Which clean energy solutions are effective, and which need more work? Through explorations of household, city, and regional clean energy innovations and implementation efforts, Professor Daniel Kammen both analyzes successful innovation processes and identifies the areas that need urgent action and targeted programs. A mixture of analytic and empirical studies are used to explore what steps have worked and where dramatic new approaches are needed.
Full video and 3 questions with Kammen:
I’d like to thank Dan Kammen for coming to visit us here at MIT Energy Initiative. While he– this actually just testifies to his busy schedule. So I really want to thank him for coming this way, and it’s really an honor to have him. Actually, Dan quite humbly said he just wanted me to introduce him as Dan from UC Berkeley. But at the same time I have to add a little bit more, because for those of you who don’t know him by reputation, I think his achievements really testify to his authority and experience in this area. He is the chair– professor of energy at UC Berkeley. He’s the chair of the Energy and Resources Group. He founded the Renewable and Appropriate Energy Laboratory. He’s authored or coauthored 12 books, more than 300 publications. He’s testified 40 times to state and federal congressional hearings, and he was appointed by Hillary Clinton to be the first fellow for the Energy and Climate Partnership for the Americas. He was appointed by then Secretary of State, John Kerry, to be one of the first science envoys for the State Department. He very famously resigned last year from President Trump’s administration. He’s founded over 10 companies. Is your talk ready? It is ready, yes. [INAUDIBLE], thank you. The only thing I left off is that he plays backup point guard for the Golden State Warriors– Don’t I wish.
– -in his spare time. So I think that’s probably enough. I’ve probably embarrassed you enough. But thank you very much for coming. Let’s give a warm welcome. Thanks so much. I really appreciate it. [APPLAUSE] Well, thank you all for being here and for giving me the chance to speak. And, David, thanks for inviting me back out. We actually got connected when you were a Fulbright Nexus fellow, which is a pretty interesting program in itself. And it’s one of the many casualties of the current change. Am I touching something and making it– So once again, thank you all for being here. And I’m hoping that this is sort of an extended advertisement for some of the possible collaborations that myself and a number of colleagues at Berkeley, and at LBL, and at Stanford would be very interested to do. And so my lab is, by our standards, a pretty large operation. There’s people from Chile, and Nigeria, India, Navajo Nation, the developing country of Washington DC, a whole variety of places. And I’ll complain about particularly the last one today and talking about some of the things that we are engaged in. The lab website, which I think was on the poster, is here. And the Twitter feed is not Dan’s vacation pictures. It’s designed to be sort of one tweet a day about things going on in the lab. So anyone who wants to access the slides and things, I’d love to connect with you. A number of our students manage the dialogue that the lab has with others, and so you’re really welcome to connect on that. What I thought I would do is one bit, which I assume is sort of complete review, which is a particular clean energy take on where we are in the US at the moment. And then a short bit on what I would consider sort of a theoretical, kind of a traditional, way to think about innovation in the context of energy– to energy technology. It’s something that Jessica Trancik and I have had a long dialogue over over the years, and you will recognize many bits of it, including some of your own. But as I said, I’m not going to do this as kind of a deep burrow into one topic. I’m hoping that the parts in the beginning will highlight a couple of the areas where our lab is really interested in thinking about how to accelerate the clean energy transition. And I’ll use that kind of open the door to a next piece, which are some of the open access tools that we’ve been working on, both to think about power systems across scales, dramatically across scales, from sub-household level– I guess that’s a nanogrid, but not a term I use very often– to large scale power systems in the context of industrialized and developing economies. And then the last bit will hopefully have already bled out of this kind of open access piece. It’s some of the practical things that have come out of a number of efforts. Some of the companies that I would say we– they spun themselves off more than we sort of sat around thinking about how to push them out. And some of the collaboration opportunities that I’m hoping is something that we’ll talk about a fair amount, because it’s a pretty interesting time for all of the possible ways to be depressed about things. My own lab work takes place in two contexts. We have a physical laboratory that looks like a mixture of an electronics lab and kind of computing space, where all the students and the post-docs you kind of saw there work at least part of the time. But then we have some very large, and in some cases, very, very long-term international collaborations. We have a physical laboratory hub in Nairobi and a second one in East Malaysia, in Sabah, the capital– in Kota Kinabalu– the capital of Sabah, in Malaysian Borneo. And then we have a set of partnerships with several different Chinese universities, that will come up a couple of times, where we have a regular exchange of students. Finally, after years, a very active exchange of data. It took a while to get there. And, again, all of these things will come up in various ways during the comments. And since you guys start your talks later in the day than we would dare do for reasons of child pick-up and all, unless you guys have a strong desire not to do it, I am more than happy if people want to interrupt. Because I find that late in the day talks someone always has to go. And so if you want ask something, I am definitely not averse to jumping in. If it’s too difficult I’ll say, oh, that’s a good question for later on discussion and not answer it. But I would say, feel free to interrupt if there’s things that you want to hit. So let me start with the current context. And, again, I’m hoping this is just kind of a bit of a stage setter in terms of not just the motivation, but for some of the collaborations that work. And so this graph is one I hope that many of you have tattooed somewhere. So this is the business as usual path. And then the pink area defines, more or less, the commitments that countries made– not did, but have made– on paper, fueled by lots of good wine in Paris. So the pink is the INDCs that have now become the NDCs. So those are the national commitments. And one of the most interesting aspects of the science, of thinking about innovating for the low-carbon economy, is that for those who are experts in or students of the conference of the parties process, the Kyoto Protocol, the Copenhagen– whatever the hell happened to Copenhagen– the Paris Accords, essentially this is what countries said they would do, but in a language that essentially didn’t exist before Paris. There’s a little bit of argument and debate about that. But essentially, what’s captured in the national commitments that went into the pink are some countries said they were going to do very aggressive clean energy activities. Others said that they were going to play a large role in smart carbon and water efficient agriculture. Others said they were going to be engaged in forest conservation. So essentially, one of the jobs after Paris was for members of the IPCC– and how many people have done IPCC internal duty over the past years? So that’s a smaller percentage than I would have guessed, unless you guys are just slow with putting up the hands there. And so, essentially, the IPCC community had to translate these national statements into their carbon equivalent, and that’s not always easy when it has a mixture of mitigation and adaptation. But essentially, those commitments– not that they’ve happened yet– would cut roughly a degree off of the overall path. And so I’ll call those the NDCs and the Paris commitments. The big meeting post-Paris is the 2018 winter meeting in Poland where countries, in theory, have to come clean on how much they’ve gotten done on this. But now that we’re in this strange world in the US, it’s not even clear they’ll do that. But that’s what, in theory, is supposed to happen in Paris. And then that, of course, leaves this significant degree and a half, or more, that