 Ah, the days go on, the week goes on, here it is Wednesday, and you know what that means. Energy, the state of energy. Hawaii, the state of clean energy. And we have a special show today, me and my co-host, John Cole. Hi, John. Hey, Tate. And we set the show up with McKenna Kaufman. She's from U Hero, which we're going to ask her to define that in a minute. But I'm going to tell you now, she's a co-director of the Energy Policy and Planning Group at U Hero at UH Minoa. Thank you for coming on the show, McKenna. Thanks for having me. Yeah. U Hero is the University of Hawaii Economic Research Organization. Yeah, so what is it? It is researchers who are really interested in policy analysis. So we, it is within the Social Science Research Institute, and it sort of attracts research fellows from throughout the College of Social Sciences, people who really want to work on policy, economic policy. You know, it sounds like, you know, to me the University is like divided up two parts. Maybe that's too simplistic, but one part is the part that folds in on itself, you know, that you never hear about. It's behind the Ivy Tower, you know, up here in Minoa. And the other part that's dedicated to helping the community that comes out, and if I had to make a wild guess, I would guess that U Hero is part of the part that comes out. Am I right? Well, I'm not sure about the two-part analysis, but I definitely think that U Hero is, if that analysis is correct, then U Hero is part of the part that comes out. Okay. So what do you do for the community? Well, basically we do economic research looking at how do we get to our clean energy transition, what are the trade-offs involved, and what are the best choices we can make, right? This is hard. Yes. This is hard. I hope they pay you really well for that. Never mind. So give us an example of the kind of problem, the kind of research, the kind of analysis that you do in the planning and policy group. Well, one of the things I wanted to point out is sort of this long relationship between U Hero and the Hawaii Natural Energy Institute, John's organization, H&EI. I think many years ago we sort of sat down and said we really need to marry the engineering side of things with the economics and policy and make sure we're talking to one another in order to understand what some of these trade-offs might be. So in 2009, we sort of kicked off this collaborative relationship where we've been developing models that talk to each other. So engineering models talking to economic models, and we've been able to sort of build capacity to answer some of the state's questions. So some of the things we've looked at are what are the economic and greenhouse gas. We also have some environmental components in there. So greenhouse gas emissions are measured as well. So what are the economic and greenhouse gas impacts of, say, laying an undersea cable and building a large wind project, which is something that the governor has recently sort of resurrected as an idea, as well as what would be sort of the economic impacts of bringing in liquefied natural gas for the power sector? What are the greenhouse gas implications of that? He hasn't resurrected that one just yet. No. But these are the kinds of, you know, analyses we try to get out there into the public domain. So to answer your question, what do we do? We put it all on our website. We make it all very publicly available. Try to write briefs in, you know, very plain language that people can really get their hands around in order to understand if we do this, what are some of the implications, right? Well, what's your website? Uh, uhero.hawaii.edu. And then you can go to the link that's energy policy and planning group and all of our working papers are there. A working paper is research, result. Does it have conclusions and recommendations too? Yes. Ooh, this is really important for government. You know, because, I mean, university can, you do huge benefit by making affirmative recommendations one way or the other. You know, and sometimes people don't do that because, you know, it's definitely intosinal. But if you do that, if you make recommendations, then you're really making a gift, you know, to the rest of us. So HNEI, how does HNEI get married, you know, to uhero? How does that work? Do they come and ask you questions or do you come and tell them what their analysis should be? How does that work? Well, HNEI has been doing electric system modeling for quite a while now. Basically, looking at the electric system and if you make certain changes to it, you know, what are the implications on the technology side? How will the system behave? And what are, you know, some of the high-level costs of making those changes and the benefits that might accrue from, you know, making those costs, whether it's having more renewables on the system and how you can make the system stable doing that. But we come up with pricing or not pricing as in how much people pay for electricity, but how much it costs the utility to run their system under different scenarios. And we can kind of come up with, you know, benchmarks that'll say, you know, if a mitigation can beat this cost, then it'll be worth putting in. But we've never, we, those models don't have deep economic analyses. And I think McKenna's group can use a lot of the data that we use in those models as far as how the system runs and how, you know, dispatch of things change and what those costs are and go a lot deeper and look at some of the implications of those, you know, cost changes and how they affect. So you want this? The energy system, the customers, and even the statewide economy. It's like feedback. Yeah. It's like you're testing your system against the economic analysis to make sure you're not flying off into space with high science. Right. It's another piece. I mean, we're looking mostly at the technical side and some high-level cost stuff and they bring it further, you know, down the economics line to be able to look at customer impacts and actual costs and statewide impacts. So you've heard it, McKenna. You've heard it from John. How much of what John has said, do you agree with? You know, we're working on this together. 