 we'll get started then. I think we have quite a few people joined already. So good morning, good evening, good afternoon everyone. Welcome to the first of two webinars we're running here. We're really happy that you join us. My name is Charlie Heaps and I'm a researcher at SEI, the Stockholm Environment Institute, and I'm also the lead developer of LEAP. We have a packed program for you today and we're going to be demonstrating some of the new features of the 2020 version of LEAP. For those of you who are not so familiar with LEAP but I think many of you are, LEAP is a software tool developed at SEI that is widely used around the world for integrated planning of energy policy, climate change, mitigation, and air pollution abatement. LEAP helps its users to explore alternative energy development pathways and assess their implications in terms of energy use, emissions, economic costs and benefits, and health and ecosystem impacts. LEAP is notable for being a complete decision support system, not just a modelling tool. So it also emphasises key tasks such as data management, results visualisation, documentation and stakeholder engagement. I do want to emphasise that today's webinar will not be a tutorial or a training. I know that many of you listening today are already LEAP users, so you will know that while LEAP is known for its ease of use, it still takes some time to get really comfortable with it. At SEI, we've been working for over 30 years to build institutional capacity to help people apply LEAP and we've conducted trainings in more than 50 countries worldwide. Obviously, with the current pandemic, that's making travel impossible right now, just at the time when many countries are working hard on the next round of their climate commitments, their NDCs as they're known. So at SEI, we've been developing other ways of building capacity, including creating new distance learning training materials and conducting remote assistance through Zoom and other platforms. So you'll be hearing about those kinds of things in the near future, I hope. But in the meantime, please reach out to me if you're interested in getting training and you want to know more about how we can help you with that. So just again to emphasise, today is not a training, it's really focusing on showing the new features of this new version of LEAP 2020. And in particular, we're going to focus on a really major new feature in LEAP, the new NEMO software that's been developed by my colleague, Jason Vasey, who's on the line with us today. There's a lot to get through in this webinar, we only have about an hour. So I try not to talk too much. As I say, there's a lot to get through. So I'm really delighted to be joined by my colleagues, Jason, who's on the line, and Sylvia Ojoa. Jason is the developer of NEMO, which you'll see shortly, and Sylvia is one of our main model developers and LEAP trainers here at SEI. Before we get started, let me just mention a couple of things, a couple of topics that always come up when people ask me about LEAP. So first of all, where will the software, when and where will the software be available? So this new version of LEAP, LEAP 2020 will be available for download in the next couple of days on the LEAP website. The LEAP website for those of you who need reminding is energycommunity.org. I'm going to move to the next slide as well, so you can see who's talking to you. Sorry about that. The NEMO software that Jason's been developing is already available, but I will let Jason go through that in more detail in a few minutes. Will this version be usable by those who are already using LEAP? Yes. LEAP is fully backwards compatible with early versions. LEAP 2020 is fully backwards compatible. It will automatically upgrade any datasets made with older versions of LEAP, and you can even run both the old and new versions together simultaneously so that you can check and compare results between them. If you're going to do that, we suggest you select a different folder for installing your datasets in so that you can keep both of them and compare the results. What hardware do you need to run LEAP? LEAP runs on pretty much any standard Windows-based PC. It's not designed for Macs, unfortunately, although you can run it on a Mac if you have virtual machine software. There are two flavors of LEAP. There's a 32-bit and a 64-bit version. The former is a little bit faster, the 32-bit, but the 64-bit allows for building much larger models. Generally, we now recommend people that go for the 64-bit version, and particularly given some of the features we'll show you today, some of the new mapping features, some of the new optimization features, it's probably better to use the 64-bit version. Will you need a new license to use LEAP 2020? No. Anyone with an existing valid license to use LEAP can upgrade to LEAP 2020 and use their current license at no charge. That includes also the new NEMO framework, but Jason, again, will tell you more about that. Generally, what are the licensing conditions for LEAP? Is LEAP free software? LEAP is not open-source software. Anyone using LEAP is required to get a license. However, licenses are free for governments, academics, and NGOs in low-income and lower-middle-income countries, and it's also free for students worldwide. Users in upper-middle-income countries can get a license at a substantial discount. All other users need to get one of our standard licenses. Our basic licensing philosophy or policies are based on sort of ability to pay. The basic idea is to ask those that can make a contribution in the richer countries to do so, so that we can continue to develop and support LEAP and make it available free of charge to our developing country partners. Finally, before we get started, let me say a quick word about the new name. You may have noticed that the long name of LEAP has changed from the old one, which was Long-Range Energy Alternatives Planning System, to become now the low-emissions analysis platform. The new name reflects LEAP's broad and focus beyond just energy and its increasing use for climate and sustainability planning. It also reflects, I think, the urgency of finding new development pathways that achieve low levels of greenhouse emissions. So we thought it was a better name and we decided it was time to make a change. So what I'd like to do now very quickly is review the agenda and then we'll get started. So you've already heard me do my introduction. So