needs to then happen and for which we have lots of uncertain ideas and jostling views. And the one line I didn’t put on here– there it is– is roughly what came out of the post-Paris excitement, when countries agreed very quickly to the Kigali Accords– that are largely onto HFCs and CFCs– and it more or less cut off another half degree. And there is some question about the math for the out years. But, more or less, this took us from a path of commitments to having no commitments– the business as usual– to two degrees. And those who are really inside the process know that in the true Paris excitement one of the areas in which most of the world’s energy modeling groups ironically failed, i.e. were slower than the politicians, was that in Paris there was a significant movement towards the so-called High Ambition Coalition, saying two degrees is not even good enough, even though that will be challenging, but 1 and 1/2 degrees. And so that didn’t get codified. But there is a significant group, again, in the days prior to the US 2017 election, where there was a big discussion about that. And, of course, anyone can look at the graph and note that while two degrees is a Herculean task, the 1 and 1/2 degree calls for something– which I won’t get into hugely in this talk, but it’s another area of activity in our lab– and that is thinking about the carbon negative strategies, from the more calm ones, reforestation, degraded land efforts, to the more extreme ones, geoengineering, and all. But if the two degree line is here, this talk– this gets us into a conversation about carbon negative that has some hugely divergent views now. And we– you’ll see where we’re working on it, but I won’t spend a lot of time on it in this talk. And this is BECS and then the various geoengineering options. And in my view, in last kind of bit of preface for the story, is that essentially the surprise attack that China and the US– the two climate laggards arguably year after year after year in the climate process– came out and said, well, we’re roughly 40% of global emissions, 40% of energy use, and we are going to do a G-2 of climate. And this happened a year before Paris, and in my view, without what Obama and Xi did, there would be no Paris success. Because this opens the door, in my view, to these very divergent pathways. Democratic Republic of Congo and Nicaragua said, we’re going to double down on forest conservation. Others said they were going to be leaders on energy efficiency. There was this very interesting and very different mix that went into the much more traditional negotiations that took place up until this point. And to tie it all together, the graph that comes out of a lot of work here and elsewhere, is that not only if we look at our path of emissions, knowing we need to cut 80% or more– more if you talk about the 1 and 1/2 degree High Ambition Coalition– but the two challenges have not been thought about very clearly by many actors in the past. And that is not only what’s the magnitude of the cuts, but every bit of delay makes the slope of that line that much steeper. And this, in many ways, is, in my view, from examining power systems around the world, the bigger challenge. All of this is a huge challenge. I don’t want to minimize stuff that all of us spend our waking hours working on. But essentially, the size of the cut is one thing. But when you go from the lower slope curves, i.e. if we start earlier, you look at pathways where the Herculean task is roughly 3% annual decarbonization would be needed, plus or minus a bit in there. And that’s something that we have seen in admittedly limited bursts in the past. There was a time when California exceeded 3% decarbonization a year for a reasonable number of years. China achieved a similar thing for a while as well. But if we delay out 2025 or beyond, the slope gets to more like 5%. And we don’t have any large national examples of countries sustaining 5% annual cuts. And so if you think about the two aspects of the problem, to my mind, that’s the one that would be even more challenging. And we’ve done ourself, of course, no favors in recent times to get there. This was an interesting paper. There was a set of lead authors that came from Europe, for a variety of reasons, and there were academics, but also heads of state. And a whole variety of people who signed on to this– my governor, former presidents in Mexico, in Ireland, a variety of places. So it’s a very interesting mix that signed onto the recognition of that problem. And from my mind, one of the pieces of the story was what are the aspects of our energy innovation system that are working well? What are the ones where we dramatically need to accelerate it to try to think about both of those targets? And as you’ll see, my worry is on both of them, but it’s particularly on this pace. And so I, personally, have been working on this for a long time. I have been writing papers, now, two decades ago, about the problem of not investing enough in R&D. Just recently, Laura Diaz Anadon, Kelly Sims Gallagher, and John Holdren had an almost identical title, essentially. And it’s basically just bemoaning how these cuts in funding make this job incredibly difficult, even if we have new dramatic players– the investments in China, some of the really innovative programs on energy storage that we’re seeing, for decentralized power in Bangladesh, places like Kenya doing dramatically different things, the dialogue about what India might do. There’s a variety of players. But both of these papers, separated by almost two decades, are basically calls to the same thing. It’s really hard to get there if you don’t think about the investment story. And so just to give one snapshot of it– and, again, Jessica has been someone whose papers I’ve been reading this for a long time. Let me do, in brief, one version of this kind of analytic theory picture, and then look at it in practice. And then use that as a segue into the next part of the story. And so visually, how do we think about innovation? And something which I probably tell a third of my prospective students, to spend some time digging into and deciding ways to think about, is this wonderfully annually updated– I think it’s annual– updated graph by NREL, the National Renewable Energy Lab, where they essentially plot the trajectories of different solar technologies. And you go from the most traditional, the single-crystalline to the polycrystalline, to some of the thin-film technologies, and there’s so many ways to read a graph like this. For a long time, most people read it in terms of thinking about the efficiency of cells. And I don’t want to ever say I don’t think efficiency isn’t really important. It’s critically important. But in many ways, what didn’t get discussed in the early days, largely when solar wasn’t as easily economic as it is today, was it’s not so much just the cell efficiency. It’s what is the dollars per watt or dollars per kilowatt hour up to the available roof area. If you’re only thinking about solar in Tokyo, yeah, you better think a lot about efficiency, and driving things with efficiency is really important. But in terms of deployment and the mixture of what you get from R&D, and what you get from deployment market pull policies, is a much more interesting story. And so you see these early technologies had generally lower slopes, lower innovation rates, on this metric. And the world of organic solar cells, quantum dots, perovskites– a whole interesting world of areas which are changing rapidly– is an interesting part of this story. And I highlight this, largely, for the under 30-year-old crowd in the room. Because I used to teach a course on solar cell physics for years and years with a material scientist at Berkeley, Eicke Weber– who then went off to the Fraunhofer-Institute– and his argument to me every year was, don’t worry about all this interesting noise. It might be fun physics, but as much as you think organic cells or quantum dots are going to become a new leader, the traditional cells are going to get better and better. And we had this argument back and forth about, well, there are some new technologies that come along, whether it’s hybrid vehicles and then electric vehicles, that might upset the applecart. He kept arguing that the traditional ones would be improved enough that the others would play a role, but not a dominant role. And I would say, from my perspective, that that world has now changed quite dramatically, where one of those interesting other players, thin-film, has started to play a significant role. And depending– if you are one of the strong believers in any of the emerging red ones on here, I can argue, from