100%, right? I'd be a little scared to say 110%. Right. One of the things we're working on now, as John said, is, you know, working with their really, really detailed production cost model. You know, we're using the outputs of their production cost model, particularly different scenarios about curtailment of renewable energy. That means there's huge times of the day that renewable energy is being discarded because we have flat rate pricing. Right. So right now there's no incentive for consumers to respond to, you know, times of the day when renewable energy may be more plentiful than others. Right. So one of the things we're looking at is taking their estimates of what are the times of the day that renewable energy is currently being discarded. What happens if we went with some, you know, radically different pricing mechanisms? Something work into real-time pricing or dynamic pricing? How might consumers respond? What's the opportunity for load shifting as a result of that? And so then we're able, in theory, we're going to be able to, because this is something we're working on right now, feed that back into the protection cost model and see, well, then how would that change? It's iterative. It's iterative. You're going to tell them, thanks for that system, that it, and maybe you should tweak the system because then the result, when we make our iterative analysis, will be better. Mm-hmm. And then in the end, the hope is that we move, we say, okay, if this is, you know, what we think is an idealized pathway to, or, you know, a couple of pathways to get to our renewable portfolio standard goals, then we can take that and say, okay, what's the economic impact of that? Right. What do we think that's going to do in terms of changing, you know, electricity prices, how does that affect the economy, the changing sort of shift away from fossil fuels and towards, you know, solar uptake and wind energy uptake, how does that affect the economy? So it sounds like there's two parts to what you do. I think I'm getting, one part is you're going to look at the pricing, see if it works. Mm-hmm. Two is you're going to look at, look at that whole ball of wax and see how it affects other things in the economy, the economy in general. Absolutely. Which we really need to know from you because everything, you know, but what is the ideal you're seeking? A better economy? A stable economy? An economy with more this and more that or less this or less that? What is your ideal economy that you're working toward? Well, okay, so I think that's a really good question and probably one for sort of the bigger policy question of what is, what's the motivation behind the RPS laws, right? And probably there's multiple motivations. So in the law itself is written that, you know, it has to be cost effective, right? So what does that mean? So part of our job is to help interpret multiple things that that could mean, right? Cost of effective against what baseline? Cost of effective against other greenhouse gas mitigation strategies? Assuming that greenhouse gas mitigation is also a driver of this, right? So, you know, and if I were to define what this means for everybody, I'd say it's how do we reduce greenhouse gas emissions at least cost? Okay, I heard something recently I'd like to throw out at you. Reducing emissions here in Hawaii as a function of how the world is going to deal with climate change? Zero effect. It's so infinitesimal that it really doesn't mean anything. So you're putting a value judgment on it, really, is what you're doing. We want to do the right thing. So my question, I always wanted to ask an economist this question, how much is the right thing worth in American dollars? Well, okay, backing up a little bit. So there is definitely a value judgment, right? But there's a value judgment in any policy that's put forward, right? We say the RPS law should be put forward that has sort of lots of value judgment in it, right? So why we do it, you know, matters. In terms of greenhouse gas emission reduction specifically is one of the motivating factors, right? Besides being cost reduction factors. I think that, yes, that's absolutely a value judgment we should do it. In terms of Hawaii's contribution, Hawaii is a really small state. So yes, it's small. But our per capita emissions are just like everybody else in the U.S., right? So on a per capita basis, we have sort of the same, you know, moral imperative that the rest of the country does. And if every small state said we're not going to do anything, well, that's a pretty quick way to do nothing, right? In terms of emissions compared to other countries, in Hawaii we're, you know, orders of magnitude larger. So our emissions are about similar to the U.S. average. What about the economy in general in terms of helping people live better lives? Helping people who are unemployed become employed. Helping people who have jobs make more money in their jobs. Helping people who pay too much for this or that or the other thing pay less. I mean, are you interested in that? Let's look at the economy. Absolutely, right? That's