a reserve shortfall in the near future, then a deterministic model will either give you zero or seven fifty. Like, if you happen to live in the scarce situation, land in the scarce situation, then you get seven fifty. Otherwise, you get to zero. But in a in a stochastic model, in every hour, the batteries will be implicitly saying, okay, what's the probability that we get that $7.50? And be setting a price that's somewhere between 0 and $7.50 based on their assessment of the probability.
Paul Dockery:Okay. Eric, you got to come in here and help us because I think we are correctly identifying the difference in these models. We haven't got to a replacement for my staircase yet. And I I I need somebody to help me with I'm a visual learner. I need a different way of thinking about this.
Paul Dockery:Do you have something, Eric? Come save
Eric Ela:us. Us. Matt, should I waste some of my my game, answers for no. It's not a waste.
Paul Dockery:It's not a waste. You just use it.
Eric Ela:I can reuse them. Okay. Yeah. So this just a way of thinking about this in in yeah. I I try to think of this because we're talking a lot about storage.
Eric Ela:Right? It seems like that storage is kind of the big thing. And, the the analogy that I think, you know, sort of like the first time I thought about and first time we really saw it, it sort of like it was like an moment is how storage links prices across time almost the same way as transmission links prices across space. Right? So if you don't have any transmission constraints, let's just ignore losses.
Eric Ela:If you don't have any transmission constraints, right, there's enough transmission, then the prices across the entire system are the same. If you don't have any storage state of charge constraints, the prices across all hours are the same. Right? So they're linking prices across those hours. That means that you can have a scarcity price in an hour that doesn't have scarcity because of the scarcity happening later, or you can have a price that's zero when there's no curtailment of renewables because that curtailment's happening later, right, in this deterministic view.
Eric Ela:Right? And by the way, the other I don't know if hopefully, this is a different way to think of it, but the another important part of that is how are these markets going, like, the structure going to differ, right, because we have day ahead and real time markets today, and a lot of that was put in place because of startup times of of thermal generators. We call it a security constrained unit commitment. You know, that term isn't even appropriate on this potentially future system. We've been calling it the SCSO, security constrained storage optimization.
Eric Ela:Might be what the new market model is called. But there's no uncertainty in the settlement of that day ahead system if it's, you know, sort of the same as it is today because, you know, the price that happens there is deterministic. It's going to happen. Right? So when you clear the day ahead market and there is a high price at 7PM, that high price is guaranteed to the storage resource that is being cleared at 4PM because it's optimizing across the day.
Eric Ela:So the stochastic model is really between what's happening in day ahead in real time or what's happening when you don't have those future conditions built in to how the market's clearing for today. So and they have to internalize that in a way. Right? So So, anyway, I that one example of just, you know, kind of thinking about storage as a linking across prices just like transmission does for locations is just one I wanted to highlight. Then, you know, I think it's important to recognize that, you know, what the structure might be if we still have a twenty four hour day ahead market like we do today.
Eric Ela:That's going to to it's it's gonna have some differences compared to if you were, you know, literally just having a single real time market that's clearing hour by hour when a lot of that strategy has to, fall on the shoulders of the assets that are then thinking, I don't know what the price is going to be in the evening time. You know, yesterday it was really bad, but the day before, you know, nothing happened. It was just a normal price. They've got to factor that into their offers versus this, you know, optimized day ahead market where they can really rely on the market clearing to do that to some extent. So those are kind of two little other nuances to think about, and, yeah, I probably failed the explanation, cost on on some of that, but, just some other ways of thinking about this.
Paul Dockery:No. I really like that. I think just in in the same way my staircase fails under a transmission network where you have transmission constraints. It similarly fails under intertemporal constraints. So I think that's exact that was really helpful for me.
Paul Dockery:Thank you so much, Eric. I'm gonna move us and we we probably have, like, three more questions that I'm gonna go fairly quickly on. Eric, you started in some of your explanations about how, like the assumption that price responsive demand is going to have to be part of, price formation going forward. I hear this a lot. It seems like every book I've read from Schweppes' Spot Pricing Electricity to, all of the energy economics textbooks since all talk about price responsive demand as the solution to all of our problems.
Paul Dockery:Is that the same thing here? And how much of your mental model about the different floors that slowly go up is based on having price responsive demand?
Eric Ela:Yeah. I think it's a huge part of it. And, you know, I think, I mean, how storage and flexible demand would set the price and how they would sort of interact in the system is really at the crux of all this. Right? So I've heard from large, firsthand and secondhand, of, like, large industrial, you know, loads that, like, they don't understand how how they would bid.
Eric Ela:It it's very different. Like, so so one extreme is is cryptocurrency. Right? So they know that if the energy price is above $75 per megawatt hour, they will not make money on mining. Right?
Eric Ela:So they have, like, this really simple translation of energy price to the value that they receive from the energy. You kind of move to any other, you know, industrial process, and then, you know, once you get on to the retail side where every device has a different value, you know, it gets really complex, and and, you know, some of that is just different people have different, you know, different companies, different people, all will have different preferences. You know? Like, you should shut down the hot tub at the vacation condo before, you know, curtailing, you know, the hospital lights or, you know, Quitin, of course. And so, you know, all of that is factored in, but but how do you sort of translate that into, like, these prices is something I think still needs a bit of work to it because, you know, and some of that's just, you know, are they able to do that today?
Eric Ela:And we know that, you know, there's very few customers that actually have access or are are allowed access or choose access to wholesale price, or marginal cost pricing. Right? That that's reflective of that. We we we approximate it using time of use pricing and and things like that. But, you know, I really think that has to be a part of it.