basic physics, that several of these might get in this category as well. And so pulling apart the data embedded in a picture like this, in terms of what basic R&D does, what market forces have done, is a hugely valuable exercise. Of course, the way that this gets encapsulated in lots of conversations is something which is really interesting and has always really frustrated me, even though I’ve been, like almost everyone who does energy systems, have been publishing on aspects of the learning curve for decades. And so this encapsulation in log-log space of the dynamics between price and total installed capacity– those who don’t look at this every day, note that time is a stamp along the curve. It is neither axis. This has always really annoyed me, because it has been used over and over again. It’s valuable in lots of ways to think about how technologies are emerging, but I’ve always been really dissatisfied for the reason that I’ll explain right now. So I’m really satisfied because we have made remarkable progress, and the mixture of the scaling up of manufacturing versus investment and research has been a topic of a lot of degrees. But if you think about this picture, one can even pull it apart even more. So here’s the overall curve you just saw. The blue on top, but then one particular technology, thin-film, less total deployment, but basically the same dynamics in two different cell materials. It’s an interesting example of how these are evolving. And then there’s the much more detailed story, where you can dig in, and you can look at– and I would just say, for those who aren’t experts in the field– so, Jessica, you should sleep for a second here. One version is if you just look at the price you see this drop in prices. This is only for– this is less than a decade run. But if you just look at the module, you see there has been this kind of dramatic drop in the cost of the modules. But as the last decade has unfolded, solar essentially went from a place where 2/3 or more of a cost of a solar cell was the module to the reverse, now 1/3 or less, in some cases, dramatically less, in the cost of the module, which has opened up a great deal of really interesting work on how do we think about the same learning innovation curves, not just for the cell, but for the system. So the balance of system. And one of the most famous versions of this, that Angela Merkel loved to do– I don’t see I have a piece of paper here– but she would come to a meeting and she would say, yeah, you in the United States, you in California, think you’re doing a good game. Here’s the stack of paperwork you have to fill out to install solar. Here is the one sheet you have to fill out in Germany. And the ease of signing up for the German, and now widely used feed-in tariffs, where you get a fixed return based on the year you install your technology– generally a 20-year run– where you get a price determined by when you install it. If you’re a country that manages it well, you decrease that margin every year so you’re not overpaying people who would do it anyway. But essentially, this has opened up a really detailed world to think about how can we beat down the costs of the management, the installation, the insurance, the financing, all of these types of features, as well as keeping to beat down more and more on the price of the cells. So that’s the picture of an interesting world where we’re learning more and more. And we’re now at a place where I really don’t think many people would have predicted just a couple years ago solar and large-scale systems– this is a solar thermal, not a PV– came in under $0.03 a kilowatt hour. There was lots of griping, this case in Dubai can’t be real. They were given the land, blah, blah, blah. They did a second tender. Just six months later, it came in at 2.54 cents a kilowatt hour. There has been recent projects in Mexico, Saudi Arabia, and in Arizona all under $0.2 a kilowatt hour. Where people are now talking about solar at $0.01 per kilowatt hour. It’s kind of just a remarkable evolution that even the few people who go back and claim they’ve predicted it, I don’t really think they predicted the degree of fall that we have seen here. So those learning curves are interesting, and there’s the kind of interesting complication. So different learning curves for different technologies. This is an older David Victor graph. Not a ViCTOR graph, a graph made by David Victor. But then you see what you have to call a forgetting curve. And even if one is hugely optimistic about where nuclear might go, looking at the actual data, it’s a very curious technology that shows the opposite– an unlearning, a forgetting kind of story. And, yes, there are many reasons and many bits about the management, the mismanagement, that have gone into some of those. So many of us have studied the data on the amount of funding in different areas to try to look at what that’s meant. And, for me, the biggest annoyance in this process is that very simple model, very descriptive, this learning curve picture, doesn’t fold in what I would personally not only like to believe emotionally, but also believe technically has to play a quantitative, a quantifiable role. And that is the investment or the effort, whether you want to call it dollars spent, or PhDs granted, or a variety of things that go into bringing down the cost. And so there’s lots of issues to think about. There’s a very sad story. This is from that same Anadon, Gallagher, and Holdren paper. And, of course, I can’t even really bear talking about the tiny projected bars of funding that are being talked about now. But I’ll save that for a depressing post-House and post-Senate discussion about what our energy R&D budgets might be. On the other hand, there are some really thoughtful stories. And this is probably my favorite graph of all time in demonstrating that you really can think through the investment and innovation, the so-called technology push, and the market pull. This is an early planning graph from the Japanese agency that ran their solar program. And what you can see here is they invested in R&D. The green above the line is the R&D investment. It’s in billions of yen. But you can see there was a longer and fairly steady investment in R&D that then ramped up. There was a delayed later program on deployment that they waited to get the R&D machines running. And then they invested considerably more in the deployment, the market pull, the market enabling part of the story. And, again, you rarely see pictures, in my view, that show that much thinking through how the investment plan you go. I would say ARPA-E is the best US example of thinking through, and we don’t have enough data yet to really examine how it’s gone. But a remarkable piece of the story. So that said, that learning curve that we all use, that I use all the time that really annoys me, is just this single parameter version where we simply use production volume to then examine cost. And just the nomenclature is the exponent, negative B is the slope of that line. That’s a one parameter fit to the data, and there’s lots of ways to play the game. Here’s the same data looking at price versus annual production versus cumulative production. And then the first thing that gives me hope that one can build a two parameter or more parameter set of curves, and do better than just the benefit you get by having more free parameters. And that is we see a more dramatic, almost twice the effect, if you look at something which is much harder to quantify, but intellectually speaks much more to me, and that is price versus not production, but price versus patents. And so this opens the door to start thinking, in my view, about versions of these models of innovation that have both bits in it. And so we’ve done a lot of work, this paper just came out last summer, in looking at deployment in storage. And so what we’ve done more recently now is to build versions of these curves that have both an R&D quantified piece and also this cumulative capacity. Now, one of the really interesting aspects of this is, no surprise, R&D is difficult to measure, because even a program devoted to solar, or storage, or wind, money goes into a variety of pieces. Do you simply count all the money? Do you count some measure of the output of the money? I use patents here, because it’s the same thing I used in that paper I showed early on from 1999. That’s not a very good argument to use a variable. But patents, as we know, can be progressive. Patents can also be defensive. We now have a great deal of data around how the big three auto manufacturers in the United States essentially chose, within one company and two others, colluded to not publish