we're going to run through a few things. First, Jason is going to introduce this brand new optimization framework called NEMO that we're all very excited about here. Then I'm going to show a very quick demonstration of how to apply NEMO and some of the new time slicing features in LEAP and also show you how to use energy storage in LEAP as well, which is a brand new feature. Then Sylvia is going to take over and give you a demonstration of two new results visualization features, marginal embainment cost curves and decomposition analysis based on the IPAT methodology. Then I'm going to give you a very quick demo of how to apply the new mapping features in LEAP, which allow you to look at emissions hotspots and emerging emissions hotspots. Then we'll do some final remarks. I'll go over some of the features that we haven't had time to cover today and also tell you a little bit about what's on the broader horizon for LEAP development. And then hopefully, if I stop talking now, we'll have a few minutes for questions and answers. Okay, so that's what we're going to do today. Now I'm going to turn it over to my colleague, Jason, who's going to introduce us to this brand new optimization framework called NEMO. Over to you, Jason. I think your microphone is still muted, Jason. Thank you, Charlie. Hopefully you can hear me now. Great. It's a pleasure to be here with everybody and to share with you these really exciting developments that we have in the LEAP platform, including the work that I've been doing with colleagues on the NEMO tool, which I'd like to introduce to you now in this section. Let me just make sure that you can adequately see my screen. Sylvia, Charlie, can you see that? Very good. Yeah. Okay. Great. So I'd like to introduce you to a new, new open source energy system modeling tool that we've developed called NEMO, which stands for the next energy modeling tool for optimization. This is a high performance tool has been built for high performance from the ground up and really been built to analyze critical questions in contemporary energy policy. We have really designed this to look at some of the cutting edge energy policy issues that we know that people in our community and the folks here on this webinar are dealing with. So really to analyze things like the grid integration of variable renewable energy, deep decarbonization pathways, what might be robust responses to climate change and how to manage energy systems in view of climate change. We developed this tool in a language called Julia, which is a new open source language for high performance technical computing. And really we chose that because of the size of the community around Julia and the speed at which that language is developing and its performance characteristics. And the tool has been built to work either in a command line mode or more relevantly, especially in this webinar with leap as a graphical user interface, the idea of being to pair the visualization data entry and data management features that you get with leap with the backend power of the optimization framework in NEMO. NEMO we're releasing as an open under an open source license, which makes it freely available to everyone. And in order to use it with leap, we would still have to have a normal leap license. But the tool is freely available to be downloaded in a couple of different formats and used under an open source license. And I should say that NEMO is a full energy system optimization tool, which means that you can optimize both the supply side and the demand side of your energy system with NEMO. But at the moment, we have it hooked up to leap so that you can optimize on the supply side with leap with one transformation module at a time. And I think that the the optimization of the full supply side with NEMO is a feature that we're going to be developing in the near future. So where did this come from? This is a project that's been a couple of years in the works for us. And what really motivated it for us was was our understanding, having worked with so many people around the world on energy systems analysis, that there's a pretty significant gap in the tools that are available for optimization modeling to folks around the world. On the one hand, you can pay for usually quite expensive optimization proprietary optimization modeling tools, which are usually powerful, but lack transparency and are not necessarily affordable, particularly to users in low and middle income countries. And on the other hand, there are some open source optimization tools that are available, but they lack some key features that are needed to analyze really important questions and contemporary policy. They also have pretty significant performance limitations generally. So what we were aiming at with NEMO was to build a tool which could fill that gap, which would be at once affordable, transparent, powerful in terms of its functionality and have good performance. And in doing that, we're really continuing the leap mission of, if you will, democratizing energy modeling and energy systems analysis by bringing advanced capabilities to a broad range of users, particularly by having an excellent user interface associated with them and by having good help and support and technical assistance capabilities packaged with the tool. So the idea with NEMO then is to bring really advanced optimization capabilities to this broader set of users that we work with, especially in low and middle income countries as a way of assisting sustainable energy transitions in those countries. We're hoping that NEMO is going to be part of the platform for supporting the next generation of sustainable energy analyses in LEAP. We're really excited about all of the new features that we're rolling out in LEAP now and think that they really do position us well to answer some of the emergent questions in policy development. How can we get to zero carbon energy systems? How can we integrate renewables into power systems in a way that assures reliability and reasonable costs? NEMO has a lot of different features. This is an overview of some of the key features that you might be interested in as modelers and analysts. It is a least cost optimization tool. That's its basic principle of simulation on the supply side and the demand side. It will allow you to simulate multiple regions and trade between regions. It covers energy storage, all many different types of storage. Usually NEMO is used in a long term