Eric Ela:But then, you know, obviously, you know, you and I, when we're, you know, thinking about our you know, whether we want to charge our vehicles, you know, fully to 100% or to 80% that night, we're not going to be bidding into Kaiso's wholesale market. So we need the the load serving entities that are serving us to essentially try to approximate all that price responsiveness, you know, into the market so that it's properly reflected into these prices and into the way that we know what, you know, supply has to be available and what storage charge has to be available. So, yes, I think responsive demand is is is a major part of this. Yes. I think they have to be able to set prices at a much larger scale than we see today, which is very minimal, but I think there's still some trickiness into what that what that cost what what the willingness is to consume and how that gets formed into a bid and then formed into a price.
Becky Robinson:And Eric, you highlighted a couple different aspects of how demand can participate, and totally appreciate your point that, you know, this is an area where we need more work to be done to fig to figure out what this looks like for different people. I love the the Bitcoin example because I think it's intuitive of, you know, they're they're literally, like, doing what they're doing to earn money, and so, like, you know, electricity is a big input about that. It's a big cost into to their process, right? But I think but sort of backing up from that, I think there's kind of at least two ways, probably lots more, you know, that I that I think about how is demand participating at a price in our markets. And one of them is to think about when prices are very low.
Becky Robinson:Right? So we've got, storage on the grid. Storage wants to charge at very low prices, and so, so it can discharge at high prices and earn the arbitrage revenue. But, so storage will seek out when our price is low, and I will charge then. Right?
Becky Robinson:Because that is sort of central to their, their existence and and what they're doing on the grid. And so in that way, you know, we we see that there are, you know, a significant chunk of resources who are willing to to, to to move their demand around according to when our price is low. And and and in California, for instance, it's as has been mentioned on here, we often have low or even negative prices in the middle of the day. It's not uncommon. And so we have sort of typical patterns of of that kind of demand sort of absorbing, you know, cheap energy on the system.
Becky Robinson:But then there's another kind of price responsive demand that we would love to cultivate, see more of, which is when you think about prices being really high on the system. And so that's less about, well, I think with the low prices one is more about attracting more demand to those, intervals or those hours of really low prices. But then when the high prices on the system, it's like you want to know, what is a customer's willingness to pay to not consume. And so, that's probably obvious, but just, highlighting that because, you know, on the system that we operate, for instance, we, have a lot of storage and so have a lot of, demand that is, charging demand that is very tuned into prices. But that's very different, I think, than load or demand indicating price responsiveness, again, when prices could be very expensive to say, okay, yeah, you know, for that hot tub at my vacation condo that I'm not even at, turn that off first.
Becky Robinson:Now, you know, retail customers, obviously don't participate in markets directly, but but if you had a, you know, a wholesale customer, to think of like that kind of a electricity consumption pattern? What would you wanna cut off first, and what's what's willing to indicate sort of that price responsiveness?
Eric Ela:Yeah. Well, I'm trying to think about a few things. I'll start with a shout out to
Paul Dockery:my
Eric Ela:mom. I was there last week, and she is a great steward of the Calais So Grid. She's in San Diego. She always tells me when I'm there that she'll only do our laundry after midnight, so she's always helping out the grid. I think it changed, so she can do it between ten and ten and two now.
Eric Ela:But there's not that many people like her, but then, you know, she's also what I would call this open loop demand participant. Right? Which, you know, all retail customers are, but also a lot of large, including those like in Texas, the cryptocurrency. They're all what I call open loop. Another term folks might be more familiar with is price chaser.
Eric Ela:Right? So they see the price and then they react. And I think that has benefits, but at large scale, that can break down, right, because that's not going into the auction. It's not, you know, so we don't understand the balance between supply and demand as well, and we're not forming prices accurately. Right?
Eric Ela:So, I like to think of this closed loop, demand flexibility, which is responding to price and responding to essentially a dispatch schedule, but, you know, making sure that that willingness to respond is is is part of the, market clearing, part of price formation. Right? And and I think that that second part is really difficult because it's much easier just to sit back and respond than to have that extra responsibility of, you know, having to put in a bid, maybe being subject to, telemetry requirements and all those other things. So it's a really hard thing, but I think at that large scale of having, especially with, you know, we can't do a podcast without talking about data centers and large loads, and, you know, if if you have all of these these data centers potentially having flexibility or using their backup supply, it has to be in the market, not just after the market to really farm these prices. So, you know, then it gets back to that question of what is their willingness, you know, what's their price sensitivity, who's responsible for submitting that price sensitivity in the ISO markets, and then how's that going to to to, you know, change the the staircase?
Eric Ela:And and and will it make it very I I think it makes it very continuous because you have, you know, if you think about the renewable storage and, scarcity, that's like three steps. But if you think about every single, you know, load device, or or type of load out there, that's, you know, thousands and hundreds of thousands and millions that all will have, you know, potentially different price sensitive. We just don't really know what they are.
Paul Dockery:It's more like it's like building out the landing. Like, you're building out the floor of the house. So there's more people walking around at a lower price level on the staircase where I do it. But shout out to Eric's mom. Really good job.
Paul Dockery:Thank you for being a steward of the electric grid. I assume you're gonna share this podcast with her, and I appreciate that. So we've we really dove into the price responsive demand. I think it's critical as you're thinking about the future price formation. The other thing I think is critical about is thinking about hedging practices and making sure you have the right sort of we we Eric started out with this.
Paul Dockery:We're talking about wholesale prices, not retail prices, making sure you have the right institutional response to this price volatility that may or may not show up. I'm gonna put you on the spot, Jacob. Is the price volatility we're talking about here bad? Is it okay? And is it and how are hedging practices going to influence the future of spot price formation?