much data on early lead-acid batteries. And that definitely slowed the movement into other materials for batteries. But patents is at least a metric for which the economics community has worked for a long time. Zvi Griliches and many others launched this area. But a reasonable question, especially for grad students in the room, to think about is, do I even think patents is a good measure? Maybe PhDs granted, or PhDs who become post-docs, or the metric which I currently like most, which are PhD students who get funded but don’t complete their PhD. And hopefully go off and found a company, and get sucked into the interesting world of startups and things. None of those are perfect measures, but thinking through these other metrics– and actually, we have a student right now looking at the not completed PhDs, but don’t leave the field as a metric that she’s really into right now. And so it’s an interesting one. But these are the stories that I think we’re going to have to make into theoretically rigorous models if we want to think through the process. And so, of course, we’ve been using this to plot, in this case, the largest chunk of data on batteries, comes from lithium ion, no surprise. And so in looking at the historical prices, one factor models, two factor models, and I’ll just put up that data and I’ll overlay it with the learning curves for wind and solar. And you’ll notice that I’ve now flipped, and I’m using log of price, but I’m actually using real units of time on here. And we see some important– I don’t want to call them surprises, because there was a fair amount of work. But some of these drops are interesting. Had to do with dramatic ramp ups of Chinese manufacturing and others. But building a more quantitative predictive set of metrics to think through this aspect of innovation is a critical part of the story. And it’s one where there’s a few people working on it, but it’s an area where we’re going to need much more, especially if dollars in the US are going to get much more scarce. Because we’re going to have to think through more strategic programs, and it can’t just be a story of we want to ramp up what we’re doing in ARPA-E, because that model is not going to scale if it’s the only thing we’re doing. This, just to kind of put more of the data together, is from a group at University College London that we’re collaborating with. These are the learning curves for a variety of different storage technologies from a brilliant paper, in my opinion, they did early last year. And you notice a couple interesting– lead-acid is the yellow curve along here, and pumped hydro is the little green bits there. And those are fascinating because this is exactly what you would expect. Technologies that you cannot mass produce, that are limited by geography or other features, it’s not a surprise that they are showing the same kind of behavior. But that itself is not good enough. It’s not just things that you can mass produce. That’s not the only parameter. So thinking through these datasets, I think, are going to be critical. Storage, of course, is the great enabler if we can get it all to work. But on the other hand, the data on storage is even harder to disaggregate, because much of the data on storage comes from systems-level assessments. It’s not just money invested in nickel-metal hydride, or lead-acid, or flow batteries– an area where I was just meeting this morning with people across town on– but figuring out how we’re going to build predictive, not just correlation-based models is a critical part of the story. I’m going to use that to transition a bit. Here is a project site that you would take on just because you really want to go there. This is an island in French Polynesia, about 20 miles from Papeete, the capitol. And this is the MOTU, it’s called– the island that was given to Marlon Brando for making Mutiny On The Bounty. That’s not a bad deal. And so there was one person, Marlon Brando’s son, living on the island when the Brando Trust came to us and said, we want to build a combination of an eco resort and a research station on the island, and we want it to be 100% renewable. And so I wasn’t going to say no to the project based on location, but it taught me a variety of things. And one of which is going to seem blatantly obvious to all the students in the room, but it wasn’t obvious to me when we started this process. So this is what the island has on it now. It’s got main rooms. It’s got pools. The airstrip is on the back over here. That’s where the research station is. And the first thing, of course, that was installed was half a megawatt of solar. It’s the largest solar array for 3,000 miles, which isn’t saying much because this is 3,000 miles from anywhere. And so that was the first thing to go in. The second part of this story was a biofuel system. There’s a fascinating back story that French Polynesia had signed a weird deal to export coconut husks at a loss. Don’t ask. But now they’ve reversed that, because now they’re harvesting from other islands. And then they installed a salt water air conditioning system. Basically a pipe that goes down just about a kilometer, brings up cold water. They use that for air conditioning on the whole island, and they use that water to do agriculture in a tropical setting, which we couldn’t otherwise do. It’s kind of interesting part of the story. And then a zinc-bromide flow battery– this has been the bane of my engineering existence for a variety of reasons. Zinc flow battery in the tropics grows all kinds of stuff in the electrolyte. It’s been a real pain, but it was the first large flow battery not installed in Canada. This company was Canadian, then became Chinese, and now I’m not clear who owns it anymore. The thing we did not install at the time, because it wasn’t ready, and because we were meeting 100% of the projected demand on the island, was an Australian technology. It’s a tank of fresh water. So it just bobs in the current based on the density difference. And in all of our focus on energy, the reason why we backed off on putting it in was because we thought we were sufficient. But, of course, when you go from an island with one resident to several hundred residents, you need more water. And this system not only generates energy, but in the bottoming cycle we actually run reverse osmosis filters. And I had totally missed the degree to which this would end up being the most valuable aspect of the system, because we were so focused on optimizing around the energy goal that we really left out some of the other goals. So it’s kind of an interesting case. And I’ll use that as the transition to now talk– I think I’m going to skip this case here just so we have some more time, since I was a little bit too loving before in those cases. And that is I want to talk about power systems analysis, because all of these technologies, both those that we need to innovate more rapidly on and those that could use market pull, are going to need to be engaged in how we build out these power systems. And so our lab has been building a couple of them, with the initial driver for us to build the models being California’s own state targets. Our target was 20% renewables in 2010. We missed that target through a party. Made it three years later, and California is now significantly ahead of schedule for our 2020 target. The estimate right now is that California will come in at about 37% or 38% renewables by 2020. And the footnote is that in California, we don’t count nuclear and we do not count large hydro. So it is a restrictive set. There’s lots of jostling and battles between the various groups about meeting the 50% target. Almost everyone thinks we will make it, but people have differing views on which technology mix. And those of you who follow the California story in more detail know that we are currently debating Senate Bill 100, which calls for a 100% renewable energy in 2045, and that includes a significant ramp up in demand beyond the commercial/indus trial/residential demand forecast because of a very aggressive electrification program. I won’t go through the story– the Art Rosenfeld, the battles– about exactly why it happened, but it’s worth just putting up on the graph. I don’t think that California would have had this aggressive debate anywhere near this quickly– basically 17 years out of bankruptcy in the energy sector– if it wasn’t for the fact that based on Art Rosenfeld’s theory in 1974, that we can go to what Amory lovins would call a soft energy path by doubling and tripling down on efficiency across different sectors, that California has dramatically changed this picture. And, yes, there is an ongoing debate about how much of this