the portion about driving down electricity rates, right? There's lots of research in Hawaii and across the world saying that high electricity rates, high energy costs really can cripple an economy, right? So if you want... On the other flip side of that economy, with ample, cheap energy is going to be a better economy. I mean, I'm sure that's part of all the principles of economics these days. Right, absolutely. And it's an interesting thing actually because, you know, if you look at our electric sector, it's really small as a relative portion of the overall economy. I think it's roughly 2% of the overall economy. But compared to other sectors of the same magnitude, it's far more important because it goes into everything, right? So because it has this sort of aggregate impact across the economy, it ends up being a much larger sector than it is just sort of in value. Sure, it's a leverage thing. Right. More than most other things. Well, John, you've been listening to McKenna. And I just wondered how much of what she has been saying you agree with. Oh, all of it, definitely. That needs 100% part. Yeah. I mean, I'm definitely no expert on macroeconomics and, you know, state-wide impacts of things. So that's why I think the relationships we have is important. So the relationship I've had is through... It's an iterative relationship. However, you know, you're the data. You're the system. You're actually feeding the economist McKenna data. And she is saying, well, you know, let's fix that and tune that. And let me ask you some questions about the data so I really understand what's happening. Right. So it's a... I don't want to make it vertical, but you're providing data up to her. She's doing policy on your data, in effect. Yeah. I mean, she's running her analyses on a lot of its data that we help provide or get for her through, you know, our modeling and relationship with the utilities and stuff. But like she's taking it a step further than we do. We're looking at the system itself and the costs within the system and how those might fluctuate. And she's taking it and going a step further. Like, for instance, she was talking about pricing and how it can affect customers' behavior. And she tells us that, you know, a certain pricing scheme might change behavior and make the load look different during the day. Then we can go back and put that into our model and see what effects that has on the system itself and how it might change, how we mitigate or get more renewables on or other things about the system that, you know, will end up with us putting out different results. And again, like you said, it's an iterative process. We can go back and forth and kind of fine-tune things. These shows also are iterative. What I mean is, every 14 minutes we take a break. That's iteration. We're going to take one now. Watch this. Boop! You're watching Think Tech Hawaii, offering lifelong learning from passionate hosts and fascinating guests ready to explore and explain Hawaii's place in the 21st century. Great content for Hawaii from Think Tech. Good afternoon. Howard Wiig, CodeGreenThinkTechHawaii.com. I appear on Mondays at 3 o'clock and my gig is energy efficiency doing more with less. It's the most cost-effective way that we in Hawaii are going to achieve 100% clean energy by the year 2045. I look forward to being with you. Aloha. Hello, I'm Marianne Sasaki. Welcome to Think Tech Hawaii, where some of the most interesting conversations in Honolulu go on. I have a show on Wednesdays from one to two called Life in the Law, where we discuss legal issues, politics, governmental topics, and a whole host of issues. I hope you'll join me. I love that kid. Who is that kid? Never mind. We have McKenna Kaufman here from U Hero. She's the co-director of the Energy Policy and Planning group there at U Hero. And we have John Cole. John Cole, former commissioner of the PUC and now a principal of HNEI, the Natural Energy Institute doing valuable things. And these guys, somewhere along the line, figured out if they got together and did iterative planning between HNEI and U Hero, we'd have a better look, especially at things like, what is it, a time of use charges on electricity? Wow, this is really big because everybody's on board for it, you know what I mean? They are. I think so. Well, knock wood, yeah, knock wood. Assuming they are, you know, if we're ready as a group, as a community to do this, this can be huge. And you're right in the middle of it, but how do you figure out how you're going to incentivize, push, pull, de-incentivize, what you have to do in order to get the desired result? I mean, this is the part of economics that's completely subjective. How do you handle that? So, on our website, we actually have a study that looks at what do we think would be the sort of order of magnitude effects of adopting what was at the time the utilities proposed time of use rates, and it's since sort of the docket has since been ruled on, and the utility has been given the go ahead to actually implement the program, right? So, the idea is it's going to be a voluntary program to start where your rates are going to vary throughout the day. What was before this sort of having three time buckets, one that covers most of the workday and high sun hours, one that's your peak, right? And the idea is that you really want to shave that peak from residential load because that's when you have really costly generation, right? And then throughout the night. And the pricing over the peak is very expensive. And in the middle of the day and in the nighttime, it's much cheaper. It varies by island, varies by electricity