Jacob Mays:Well, it's it's a it's a great question. And I think, you know, in the from an analytical standpoint, you know, the volatility is neither good nor bad as such. But if if it is indeed getting larger, volatility is getting more important, then we we have this we've always had this trade off where when we're talking about, for example, engaging the demand side, one of the ways to do that is to send price signals to the demand side. And as Eric mentioned, you know, there's very few retail customers that are have exposure to the true costs that is in the wholesale system. And there's a cost to that because if you don't give anybody the incentive to respond to the low prices or the high prices, then, there's a lot less of the response to those prices and people aren't investing in the capability to manage their EV fleets smartly and do all sorts of other creative things to shift around demand.
Jacob Mays:So there's a cost to poor incentives, and the more you suppress the volatility at either the wholesale or the retail level or both, then the higher that cost goes. And the more kind of inherently volatile the system is because we have more renewables on the system pushing prices higher and lower, the higher that cost or that theoretical cost is to to not showing people kind of the true volatility in those in those costs. On the flip side, there's a cost of volatility, you know, and and risk risk isn't free. Investors respond to the risk in the system and, you know, demand a higher return on equity, etcetera. And, there might be a premium in the deals that retail electricity providers are willing to give to their customers because they're worried about their exposure to the wholesale prices, which are more volatile.
Jacob Mays:So there's a cost of volatility. And in some sense, lot of the debates we have about retail pricing in particular are trying to get that right trade off between what's the cost of volatility versus what's the cost of risk and the cost of sorry, the cost of poor incentives on one side and the cost of volatility and the need to hedge risk, etc. On the other side. I think that the more technical capability there is on the demand side to respond to prices and the more inherent volatility there is in the supply side, it seems certain that the cost of poor incentives is getting larger, is but it also seems that the cost of risk is also getting larger. And so that's, that's certainly a dominant factor in the in the way we think about the the future evolution of wholesale and retail markets, I think.
Becky Robinson:I love this, the the setup of cost of volatility versus cost of poor incentives. And are you, when you say poor incentives, are you thinking about the resources themselves? Like once they have a hedging, contract, then they are on the hook if they don't produce. And so the fact of them having a hedging contract, are you thinking I want to make sure am I tracking right or are you thinking about something different? But in a way it seems like by having that hedging instrument, you've given that supply resource better incentives to perform.
Becky Robinson:But is that what you're getting at or are sending something
Jacob Mays:That's that could be one aspect of it, but what I was thinking is so the best example might be things like your typical retail customer on a flat rate tariff. So I'm I'm in New York. Have a flat rate tariff where I live, and so there's nothing in my bill that tells me I should use electricity at one time a day versus the other. And that's true for for a lot of retail electricity customers. And if there were a way for me to say, hey, I don't want to actually charge my electric vehicle when the when the price goes high, I would rather charge it when I know that's, you know, there's more wind, in Upstate New York or something like that, and so I know it's it's cleaner or it's lower cost.
Jacob Mays:I don't really have an easy way to do that as a retail customer in in New York. So that's, I think, a poor incentive being sent to me. And there's a cost to that because I'm not going to take advantage of the potential flexibility that's latent in my electric vehicle or, you know, other devices I have around the house. You know, I think with on the supply side, I'd say the cost of poor incentives are lower because wholesale markets do a decent job most of the time of of, you know, providing good incentives to to perform. But even there, we we do still have debates about, you know, capacity performance penalties and whether those are incenting the right type of type of behavior in scarcity intervals and things like that.
Jacob Mays:So even on the supply side and at the wholesale level, there's still debates about, you know, how do we think about the cost of giving those really strong incentives to resources, and for that matter, the buyers at the wholesale level, versus to what degree do we need to protect them from a certain amount of the more extreme volatility that can arise in electricity markets.
Paul Dockery:And the cost of poor incentives for Jacob is very low, but the cost of poor incentives for a large load customer could be very high.
Jacob Mays:Could
Paul Dockery:be And very
Jacob Mays:also, if you think about, you know, if we're so in New York, we're trying to and I think it's the same in California, we're trying to electrify the vehicle fleet. Right. And if you think about my individual EV, it doesn't matter too much. But if you think about 20% of the load on the system, that becomes a big deal and a larger cost to having poor incentives on the management of EV charging or something like that.
Paul Dockery:Well framed. I'm gonna keep us moving. What so so Eric, you mentioned a paper earlier. I just wanna because I believe Mark Rothleader does listen to this podcast, I'm gonna say I believe he was a co author on that paper. Shout out to the California ISO's participation with the leading minds in the electric sector to help form the future of electric markets.
Paul Dockery:Well done. Thank you for your contributions as well, Eric, and for facilitating those conversations. But in a recent paper you wrote, I think the best way to close out our conversation about what the future price formation is, you wrote, are we talking about a quote large overhaul or incremental adjustments? How significant the design changes need to be to enable a market with nearly full supply of renewable energy. What do you think, Eric?
Paul Dockery:What's your takeaway? Are these large overhauls, how significant of design changes are needed?
Eric Ela:Yeah. And and, Paul, that's, this EASIG Market Vision paper. Actually, Jacob and I, were both, a part of that. You know, we kinda had this little rose chart, all the different areas, and then what was a major change in each of those areas. So price formation was was in that, and and I think everything that was there, I I still, believe today.
Eric Ela:And and also, like, it's interesting to see some of the things, you know, like the discussions in PJM and elsewhere that are, you know, starting to talk about some, potential big changes. But price formation was was a very small incremental. The whole group kind of decided, hey. Like, you know, there's a lot of debates in Europe at this time about marginal cost pricing, and whether, you know, renewables should be getting the the price that was, you know, being set by, you know, every all the the fuel issues from the war in Ukraine, and we're like, no. We should keep all of this as it is.
Eric Ela:It's really working well. Locational prices work well. There might be some a few tweaks, like, I know Jacob will talk, you know, was very, had had a piece in this same report about, you know, what full strength pricing or full full strength, price formation was, full strength spot prices. Maybe we should be allowing those price caps to go higher to better reflect the value of loss load. The the major changes in this were were really around two things.