decoupling from the rest of the country was by exporting some of the more carbon intensive technologies or not. I’ll leave that as maybe a Q and A thing. But it’s a remarkable version of the story, and I don’t think we would have had the full debate without it. So right now, California’s working on a plan to say, you may not build a residence in the state after 2020 that doesn’t meet all of its own energy demand, presumably, as it was originally written, through efficiency and likely on-site solar. But as 2020 starts to get pretty close, the pushback hasn’t been so much, no, we’re not going to do it. The pushback has been much more, surely homes in cloudy or tree-covered areas don’t have to have its solar physically on its roof. It could be shared solar on the neighboring big box store, or supermarket, or wherever else. So it’s an interesting extension of where the state has gone to before. And then the more high profile versions of the story are large solar, both currently California– I could always be wrong because China and Turkey have actually potentially trumped it– but as of last year– I didn’t say that. I didn’t mean to say that. But at one point in early 2017, the largest solar thermal plant, the largest crystalline, and the largest thin-film plants in the world were all in California, and I think two of those have now been eclipsed. One of the areas that I mentioned before that we’re particularly excited about, this is a flow battery installation in Southern California’s territory. And the driver for this is something I’ll talk about in the models, and that is California now has, because of the modeling efforts, a mandate for the amount of storage. And unless I’ve missed out as of the last read I have, currently only California and Germany have mandates for storage in the mix. But I could easily be corrected by a place that’s caught up. One last piece of the story, and this was started before the US 2017 election, is that Governor Brown in California and the governor of Battenberg in Germany are the co-chairs right now of the so-called Under2 MOU. The Under2 degree Memorandum of Understanding, of which, as of last count, some 280 sub-nationals around the planet are signatories. It’s over a billion people. It’s almost 40% of the global economy. A map of the places where it is active and areas where it’s notably absent is kind of an interesting mosaic. One of the big side events at the next climate conference in Poland will be a sharing of best practices of the legal language, of public-private partnerships, and tenders for renewables. And you’ll notice that not all of the units are the same. There are counties, states, cantons, and cities that are all members of this kind of interesting mosaic. And so that’s been a really valuable place to share a lot of the data on it. Morocco passed a 52% target I think just to piss California off by 2%. That’s a good kind of battle to have. And so what I’ll highlight, now, fairly briefly– I won’t do the full power electronics of it– is that one of the two models that we use in California is one that our lab built called SWITCH, which shows I’m not good at acronyms. It’s supposed to mean, roughly, solar and wind integrated with transmission and conventional power. But you can tell the name was more important than actually being accurate on the acronym. We started by building this power systems model for California and then expand it to all the green in Western US. Right now British Columbia and Alberta are observers. British Columbia participates actively. Alberta, not so much. Mexico is an active partner. We then built the model for Chile, for Nicaragua, for China. We built the model for India, but are not allowed to release it because we got the data at a moment of weakness in India. After the blackouts, they gave us the data, but then didn’t want it published. And then East Africa, starting with Kenya. And the blue East African nations are forming a power pool right now, very much like the one in Western North America. So it’s interesting places to do it. The way we do the model is much more the physicist approach than the electrical engineering approach, as my electrical engineering friends tell me all the time. And that is we model every power plant with its heat rate– every transmission line. We have a limited set of energy efficiency measures, and we take as small time steps as we can from today forward to 2030 or 2050. So in the places where we have the best data or the best synthetic data, we take sub-hourly time steps all the way to 2050. It is a lot of data. And what we model is the capital cost of all investments, and we’ve done it as a linear program so that everything we add is a constraint. You have a carbon price. We write it in as a constraint. You have a transmission bottleneck. It appears as a constraint. We model every current transmission line, and we allow the model to build AC or DC, medium and high voltage lines, and then run this forward in the system. And so just to give you a feel for it, and the feel for why doing this much computation, for me as a physicist, was really just a humbling experience, this is a picture of the different scenarios that we’ve spent a lot of time on for Western North America. Each one is a scenario built for a different reason. SunShot is the price of solar getting to the low levels it’s now at– $1 a watt for commercial, $1.50 watt for residential. This battery shot program is something that Argonne National Lab is the US lab leader on. My favorite scenario, no surprise, is SunShot and low-cost batteries. There’s cases where we have nuclear, and we have CCS, where we have natural gas prices go up, where methane leaks, a limited amount of hydro– a variety of cases. So even if you throw out the limited efficiency scenario, because you believe, at least some degree, in the wedges, and that would be the one stupid one to build. I may not like all of these, but all of these are scenarios that make that two degree target for this region of the West. And so this– lots of interesting dynamics. I was super happy when we finally got all of our flow battery modules working and this and that. But essentially, this says that you tell us a mix and we will build a scenario to get there, but it doesn’t discriminate between them. What does discriminate is for some of the most distributed you need to start investing early, because transmission lines are painfully expensive and time consuming to build. And so you might want to do them all, but some of them are just simply not going to work on the time scale if you don’t get on with the job. But this is way too dizzying a set of scenarios to sit down with policymakers. One has to think more about which ones would you want for a variety of reasons. So here’s kind of a picture of that world. This is a snapshot in 2050. What I’ve done just to make it more intelligible, these are the hours of the day. So I’ve stitched together two days each month. One is the peak demand day, and one is an average day for each month. The colors are, hopefully, representative. The black line is the forecast demand, the light blue is wind, dark blue is hydro, yellow is solar PV, orange is solar thermal, and the negative going lines are charging up storage. So one of the questions that comes out of a lot of this is, how much storage do you need relative to generation given the local features? So in the US West, we routinely have not one or two days, but often a week and a half or more of incredibly low late summer wind. It goes almost still, thankfully for us. That’s when solar is generally at its peak. So we actually get a nice balance back, but it’s not a– that’s something that varies place by place. We have now been working– I won’t bore you with the numbers on the prices of different technologies. But one of the features that we have now been pushed by really interesting outside forces is to think about much, much more electrified vehicles. And so California’s goal is a million electric vehicles by 2020. I proudly went and told some colleagues in China that and they said your reading last week’s news, because we just upped our 2020 goal to 5 million, which is a neat part of the story. And with some of the new promised but not yet delivered vehicles, we have some incredibly low price and dramatic range vehicles that are being proposed by some of the players. So that trucking, not just passenger and light-duty vehicles, might be really amenable to this kind of process. And so, again, just because we’re– we have done a similar story in China with the business as usual, the path that would meet their commitment to peak emissions in 2030, also cases to think about turning this over