system. Oh, yeah. So, when you shake it and make it, though, you're going to provide the pricing that is necessary to shave those peaks to make a flat line out of it, yeah? Well, so this was with the utility's proposal and what they sort of have to move on is to shave the peaks as well as basically trying to do a better job at matching the supply of renewable energy, right? So, if you think about solar, that's in the high sun hours, 10 to 5-ish. Wind is more consistent throughout the day. But trying to match those times of day... So, you know what you want. You know the curve you want to get. Roughly. It's a little bit... You know that if you raise the price of energy at a given time, you're going to, according to the human condition, then people will use less of it. Right. And if you reduce the price of energy, then hopefully people will do the reverse and use more of it. So, how much less and how much more and how do you find out? Is there a magic box that you can talk to? We made a magic box. What we did was we looked at what happened in other places when they launched these programs. So, we took sort of our best estimates from what happened in other places and figured out which ones, you know, applied to Hawaii situations, which ones didn't, and used those estimates to give a proxy for what we think could happen here. This is social engineering. It really is. You're taking certain factors from a place that you think is comparable. Right. And you're hoping by your analysis that when you think it's comparable, it really is comparable. Yes. So, ex post analysis is critical, too. So, what we, you know, basically, in the end of the day, time of use rates in terms of block pricing is a stepping stone. There's a couple of stepping stones, right? From an economics perspective, you know, what would constitute real efficiency would be something akin to real-time pricing. Because, you know, even though the sun, you know, high sun hours really clouds roll over these things, you know. So, what you would really want is real-time pricing where you'd be sending those price signals instantaneously or close to instantaneously. But that's, I think, you know, we have to take a few steps before we can get there. So, this time of use block rate structure is one of those steps. That's so interesting, though. Can we dwell on that for a minute? Just for a minute. You know, so the cloud comes over. You want to hold your flat line. You don't want that cloud to affect, you know, to change the curve. You want people to react immediately. So, I can just see a guy walking down the street and his cell phone beeps at him. And it says, you know, special sale on electricity. You can buy it now. And he goes to the other app on his cell phone and he says, turn on my appliances on. I got a special sale on electricity. Turn them all on. And that sends a message to his house. And now his house has got all the apps running. Doing the laundry automatically, whatnot. Is this doable, China? Is this doable? Ideally, yes. And ideally, you would even cut out the app and the phone and it would happen automatically. Sign up for that special program. There's going to be people trying to sell those kinds of products one day. But that would be the most efficient. Those products are in development right now. This is what I just described. Yeah, absolutely. So, I mean, it's a few years off, but it's definitely in the works, right? That's why I think time of use rates are that sort of stepping stone is because time of use rates give people some certainty, right? It's not... There's less uncertainty. Let me go back to my double app thing. And it's not to suggest, let me put the two apps together. Now, it's automated. Neither of them is actually on my phone. They all live in some computer somewhere. In the cloud, if you will. And it's going to say, well, we need to have more people doing their laundry right now, so we're going to have more laundry done. We're going to make all the laundry happen right now. And I have signed up for that. I have checked the box and signed my name, and I want this, and, you know, I don't want this crashing on it. I just want it to happen. But if everybody does that, you're not doing any more social engineering. The machine is doing it. Do we still need you, McKenna? I'm okay with that. All right. We got down to the bedrock. That means all the solutions have been solved. The problems have been solved. There's all these solutions. Isn't that the logical conclusion of your work together? That when you find out what these principles are, then you supplant the machine for, you know, the human foible. Yeah, and I think, I mean, so getting away from the machine as she goes to bed. Sign it. Really sort of thinking about what is that interaction between available technologies, future technologies, and how people are going to react to them, and what are the incentive structures to help that or not, right? Is really where this is all sitting, right? So right now we have a set of constraints about technology and what we think could happen. What do you mean by that one constraint? I mean, sort of, you know, I think battery technology costs, right? They're coming down really rapidly, but it's still, you know, on the expensive side. And then the constraints aren't just technology. It's also, you know, the social and physical constraints. You know, who has the space to install batteries in their garages, right? Who has the space and the autonomy to do that? So that's huge, right? It's a moving target. I mean, your assumptions have to be constantly changing. Next week, next week, there's something that changes