Eric Ela:You know, how we do hedging investments, and how we enable demand to participate in the markets. Those are the two things that had sort of the what we thought was the most opportunity to make some larger scale changes to how the markets work. And by the way, that might not be wholesale markets, that might not be like what an ISO has to do, it's just really kind of a whole big picture. You know, the states have to be involved, utilities, you know, etcetera, and some of that might be legislature, legislation changes, etcetera. But how do we get demand to participate?
Eric Ela:You know, how do we you know, I was gonna say fix, but I don't want to be too critical of, you know, capacity auctions and some of the things that, you know, obviously there's a lot of differences in how we do that even across The US, but just ensuring that that's not interfering with all this useful thing, all the useful stuff that we have and how we set the energy prices. We want it to be kind of more of a supplemental way of, you know what we're scared of is having, you know, suppliers rely on emergency, you know, headlines to make up all their money. That's sort of the scary part that people don't want to happen. We don't want winter storm Uri, you know, where, you know, unfortunately people lost their lives to be the event that, you know, these suppliers have to wait on to make all their money. That said, we we do wanna try to build in all these great incentives for how prices are to to really, be the main driver of of all of, you know, location incentives, flexibility incentives, and and investment incentives to be a part of that.
Eric Ela:So, something has to to to probably, be improved there, and and, maybe it's just improving how, loads are hedging, against these price spikes and and things like that, and and also, you know, how do we sort of reflect that some loads you can curtail, and that's okay. Some loads, maybe you can't. So that's that's the other, thing, and I think some of the thoughts around this differential reliability are really interesting and something I, you know, I think really is just another way of saying get more flexible demands in there. But, yeah. So I think those are probably the two major things that we need to keep working on.
Eric Ela:I wouldn't touch LMPs, you know, much more anymore. I think how energy markets work and how we've done over the years, I think it's still working great, and I think it still will work great under a system that has, you know, approaching a hundred percent zero fuel cost resources.
Paul Dockery:Great. Thanks, Eric. And I do wanna shout out. So the first paper we talked about had Mark Rothleader as a coauthor. I believe Sylvie Spewack was a contributor to the second paper in the E SIG working group that came up with that.
Paul Dockery:And Jacob, you were a co author on that paper. Which of those is your sweet spot about how you think about markets? How they may they need to change in order to meet the future?
Jacob Mays:Well, I think I basically agree with Eric. You know, I think that the the price formation, spot price formation is an evolution, not a revolution. There's things that are happening. We're, you know, adapting ancillary service products that affects the way that prices are formed in the the spot market. We're obviously creating some new participation models and things like that and we're, so there is some evolution in spot price formation, but basically the same LMP system, you know, works in these hypothetical future systems.
Jacob Mays:So the much larger changes and really this is borne out in some of the debates we're having today in PJM and elsewhere is about resource adequacy and long term investment and how to how to manage that, and then on on how to incorporate the demand side in in a more efficient way than we've done in the past.
Paul Dockery:Great. Anything you'd add, Becky? Where's your head at on this conversation?
Becky Robinson:I like the conversation we've had, how, you know, the the sort of bimodal assumption of of pricing or potential, you know, is wondering if the potential outcome is this bimodal, distribution of prices, whether either around zero or they're really high. I like that as the, you know, bit of a straw man or a foil. But yeah, I mean, seems like our, you know, experience now, which I realize we're talking about systems of the future, But, but even now, you know, when we look at the, you know, the California ISOs markets, seeing those zero or even negative prices in the middle of the day. But we have, you know, other resources that are setting the price later in the day. And you know, kinds of resources still on the grid, of course, as well as, you know, we're interconnected with the rest of the West, right?
Becky Robinson:So lots of different, so even more types of resources across the broader regional footprint as we think about, you know, how are, how is power flowing from one region to another, one state to another and so forth. So I like the the points we've raised here today about, there's lots of things that impact these prices, both in sort of the for the technical and, technical level of, you know, non convexities and that sort of thing, as well as, I'll call it, you know, geographical practical, you know, the, from the world we live in today perspective. So, I think it's definitely an area of interest to watch, you know, as we think about, right, we've got work going on about how do we get, how do we get more demand participation in the markets? How do we, you know, encourage more of that flexibility? And what do we think about what price signals are needed, in the markets, especially in a very dynamic environment when things are changing, new things happening, around the around the West, and and how do we think of what what signals we need the system to be able to send, when things get tight around the system.
Becky Robinson:So definitely appreciate the conversation today with with all of you.
Paul Dockery:And I hope to bring this conversation back. I think future prices may be a recurring topic, because we didn't even talk about ancillary services and how they help inform spot price formation and where how that could change over time. I believe that's one of the areas where the California ISO has done a bunch of maybe leading edge work about how to do things like imbalance reserves and flexible ramping. We had a whole episode with some excellent experts about ancillary services, and maybe there's a way we can all come back. When we come back, I also want to play this brand new game.
Paul Dockery:So we're going to race through this game in hopefully about ten minutes. And it will make no sense to anyone except for hopefully us. And then maybe when we bring it back again, it'll make more sense the second time. I'm calling this game explanation optimization, where we are trying to find the the least mental work to explain this complicated topic of the future of electricity market price formation. We we run an auction at every node in the grid every five minutes.
Paul Dockery:That's part of what electric systems and electric markets do. We're gonna try to run this explanation optimization in two segments in five minutes. My explanation probably needs to take less than a minute. I'm a little worried about this. I'm gonna slow down, take a deep breath.
Paul Dockery:Is everybody's mental, like, space ready for this? Because this is a complicated game. I was warned that this was too complicated a game for this podcast. Is everybody ready for
Eric Ela:like how you said that we can repeat this again, so if it doesn't work, we'll then have a lot of practice for the next one.