and getting down to the IPCC types of goals. Interestingly enough, we find the carbon price that one would likely need to drive it in China is not so different than we’re seeing as a price that we think in Western North America. Again, lots of details of the transmission maps and things. But most of you probably heard that China just announced a strategy around phasing out internal combustion vehicles. And so we’re working with a four university consortia in China led by Sichuan University, Tsinghua, and North China Electric Power University to use the switch model to examine how much added demand there is likely to be as they think about this massive electrification. And so, again, it’s been an interesting collaboration where we are likely to push our California EV goals based on this collaboration. The last little bit of things I want to get in is that we’ve started to look at countries that were neither big emitters, nor ones where a carbon price would be a big deal. So Kenya is a country that, as of this moment, is largely powered by hydropower, and there are two competing strategies. Last week in The New York Times– there was a reporter who traveled with us for a week. Kenya is considering building its first two coal plants, ironically both financed by China, even though that wouldn’t fit with the Chinese story at home right now. But because they are powered by hydro today, essentially, carbon price and a lot of the traditional policy tools that we model in are not going to play a big role. But the red on here is geothermal, and the strategy that looks most attractive to us in terms of cost is actually to basically go from a country that’s 2/3 powered by hydro to 2/3 powered by geothermal, with solar and wind playing a big story in Kenya. It’s now home to the largest wind farm in sub-Saharan Africa. So we work closely with their version of the FERC, their equivalent of the Federal Energy Regulatory Commission, to examine these scenarios and what some of the costs of the coal and other kind of cases would be. So I hope I’ve highlighted a number of the examples where this kind of gets real in terms of planning, but I’ll very quickly just highlight a last little bit of the story. And that is that one of the areas where I would say the energy theorist– is too strong a term– but the energy futurist crowd are most divided is, will all of this large-scale power planning be the true wave of the future or are we going to be much, much more disaggregated? The Elon Musk view is that you have a Tesla in your garage, solar on the roof of your garage or your building, and you don’t need or want the utility. And that’s not quite the picture that I see happening. But we have an interesting collaboration with a variety of private sector partners where we are taking right now a 40-home block in a lower income area. There’s one home that I won’t mention. This one, that’s a crack house– but I didn’t say that. And what this 40-home block has agreed to Guinea pig on is a mini grid, but not just around the energy. And so the plan is to build solar on the rooftop, but to aggregate it so it’s not for each home. And the gray building over there is a unused garage that the group has devoted to a physical storage system. It’s actually a mechanical flywheel that would be the storage, because it has very fast response properties. And so this eco block, as it’s being called, is a physical version of building out neighborhood by neighborhood. There’s the first of the Amber Kinetic. It’s actually, of course, a Berkeley spin-off company. That we’re installing these as the storage technologies, because we’re really interested if we can develop enough low-cost control technologies so the block level can do it. Here is kind of a mix of mini grids, and I mentioned a few of them. The Brando is the one I showed you before. There are some in Nairobi. There’s a series of them in Myanmar we’re working with to try to figure out which of these localized mini grid models makes the most sense. Is it mini grids that are truly on their own? Is it mini grids that are grid interactive, meaning they’re always connected and they choose to buy or sell? And so were trying out a number of the control issues in the process. Last thing I’ll mention is something that I think that I’ve been guilty of sort of my whole career. And that is thinking largely about these analytic models and the innovations on the hardware side, from applied math to designing systems, but we’ve done very, very little on behavior. And so one of the things that we built for Governor Schwarzenegger was a carbon footprint tool using the utility bills– interviews that we do online for are you a vegan, vegetarian, vampire, whatever kind of eating habits you happen to have, what kind of vehicles you have– and there are versions of this calculator for businesses, schools, homes, et cetera. And so what we’ve done is to kind of do the standard thing, where you do a carbon footprinting. But the feature, for me– the zero-th order learning about behavioral economics– it took me a while to get there– was this is what it spits out. It tells you your carbon footprint in various areas, and then it tells you the next 20 or more things that you didn’t do and what would be the financial and the carbon impact, and then it queries you as often as you ask it to for have you done them. So this individual did the more efficient vehicle, the telecommuting, and switching to CFLs, didn’t do the other ones. And then we gather data from the people that allow us to gather, and build both our overall carbon footprint maps, but also interactive apps for your phone and various things. And so I’ll just kind of flash up the data. This is averaged over zip codes to preserve some of the details. We actually do it down to the census tract level. But this is kilowatt hours used and the zip codes, natural gas usage, fuel oil usage. And then the ones that are probably the most interesting is that this is the average carbon for transportation, goods and services, and food. And so let me flash back and forth, because this one is the one that people kind of get most interested in. It’s largely a measure of affluence and delivering of amazon.com packages to your home. The people– I won’t name any names– my wife– who will routinely order the size she wants and bracket it, knowing that free returns– and I– we don’t get along really well on that particular practice. And then the carbon footprint of food. Noting, however, this is the carbon footprint of food at the location where it’s grown, not the end use consumption site, because we don’t have a way to track that very well. And then the overall carbon dioxide emissions. And if you look closely or if you’re young, you will notice that the cities look like, I guess, reverse pimples. If we kind of zoom in on four cities, whoops, not four cities yet. Did I put the wrong– oh, I didn’t bring the four– apologies. I didn’t– apologies. I didn’t bring the one with four different cities, I brought just the New York City one. But you’ll notice that the Manhattan core is much greener. That’s not just because car ownership is less. It’s also because for the same income level you generally have less power points– not PowerPoint slides. I have too many PowerPoints– but less sockets and things, less outlets. And, of course, cities like New York, and San Francisco, and many others have won wonderful awards for being low-carbon cities generally by not including the suburbs. And, of course, this just highlights the degree to which that is just absolute cheating, which is obvious. But showing people the maps has been kind of a helpful part of that process. Just because I’ve gone the full hour, which I did not intend, I won’t get into more of the really off-grid, pay as you go, small-scale technologies, but I just want to highlight that we’re interested in them too. But let me end there– I know I’ve gone long– and just open it up for questions. And thank you all for staying. I had not planned to use up the full 55 minutes. So thank you very much. [APPLAUSE] 1201 00:56:58,710 –> 00:56:59,445 Thank you. My name is Seth. I work for an energy consulting firm not far from here called SourceOne. Oh, yeah, I know it. Oh, great. I saw Veolia on one of your slides, our parent company. I’m glad to know they’re investing in something good, as they often do. So I had two questions related to the recent proposed rule from FERC. The first question is technical. I’m just curious, from a modeling perspective, the recent proposed rule is about enabling or causing the regional transmission operators to include storage in their participation models in the wholesale markets. So one technical curiosity