it. Or something happens in the science that changes it. Yep, definitely. I mean, another thing is the system needs to keep up with all these things we're talking about. It's been a little slow in that regard. So I mean, that's part of the problems we're facing now. What do you mean by that? I mean, the electric system and how it can handle the changes. I mean, it was built to work a certain way, and now things are changing so much. I mean, traditionally it's been, they generate the electricity to meet people's use of it, to the demand whenever they want to use it. And now it's kind of flipping, whereas we're trying to make the use of energy fit the supply more, you know, when it's being produced, like solar energy. It's just helpful to make our listeners understand what you're working on. I want you to describe it. Yes, okay, these are slides that describe it. We didn't actually call them up when we were talking about it. So this is kind of a slide that sort of describes we use HNEI's work in production cost models, and we feed that to our electricity demand model. And we're able to then estimate load shifting potential, right? And then we sort of feed that back, this iterative process back to HNEI. And then ultimately when we've iterated enough and we think we have some reasonable scenarios, then we'll put it up to our economy-wide model, which is called HCGE, which then we can study the economic impacts of these really high-level renewable energies. My reaction is this is great. You're going to be busy doing this for your whole career, and you're going to be better and better. But two time factor questions I want to put to you guys. Why didn't this happen before? What was holding us back from the marriage you're talking about and this kind of iterative analysis that we can do now, are doing now, why didn't we do this five years ago? What was holding us back? Well, we've actually been doing it for about seven years, right? Well, that completely destroys my question, but that's okay. But maybe it's a matter of we need to get the word out. Okay, next question, and this is another time factor question. So right now you've described a pretty sophisticated way of looking at things. In my view, that comports with all the whirling nervous items that are in motion in energy. So it's hard to think that that's going to stay still. It's not going to stay still. So where is this all going to go in terms of the perception of the systems, the development design of the systems, and then the process you have working between the two of you to achieve the optimal result? Where do you see your relationship going in the future? Next five years. And don't tell me you've already done that. John? We haven't done the future yet, but like McKenna said, just continuing this process to try to look at the best way to run the system in getting to the policy goals that we want to meet. And that's both on the technical side and the economic side. So I think the relationship is important to have the feedback between them. What are your constraints now? I mean, if make me king, and I say to you, what do you need? I'll give you anything you want in order to develop this science and this kind of analysis to the best it can possibly be. What constrains you? What can I do to make it easy for you? Money? You want money? Just ask. Well, some of it's the cost of the technologies that are needed to go the way we want to go. Like McKenna mentioned, battery costs. We know it works, but it's expensive right now and we're not real sure about the life or how long it lasts and how often you're going to have to replace batteries, for instance. You told her about graphene. It's things like that. You told her about graphene. Oh, I've heard about that. You know about graphene. I don't know. New kinds of batteries coming down the pipe. If that comes down the pipe, it might solve all our problems. But I mean, those types of things that we don't really have control over and we don't quite know how it's going to shake out yet, but we see trends. So I mean, we kind of, that's what we're looking at to try to figure out where to go. Yeah, we all have to look at it. We all have to stay informed and you have to keep coming back to think tank. But let me offer you, you wanted to know before what light to look at. It's that red light over there, McKenna, and I'm offering you the opportunity to close now and tell them whatever you want them to take away from this discussion. What would you tell them to take away from this discussion? So there's some great research going on at the university. And I think one of the things that John and I wanted to get across today is, you know, we've been really working on thinking about how the engineering side and the economics and policy work together. But also what I think is so important in this building off what John just said is, you know, we really don't have any kind of crystal ball. We're trying to build an understanding of what are the trade-offs with what we know and what we can reasonably expect, right? So that's why I think we do a lot of scenario analysis, right? If it's this pathway or this pathway or this pathway, then these are the decisions you need to make now and these are the trade-offs in the future. And so if we can give insights into that for decision-making, that's our goal. This makes economics more interesting than you ever thought it would be. No kidding. This is terrific. What are you guys doing? John, you want to close? Say bye. Thanks a lot for having me again, Jay. And maybe we'll see you again next week. Okay. Maybe we will. Thank you, McKenna. Thank you for having me.