Paul Dockery:That's exactly right. Okay. So an auction has three parts. At least the three parts I'm gonna use in an auction is bid submission, then clearing, and then settlement. So we're gonna run this game in three segments.
Paul Dockery:Okay? Love a good board game with multiple steps in it. That's what we're doing here. The first is our bid submission. And instead of bids worth price and volumes, we are going to submit concepts.
Paul Dockery:So they're the concepts, hopefully we've already circled around in the conversation today. Each of you will be able to submit like, hey, this concept I think is the lowest mental cost, lowest wonkiness, like lowest cost for my mental model, for my mind, but has the greatest explanatory value. But you're just submitting the concepts in the first round. In the second round is when we clear. And we're gonna do this together.
Paul Dockery:It's gonna be both competitive and collaborative because obviously you're submitting the idea. So you'll get the infra marginal rent on how good of an idea it was. But we're gonna work together to rank these ideas on are they really are they where do they fall on our wonkiness rubric? Is it a really easy thing to understand or really complicated? And then how much explanatory value does it have towards explaining the future of prices?
Paul Dockery:We're gonna do that together. And then we're gonna clear the actual clearing, which is me setting actually the administrative, demand for this explanation is somewhere on this. And it has to be arbitrary because I can't do, more unresponsive, demand for this concept. It's it's, it's too much to ask in the ten minutes we have left. Okay.
Paul Dockery:And third and lastly, we'll end as always with frequent awards, and that'll be our settlements where we decide who did the best job of explaining this. And so if you're watching on YouTube, I'm gonna have producer Koliadich put the image of what this looks like for a staircase of understanding, which is our supply curve. I did a bunch of work. Also thanks to Sylvie Spiwack for helping me come up with all of these things. We have our wonkiness rubric, which I shared with the guests in advance of this, which is the same wonkiness rubric we used with our members of the Market Surveillance Committee.
Paul Dockery:So, that's how we'll score these ideas. Okay. Now I'm gonna set the clock. This was too much explanation. I was warned this would be too complicated.
Paul Dockery:Hey, friends of the pod. Hey, just send us a just tell tell people that you liked us even if you didn't because this is too exciting for me. Okay. I'm starting it. You got five minutes to submit your concepts.
Paul Dockery:Who wants to go first? What's the best concept? Becky, gotta just talk. Go. Yeah.
Becky Robinson:Okay. Okay. So my concept is infra marginal rent and why you care about it. It's how you earn money in the energy market, and more specifically, how you get a return of and on your investment. We'll assume away a lot of things, and and assume that at a general level if you're an electricity market participant.
Becky Robinson:So what it literally is is the difference between your cost or your bid and the clearing price. And so in in each of those intervals where your bid is less than the clearing price, that that difference is that inframarginal rent, and that's your sort of, you can think of it as what goes in your pocket, you're taking home, and and, you know, what you can think about as a way of, was my investment a good bet, right, over time as we see how much infra marginal rent do you earn. So connecting it to the conversation we had today, if you think about zero marginal cost resources like VERS, if they are producing energy in an interval where a gas resource, say, is setting price, based on its fuel cost and so forth, then that, you know, then that renewable resource is, is earning revenue in the energy market. And and contrast that with if there's an interval where you've got lots of VERs bidding zero and you've got a clearing price of zero because that's that's the the LMP at that moment, there's no infra marginal rent there. And so these, you know, these are what in the language of FERC, you know, would call market based rates, different than cost based rates.
Becky Robinson:Right? In some intervals, you're you're making more money than you than your marginal cost is for that interval. Why is that okay? That's okay because markets bring incentives and highlight what I think is a is a great point that a profit motive can be a beautiful thing for society, because it incentivizes people to come up with better ways of doing things. How can I make energy more cheaply?
Becky Robinson:How can I, make this unit more efficient to produce more? And, in a market setting, you get to reap the benefit for that kind of innovation, as well as bigger kinds of innovation, right, of, just what's the new technology that we don't even have on the grid today, but that we might need and that can do things better than we do it today, and a market is a setup where you can, you know, you've got people, incentivized and and getting rewarded for that kind of behavior of coming up with new and better ways of doing things, which I think we'll need a lot more of in the future.
Paul Dockery:I love it. And I love your strategy to, like, corner this market by forcing out all the other submissions because we're running out of clock time. Eric, Jacob, you got two minutes left. Submit your concepts.
Jacob Mays:Okay. Well, I'll go. So I'll I'll I'm just gonna build off of Becky's, which I know is not quite the spirit of the game, but I'm gonna submit incentive compatibility. And the reason why is because, you know, a lot of the debates we've mentioned about, okay, well, maybe we could have an alternative system for price formation. And the problem is moving away from LMP and moving away from that system of infra marginal rents that Becky was just talking about promotes bad behavior by market participants.
Jacob Mays:So incentive compatibility is the idea that we want to design a market so that market participants will tell the truth about what their capabilities are, what their costs are, how they value things, what their true value of what their usage of electricity is, what their true cost to produce electricity, etc. And the best way to do that without introducing a lot of opportunities for gaming, etcetera, is through spot pricing, the way we've done it with LMP. And, and so, incentive compatibility is my first nominee.
Paul Dockery:Okay. Eric, what do you got? So let
Eric Ela:me I'll use opportunity cost because that's a big thing we talked about, particularly with storage, because we use it in everyday life, right? You know, when I try to hire, you know, I think about whether I want to do the floors in the new room, or if I should hire someone, I think about, hey, it's going to take me three months to do this, but I'm not paying for it because it's my own, you know, labor. But, you know, I can hire this one guy who does it really well. It's going to look much better and I will save all that time because then I can go maybe do some other things including work on things that I know what to do and earn revenue from that. So opportunity costs are, you know, they're real, they're important, and, you know, they're part of everyday life.