question is, how will that factor into how you run your models? Will that change the way that you do, you and your team, do your research? And then the second question is kind of more at a qualitative level. What is your expectation, I suppose, for how that is going to impact some of these cost decline curves? And how do you think about that on a higher level? Also with the demand response one that’s coming up. Perfect. So just the preface for this is that until this gets through and everything happens, Germany includes storage as one of the technologies in their feed-in tariff, but there is a complication about the South and the North. In California, we, evidently, and I’m partially to blame, don’t understand the difference between kilowatts and kilowatt hours. And there’s a reason, which maybe I should explain why we did it. So what California has done is we have a storage mandate. Our peak demand on the worst hot summer days right now is about 65 gigawatts. And by 2020 the utilities have to have online, active in their network– which is why it’s the lead into your question– we have to have 1.2 gigawatts. So it’s a peak power. It’s not a duration, a kilowatt hour. We have to have this, which is equal to just about 2% of our total peak demand, which, of course, we only hit in the hottest summer days. Our winter peak is often half of that or even less now, because of some other efficiency measures. And the reason why we don’t understand kilowatts and kilowatt hours is not, actually, because we’re totally stupid. It’s because there was a lot of worry on the utility side about requiring a performance for batteries and flywheels that are not yet in the market. And so there was an agreement, a bit of a negotiation, to make it about the nameplate rated capacity of the peak storage. And in the next version, if we can get sort of more agreement and more operating experience to move on, so that the next target, for which we’re using SWITCH and the model from a group that some of you may know called E3, which is or was the home of the so-called Deep Decarbonization Project– Jim Williams and others– our next goal, if I win, or I and people I’m trying to bend their arm, is 4% in 2024. And the reason why I do this long intro is because we’re exceedingly excited. That I’m sure there will be some misapplications of this FERC rule. But if they really do it, at the very least, it will get back to what I was highlighting with the learning curve in the beginning. And that is we will have a credible demand pull, broader than California. The problem is that not everyone has the same mix of expertise in the utilities. All utilities want to go one direction or the other, but that it’s far easier to do this if the utilities have a pretty significant IT team. Because once you bring storage in like this, it’s much more than the peak number. It’s how do you operate it so that it will allow you to shave off peak and to put enough power in so that for the forecast to peak times you can get a lot out of the batteries. Otherwise, they’ll be on the network, but they will not be performing economically well. So kind of a long-winded answer. I’m exceedingly excited, but I anticipate there will be some misapplications of it. Whoops, I don’t know why I landed on that page. That was not the intended spot. But in the SWITCH modeling, which was kind of the second part of your question, one of the things that’s really interesting to us is that in looking at some of the local places where we have storage online, like that flow battery I showed in Southern California, we’ve actually found that it gets utilities into the ancillary market in a much more interesting way. And for those non-energy and electricity all the time people, that essentially means the market for a whole variety of services beyond just kilowatt hours. Frequency control, response to blackouts– there’s a number of ways in which we think that storage has benefits that haven’t even really been explored yet because there’s so little out there. And so I’m exceedingly excited. And it’s one of the reasons why I spent so long on the PV curve in the beginning with all of the different NREL graphs. I actually think that, even more so than wind, the diversity of storage chemistries and physical technology for some of the flywheels means that storage is more poised to be like solar than anything else that has come along. And if one wants to really think about baseload renewables, we’re going to have to drive the prices down. Because even with the current forecasts, we’re not going to get to the mythical $100 a kilowatt hour, kind of the SunShot equivalent soon enough so that the FERC rule won’t impose some real costs on some of the players. So I’m very excited, but we’re going to have to find some ways to get in. And there’s no question, the potential cuts in budgets at the federal level are the worst possible medicine for getting us there. So I apologize for such a long answer. Hi, yes. I grabbed the mic. Speaking to the cuts in budget at the federal level, I have lived and died that slide. I graduated as an engineer right after Ron Reagan came. I benefited greatly from the ARRA, and we made a lot of progress in biofuels, bioenergy. What advocacy is going on right there? What is your hope and what’s the mood? Are you doing something about that? Oof. I’m not even sure– the stock answer, which I think is not a bad stock answer, is that there seems to be no one outside a few people who sit less than a meter and a half from the president who actually want this to happen, and that there’s a very broad consortium that wants something else to happen. And while that’s the stock answer, I actually think it’s our best defense, but it’s pretty weak. The other version of it is a sadder piece. And that is that if the proposed cuts are more like the– I’ll just call it– I don’t think we’re going to get a 70% cut, but let’s say we get something that approaches 50%. It would cripple lots of really interesting programs, including– ARPA-E is slated to be cut entirely. Just no discussion, it’s just gone. It’s 400 million disappears in the drop of the bucket. One of the big challenges, even though China is outspending the entire rest of the world right now, is that when you look back at other R&D efforts in the past– and Dan Kevles is a historian who was at Caltech and at Yale. A number of others have examined this, and some others, in detail. Even if you have a big budget, you generally invest poorer and poorer and poorer over time unless you have a competitor and a foil. And so I really worry that even if we think there’s a reasonable pot of money out there it will be underdone. And I don’t have to look any further than California, where I think we’re spending our money pretty well, but we have an amazing short-term glut relative to our long-term baseline. And I like having the glut. I feel like I’m writing proposals as fast as I can. But I don’t think we’re going to look back in five years and say, even in California, spending our $7 billion of cap and trade money, I don’t think we’re going to be able to say with a straight face we used it as well as we could have if Canada, or France, or Mexico, or someone else was competing with us in a way that, if this happens, there is no competitor to China. So that was a very weak willed answer. Because except for that pushback, this bipartisan pushback, I am definitely worried. So one of the things that we try to do is to highlight all of the other benefits. The security benefits don’t have to necessarily be on oil and gas. They can be around the benefits by having a resilient system where the grid is smarter, where we have more electric vehicles, where we have vehicles or grid, where we produce hydrogen offshore, in areas where we want to tie it in. I think that story quantitatively works in terms of jobs and in terms of air quality, but I think we’ve demonstrated this government doesn’t really care about air quality. They just had a press release to highlight relaxing a toxic dumping rule four days ago. So no good answer. Questions here. Hi. I’ll save you for the end. We want to go right here. What is the advantage of a mini grid relative to or compared with a large-scale grid where you can make up for the fact that maybe the wind is low here, but 100 miles away it’s blowing [INAUDIBLE] sun? So if you only look at cost I think you can argue that, for the foreseeable future, mini grids never win in urbanized developed systems. Where I work in South Sudan, and in Kenya, and Uganda there’s places where no question it is– the only form of larger power than to pay as you go. That that’s not my argument why I think the mini grid argument is