Eric Ela:It's it's something that, is a big part of how we make decisions.
Paul Dockery:Okay. This is our first time running this auction, So we are gonna extend the bid submission window by a couple more minutes. You know, first time. You gotta you gotta sometimes you gotta keep the window open for bid submissions. Excellent job we have.
Paul Dockery:Infra marginal rent, incentive compatibility, opportunity costs. What other concepts do we wanna submit to understand the future of prices? I decided as the auction, the administer of the auction that I'm not gonna submit concepts that would be incentive incompatible. That'd be putting my thumb on the scale. Who else has a concept they wanna submit?
Becky Robinson:I only came up with one.
Paul Dockery:We're gonna cut that out. Jacob, Eric, give me another concept.
Jacob Mays:We're going in reverse order for the second round?
Paul Dockery:Go for it. You could use Becky's strategy of like forcing everybody else's, like, quartering the market by
Becky Robinson:I thought you were supposed to have to do this too, Paul.
Paul Dockery:Yeah. Well, I'm yeah. That was and then I realized that's a bad that's bad. Who else has one?
Jacob Mays:Okay. So I'll I'll I'll submit risk aversion. So I I it it builds off of opportunity cost in the sense that is and particularly if we're if we're thinking about storage, which which we talked about earlier in the conversation, storage is developing its bids based on the future price distribution that it thinks is going to arise. And part of that's an exercise in stochastic modeling, trying to understand what the uncertainty is. But then also part of it is understanding, you know, what's my exposure if I have sold forward on a contract and I can no longer meet it or something in that regard.
Jacob Mays:And so you want to understand not just kind of the expected value of what everything looks like, but also what's what's the worst that could happen or what's the what's the downside. And that's going to affect the way, people construct their bids and offers. And then it comes into play, maybe even to an a greater extent, in the long term investment sphere where opportunity cost is also important. Opportunity cost represents the that's what the weighted average cost of capital is because investors are deciding, do I invest in a power plant or do I invest in a power plant in a different market or do I invest in some entirely different segment of the economy? That's an opportunity cost.
Jacob Mays:And what they're evaluating is the risk adjusted return of the assets that they're investing in. So risk aversion becomes critical to understand when you're trying to estimate or evaluate those, those types of decisions.
Paul Dockery:Love it. Good submission. Risk aversion. Eric, do have another one?
Eric Ela:I guess I could do demand flexibility real quickly, because that's an important one that could make the cut in the auction, but you know, I guess just the explanation is just as a customer, how much am I willing to spend to get the product? And you know, to use these real world examples that are always helpful to understand electricity, I think the airline, you know, because everybody a lot of people have flown on airplanes. It's some way to think about. So there's there's kind of two forms of that demand flexibility or demand response. There's, you know, hey, my buddy's having a party, next weekend, and I'm trying to figure out if I want you know, buy a plane ticket to go to it because I don't have to go to it.
Eric Ela:And I'm gonna look and see what the price is. And I kind of have this, you know, in my mind, this bid that, you know, if the price is over $200 for a round trip, I'm gonna call them up and say, I can't come. You know, maybe next time. I just you know, I don't wanna spend I don't have that many money to spend. But then there's also sort of this demand response side that is the airplane is short on supply.
Eric Ela:They overbooked. They don't have enough seats. And, you know, rather than involuntarily curtailing one of the customers by telling them, sorry, you can't go to, you know, your grandmother's funeral or, you know, a work trip that's happening. We we don't have enough space for you. We're gonna allow someone else to get paid more than what they paid for the, ticket that they bought in order to, not have involuntary curtailment, and that person that individual is happy, everyone's happy, and, we balance supply and demand.
Paul Dockery:Great. I think so far we've got a great setup here. I'm putting them on a screen over here. We'll have it on the the actual screen for those watching on YouTube. But so far I've got infra marginal rent, incentive compatibility, opportunity costs, risk aversion, and demand flexibility.
Paul Dockery:Because I want some margin here, I'm gonna do the procurement backstop. From what I heard in our conversation, we also have a concept of full strength spot prices. We're gonna put that on the boards just as a procurement backstop. It's a good concept. We should probably have it.
Paul Dockery:I also heard security considering storage optimization, which I thought was a great concept that I'd love. We're gonna, Procurement Backstop, we're gonna put that just in case we need that concept. I also thought two concepts that are, were paired but are different, that also Procurement Backstop we're gonna bring in, the cost of risk and the cost of poor incentives, that closes even the extended bid submission window. You can probably hear that going on in the background. Okay.
Paul Dockery:So then I feel like we've got our bid submission. I actually feel pretty good about how how full our bid stack is at this point. How are you guys feeling so far? I love
Jacob Mays:Yeah, backstop procurement.
Paul Dockery:Okay. I felt like we needed that there at the
Jacob Mays:Yeah, end the that administrative element, it's important.
Eric Ela:Or is that operator intervention? I don't know.
Paul Dockery:Maybe one or the other, you know? We needed it though. What we demonstrated it is it was valuable. So on the next part of this, we're gonna clear the auction. So I'm gonna rely on all of us to be competitive but collaborative, and we need to now rank in our staircase of understanding from most explainable, from most from least wonky, to most wonky is is our cost threshold here.
Paul Dockery:And remember for those that haven't listened to the podcast yet, we have a wonkiness rubric. We'll share it again in the podcast, hopefully, if you're watching on YouTube. But a one is a kitchen table issue. A two on the wonkiness rubric is at the library. You just need it's a matter of doing the homework.
Paul Dockery:A three is in the regulator's office. Somewhere in a 400 page filing, a footnote is doing all the work. The four is a Market Surveillance Committee, wonkiness level. There's a working paper and a draft opinion. We've got our star Jacob Mays here, the newest member of one of the two new members on the Market Surveillance Committee.
Paul Dockery:And then lastly, the wonkiness rating five is dual space. You're off wandering around with Lagrange multipliers. Okay. So where in these concepts, which one do you think is the lowest most of a kitchen table issue? Who wants to go first?
Paul Dockery:Help me out here. I need help. This is collaborative.
Becky Robinson:So I really liked Eric, your ex your use of the flight overbooking example for demand flexibility and making supply and demand, the supply and demand curves meet. Because I feel like we've all, you know, this very tangible example, you know, we've all been there, you know, where, yeah, like they've sold to, although less recently for me at least. But yeah, where they come around, they're like, well, you take $200 to not go on this flight, to go on a later flight, dollars 300, dollars 400, right? They're looking for volunteers. And I think, that's a great sort of analogy and in a way to kind of bring home, what are we looking for demand flexibility to do?
Becky Robinson:Like you said, you know, some people, are, have, have travel plans that are, you know, if you're going to a funeral, a later flight may not work, for, for the whole point of traveling. Whereas other people, you know, it's like, sure, I'll, you know, I'm I'm on my way to vacation and I'll get there a little bit later and now I'll have some extra money or I can take another vacation another time, because of the the voucher I get. So I think that was very understandable.
Paul Dockery:We think that's a one. We think that's a one. Maybe that's a kitchen table issue, demand flexibility. Is that the start of our staircase of understanding? Or do you is there a different not that
Eric Ela:wide though, right? Yeah.
Jacob Mays:It's it's it's good. Yeah. It's not a lot of explanatory power, but it's a kitchen table issue. It's it's, you know, well articulated and and we can put it at the bottom of the the bid stack here.
Paul Dockery:Okay. Bottom of the bid stack. It's a it's a, like, a 5% width. Okay? 10% width.
Paul Dockery:Okay. Half a post it note width for those watching on the YouTube. Okay. What's next then? What's the next thing that's least cost?
Eric Ela:I mean, I think Becky's was had a lot in it. Right? So I think that's, you know, just infra marginal rent, like, just the importance of why marginal cost pricing exists.
Paul Dockery:Infra marginal rent? Is that a full width explanatory value? And is it a two? Is it, like, not not a kitchen table issue, but maybe at the library?
Jacob Mays:Yeah. I think I put it above opportunity cost. Opportunity cost is is a little bit jargony, but like Eric said, it's it's something that it exists all over the place. People understand that trade offs exist. And so I would put opportunity cost below it in the bid stack.
Jacob Mays:But yeah, it has an awful lot of explanatory value.
Paul Dockery:So you think opportunity cost is a bunch of explanatory value and is lower wonkiness? You say no, higher wonkiness.
Jacob Mays:I was saying opportunity cost is actually lower wonkiness than infra I marginal would probably go with infra marginal rents with more explanatory power.
Paul Dockery:Okay.
Jacob Mays:But opportunity costs, I think has a lot of explanatory power too.
Paul Dockery:Yeah, I agree with Jacob. Okay. So we're going demand flexibility. We're doing this. This is great.
Paul Dockery:Okay. And then we're putting opportunity costs next. Right?
Eric Ela:Right.
Paul Dockery:Lower wonkiness, but not as much explanatory value. So we're cutting a little bit off.
Eric Ela:All right.
Paul Dockery:Okay. So we're going there. That feels good. And then we're going in for marginal rent. A lot of explanatory value, but wonkier.
Paul Dockery:Is that right?
Eric Ela:Right.
Paul Dockery:Okay. See what I'm doing here? You guys got it? Okay. What comes next?
Paul Dockery:I've got incentive compatible, full strength spot prices, risk aversion, cost of risk, cost of poor incentives, incentive compatibility. Does that feel right?
Jacob Mays:Yeah. Yeah.
Paul Dockery:Incentive compatible feels it feels less wonky. We're at like a three on the scale. And explanatory value? Where how wide do you think incentive compatible explains? More or less
Jacob Mays:than
Paul Dockery:infra
Jacob Mays:rent? Well, I I mean, I kind of paired it paired it on purpose with with Becky's submission. And, you know, Becky talked about a bunch of other things, so maybe not quite as long as wide as, informational rents, but it's it's up there.
Paul Dockery:Okay. A little bit wonkier and a little bit less explanatory value than informational rent. That feel right? Sure. And then risk aversion feels right next or cost of risk and cost of poor incentives?
Jacob Mays:Yeah. They kind of go hand in hand there, I think.
Paul Dockery:Yeah. But I think you lost with the risk aversion feels wonkier than cost of risk. This was your idea, but I we had to backstop procure it because you want the wonkier one.
Jacob Mays:You know, you you substituted my market based offer with a cost based offer and it was
Paul Dockery:Bam. Just You gotta be careful. Yeah. Okay.
Jacob Mays:Market market power mitigation on my, my wonkiness here.
Paul Dockery:But how wide, how much does cost of risk and cost of poor incentive explain then? Do I put these like right next to each other? Is this already merit or not?
Jacob Mays:No, think, no, I think this is a, you get rid of the risk aversion submission. You you you you mitigated me and so and put in cost of risk. Cost of risk is, is, is taking its place.
Paul Dockery:Okay. And is this like super long then? Is this the cost of risk and cost of poor incentives is like a really nice long mid merit resource that's taken me a long way? Or how much explanatory value do these two things combined have?
Jacob Mays:I mean, I like it, especially for the long term investment type of understanding. It's a good pairing.
Paul Dockery:Okay. And then I feel like full strength spot prices, is this our ladder to the attic? Are security constrained storage optimization and full strength spot prices our ladder to the attic?