so critical. I should have put it in the slide set. But there was a famous effort organized by the European Union in the 1990s to assess the future of solar. And I was one of about 200 people on the various teams. There was a team for large-scale grid people, there was the off-grid, there was the military, there was– all these different pieces. They all did their assessment of what we thought the technology could do. We added up all of our forecasts, and surprise, surprise, all of our growth curves actually had the same r, they had the same slope. I wonder why. A bunch of teams talked. Even if you were in the small village group versus someone doing on-grid, we all socially homogenized to each other, and the real world outperformed us by two orders of magnitude in terms of how quickly the prices came down. At that point, in the late ’90s, early 2000s, it wasn’t because of the large-scale solar systems that we’re now accustomed today. It was because lots of early adopters paid more than they had to and we had a diversity of markets. And so I actually think that that’s part of the benefit to the mini grids. That we will get more interesting innovations, we will get more companies formed, we will get more capital priced earnings out of a process where there is more mini grids. Is that something which I think we can capture easily in the current DC world? No. But I do think it’s something where we’re seeing elsewhere. So California, using part of our money, and in China, each just invested well over $100 million in mini grids that we don’t think are going to outcompete the traditional grid tomorrow morning, but over a short time I actually think they will be real competitors. And so that wasn’t an ironclad answer for why I think it’s better, but I think that the places that are investing– China being one of them– is investing heavily in mini grids right now, even though the goal is a totally connected systems. So I liked it. Yeah. 1469 01:09:54,684 –> 01:09:57,770 Yeah, you seem to be a strong proponent of storage mandates, and I wanted to just kind of get your thoughts on– there’s been a number of studies which show that if you operate storage in a profit maximizing way, it wouldn’t necessarily reduce carbon emissions, it might increase it. And maybe the reason why many jurisdictions don’t have storage mandates is because they are costly and don’t reduce carbon emissions. But that’s a static approach. I don’t know if you have a broader justification, turned dynamic technology learning, or if you feel like the California case is somehow special. No, I actually don’t. I don’t think it’s that special, in fact. The reason why I think that it’s a mandate, and why I’m very enthusiastic about it, is because one of the lessons that has come out of Germany, and Denmark, and Portugal, and California is that the carbon price has done very little, if anything. That the real carbon reductions we’ve seen around the world have come from mandates. The mandate to reduce emissions doesn’t have to be on the storage element. I think we’re ultimately going to need storage, and certainly with low natural gas prices today, you can argue that we could just use gas as our storage proxy, build systems, maximize the amount of renewables, but I don’t think we work that way. And so having a mandate for storage, like having an RPS, or a feed-in tariff, or an urban grams of carbon and particulate per cubic meter, I think those targets really do work. So I am very happy to invest in storage right now when it’s cheap to do it, because we don’t need to be 25% storage today. We just want to get into the market and demonstrate to the grad students, and the undergrads, and the pre-undergrads, and the authority in the room that this is one of the areas that we think is going to be a leader over time. And so just because storage itself doesn’t bring down carbon, the places that are likely to install a lot of it, they already have their own mandates. None of the states in the US that don’t have an RPA I don’t believe are going to be storage leaders, with the possible exception of Louisiana, that’s indicated that they see storage as a resilience feature, which they may want to invest in irrespective of an RPS. So that’s why I think that investing in storage now, even if it isn’t the lever to bring down carbon, is a really key part of this mix. But it’s a good, tough question. 1526 01:12:30,160 –> 01:12:30,660 Hi. A question about nuclear energy, because when you compelled all the options, you just put nuclear and carbon sequestration– and in the Dewey and in many other studies show that– and France, for example– show that it can be the main baseload alternative. And then you can do the rest with renewable plus storage. Right. So why isn’t this more addressed in your analysis? Right. So I’m a professor of nuclear engineering. It was on the initial slide there, at Berkeley. Largely, though, not because I do nuclear reactor technology, because I do risk analysis. And so if nuclear could meet the, I thought, reasonable set of criterias that the Generation IV Roadmap was supposed to get to, it could be a very different player. But we don’t have that today. We don’t have the cost reliability, affordability, shorter construction times that are discussed, and that’s even before you get to managing the right story. And actually, I spent several years on the Gen IV with Neil [INAUDIBLE] here– in the planning process. My worry right now is that you really have to be in a wildest dreams picture. We have roughly 420 nuclear plants around the world. We have roughly 60, half of them in China, that are under construction or planned to be built. Even if we built all 60, which won’t happen, nuclear doesn’t maintain market share. Unless this 60 is a driver for a huge additional wave, whether it’s SMRs, traveling wave, liquid– whatever the technology is– unless the construction times change dramatically, order of magnitude of construction times, I don’t see it playing a big enough role by the mid-century. We might be hugely nuclear in the out years of the century, but in terms of this mid-century two or one and a half degree number, you really have to say everything breaks exactly in nuclear is waved to get it to work. Now, that doesn’t mean it can’t. We have lots of high profile groups. This is one of the best nuclear energy departments in the world, but we are in a situation now where we’ve squandered so many years, that if you take the two degree or less 2050 target, it’s going to be everything has to work in the most optimistic picture. So I’m not against it, but I just see it as a really hard one fitting into the climate equation in the short-term. Maybe one more question before we– Why don’t we go all the way back. Yeah. Hello, I’m the news editor of Clean Energy Finance Forum. I wanted to ask you, what are some of the most exciting things you’re seeing lately in terms of innovation and energy efficiency? Well, I think the most exciting one is in some ways the most boring one. And that is that more and more places that didn’t have any quantitative approach to demand side management are now interested in different aspects of it. They’re interested in how much DSM can you legislate or just ask for versus how much do you have to pay for, and what should be that cost curve as you pay for it. And I know that’s not as exciting as certain window materials or this and that. But if DSM could become a large part of the story– and actually in our models, one of the big features for California is that– forget the duck curve. We now see nighttime, evening demand being by far the largest demand in the state in the out years. And no matter what storage does, if you can’t do a huge amount of cost effective responsive DSM it’s hard to get there. So that’s one that I’m really keen on. The other one is actually– I’m not dodging the question. But those of you who have seen this new push towards transparent solar cells, now we can think about buildings that are solar and we can have smart and responsive windows at the same time. And thinking about much better windows at a time when– again, I hate to say anything bad about architects and planners, but I’m sort of getting tired of the glass. Every single building I see is now another glass box, whether it’s a vertical glass box or horizontal box. But if we can now think about really optimizing surfaces– in the past, we were doing it just one parameter on– that would be a huge push forward for me on the efficiency side. And, again, DSM I think is where we have the most ability to change the equation here. Thanks very much. Thank you. And thank you guys for staying so late. You’re– I really appreciate it. [APPLAUSE]