Eric Ela:Yeah, think storage optimization is kind of like, you know, hey, if it's a really peaky day, it'd be great to have that be part of this future explanation, but, you know, we probably don't need to get into that too much on your average explanation day.
Paul Dockery:Okay. Love this. So it's five. Absolutely. That's a dual space thing.
Paul Dockery:Okay. Full strength spot We'll put it at four. Just Yeah. Put it Okay. I mean, I feel like that's really we didn't even use incomplete markets and risk.
Paul Dockery:I feel like, we're just going to put that out here because I feel like I did have a bet with Becky that you'd bring it up, Jacob, and it's, and I guess I had to bring it up.
Jacob Mays:I will note for the record that Becky brought up non convexity. It was not me.
Paul Dockery:Yes. For the record. Okay. So we're clearing this. I we're gonna clear this.
Paul Dockery:I believe it feels right that the cost of poor incentives is the right spot to clear this on. That feels right, doesn't it? We're like, that's that's our explain that's where people want to stop with this explanation. That's the demand for this explanation, which means
Becky Robinson:If only we had an active demand side in this auction.
Paul Dockery:Only we had an could active listeners. Demand Okay. Ask Notes for next time. Ask the listeners maybe for future. Help us clear this demand curve.
Paul Dockery:Hey, actually, that's a good game. Let's clear this us help us clear this demand curve. Send us leave us a note. Send us a note where you think your demand for this explanation fell on our demand curve. I believe Koliatich will help us, and Stacy Gibbs.
Paul Dockery:And there's other people that will remember in the credits. I feel really good about that. Congratulations, everybody. I think that was a great explanation optimization. We're gonna take this from settling.
Paul Dockery:So the settling just means congratulations, you all won the auction, because you all cleared below the demand curve. And frankly, you set the marginal wonkiness and the esoteric value even though I had to substitute in the cost based one, which you did a better job explaining than I did. Okay. So we're gonna end with frequent awards. Markets runs end with market awards, and we end frequent Frequency Band with the segment we call Frequent Awards.
Paul Dockery:This may be the last time we end with frequent awards because, these are I'm running out of, of of kind of my discretionary ability to award these things. Okay. So Eric, I'm happy to present to you a General Electric time over current relay because Eric, I really appreciate how you kept us from blowing a fuse and really kept us staying within this narrow range of our operating range to make sure we had good concept, that we're well disciplined. Congratulations on winning this electromechanical relay. If you send me your address, you'll get this customized with a wonderful Frequency Band logo.
Paul Dockery:Eric, I hope you appreciate this award. Anything you'd like to say to close us out?
Eric Ela:Oh, man. What, what is left that we haven't said? I, really appreciate the, the time and, look forward to trying explanation optimization at least one more time and, with some of our tweaks built in for the next time. So, thank you Paul and thank you Becky.
Paul Dockery:Thank you for having us, or thank you for coming. We're gonna cut a lot of this. I've messed up a bunch of times. I hope you feel seen, heard, valued, appreciated, Eric. I really do appreciate the conversation.
Paul Dockery:Jacob, it's no it's no it's no secret. I really appreciate everything you bring to all of our conversations. I'm giving you because you bring you really differentiate topics for me in ways I find clear. So you're getting the transformer differential relay, which is a critical piece of infrastructure to make sure what goes in comes out and people have clarity of thought and the transformers don't blow up and, end up with, toxic gases that turn into bombs. Congratulations on the differential relay award.
Paul Dockery:Anything you'd like to say to close us out?
Jacob Mays:Thanks for having me. It's been a nice conversation, and I'm also looking forward to the next round of explanation optimization.
Paul Dockery:Nice. Thank you, Jacob. I hope you feel seen, heard by, and appreciated. Becky, don't have any words for you. Do you like the Frequent Awards?
Paul Dockery:I feel like it's a bit that this may be the end of this bit. It's a little bit, there's a burden to it.
Becky Robinson:I mean, I think it's fun. You know, like, you you you have these devices and and, artifacts and things, and I'm like, what what does this do? I, you know, I I get to learn a thing when you, like, tell me about a new a new award.
Paul Dockery:But Trust me. I could find all of so many devices. I could find so many devices. I'm gonna get a fuse at some point. Ugh.
Paul Dockery:It's just a lot of work, frankly. A lot of more work. Anything else you'd like to say to close it out, Becky?
Becky Robinson:Just thank you so much to our guests. This was a really fun conversation, and, I also really enjoyed getting to sift through all the papers that our guests today have have authored, coauthored, etcetera. It it was a it was a fun exploration. So yes, thank you all for for joining us today.
Paul Dockery:Thank you to our listeners. While you aren't seen or heard, you are valued and appreciated. Please like, subscribe, and share the show so other electricity market enthusiasts like us can find us. Roll on, enthusiasts. Roll on.
Becky Robinson:Frequency Band is a production of the California ISO. It is produced and directed by Paul Dockery Paul Koliatich and Jeremy Lipps with writing by Paul Dockery and Becky Robinson. It is mixed, edited, published by Paul Koliatich with graphics by Stacy Gibbs and Annabel DeGraff. Jamie Ackman is its editor in chief.
Paul Dockery:Its executive producers include Crystal Ball, Jacob Mays, Nicole Hughes, Aaron Bloom, Deborah Smith, Monica Gaddis, and Pam Sporboard.
Becky Robinson:The views expressed during today's recording are our own and not the official views of the California ISO or the organization of the guests also appearing on Frequency Band today. Any aggregation, quotation, or references to opinions shared in today's episode should be ascribed to the individual participants and not their respective organizations.
Paul Dockery:You can find additional information in today in the show notes of today's show, including where to subscribe. Frequency Band, celebrating the wonky charm of electricity markets.
Becky Robinson:Frequency band, staying in sync
Paul Dockery:at
Becky Robinson:60 hertz.