or is intended to be used for long term modeling, but it can be used for shorter term modeling as well, such as production cost modeling in an electricity system and you could consider energy storage that works over shorter timeframes. One of the really neat features about LEAP 2020 and NEMO is that they have flexible, really flexible time slicing capabilities, which Charlie will talk about in a second. That allows you to look at the way that energy storage operates over slices as small as an hour to larger slices that may span multiple days or seasons. NEMO also has the ability to simulate nodal networks in energy systems like transmission grids and pipelines and to model power flow and pipeline flow in gas and oil pipelines. Power flow modeling is done with direct current optimized power flow modeling, which is a fairly standard technique for long term models and planning models. It will simulate emissions and emission constraints, including carbon prices and pollutant prices and renewable energy targets as well. For performance reasons, we've built a NEMO to have parallel processing within it to try to speed up the processing of model construction and parsing as much as possible. Another really important feature is that it covers multiple solvers. They're compatible with multiple solvers. We ship NEMO with compatibility for five solvers right now, two open source solvers and three commercial solvers, and we're in the process of adding new solvers all the time. So it's really flexible in terms of the program that you use for solution of the optimization problem. And we chose to put the data in NEMO. Its data backend is an open source relational database, which means it's very easy to get data in, very easy to get results out, and you have the power of SQL structured query language at your disposal if you want to use that to manipulate data. Most of these features are already accessible within the LEAP environment and with LEAP as the user interface. The two features that I've starred here on this slide are features that are for which we're developing the LEAP integration, and that's coming soon. I think you can expect to see that in the next few months. So I would be remiss if I didn't say where can you find NEMO. We've got a project website for it on GitHub. The link is given here. We distribute NEMO in two different ways. You can download the source code and run the source code yourself if you want to install Julia. We also provide an all in one installer for Windows environments that's compatible with LEAP, and that can be downloaded directly from the LEAP website at energycommunity.org. And in keeping with our approach to building tools that are usable as usable as possible, we've put up documentation for NEMO which covers really a great many things that you can do with it and everything that you would need to know to use it. That's available at the link that you see here. What I will do is very quickly show you the websites that I was just mentioning. This is our NEMO GitHub page which starts to introduce you to the tool and it's now publicly available and folks are welcome to come here to read about it and to link to our documentation which describes really everything that you would need to know to start using NEMO right away. And again if you want to download the tool you can come either to this GitHub site or you can go to the LEAP website and download it from the LEAP website. We just ask that you register to download it as you would register to download LEAP normally. So without any further ado I'm going to hand it back to Charlie who's going to start taking you some of the through some of the integration between NEMO and LEAP and showing you some of those capabilities. Thanks Jason and I should apologize for making Jason do that bad joke about finding NEMO. I made him do that. Okay so what I'd like to do now then is Jason's given you some of the exciting developments that we've been working on with NEMO. I want to show you how some of those things are integrated into LEAP and some of the other changes we've made to LEAP 2020 to let you do some interesting new types of modeling that you couldn't do in the earlier versions of LEAP. So we're going to show you three things mainly. First how to use the new NEMO tool, how to use the new time slicing capabilities in LEAP which are much more flexible than before and much easier to use as well and how to do NG storage modeling. So in the past you've been able to use LEAP to model overall energy demand and supply but it was always difficult to get a really sort of detailed or nuanced picture of how that supply varied within the year to look at sort of seasonal and daily variations in demand and supply and those issues are really important and they're going to be increasingly important in the future as we start trying to transition to much lower emission development pathways. We're going to need to find ways of integrating much larger amounts of variable renewable power into our grids and how we're going to do that while balancing the seasonal and daily variations in demand. Well that's probably going to require much more flexible operation of grids and it's going to require energy storage as well and it's probably going to require energy efficiency on the demand side to make space for sort of growing electrification which is likely to be a very key strategy for low emission pathways. So for example if we're going to electrify our transport fleet we're going to have to make space for that by doing efficiency in all of the other sectors. So I'm going to give you a little sort of a demonstration of those kinds of things how you might model them in LEAP. I'm going to show you a fictitious data set that covers some of those topics so bear in mind that what I show you here is not a real country these are not real numbers but a lot of the input data I've used in this fictitious data set is pretty realistic so I think it's quite interesting. So let's start by looking at some of the data inputs. So the first thing I'm going to do that's maybe different from what you've done when you've used LEAP in the past is I'm going to set up my demand analysis in a slightly different way. So the first thing I'm going to do is visit the setting screen in LEAP. You won't have seen this screen before in fact you will have seen this screen before but it previously was called the basic parameter screen so we've renamed it the setting screen which is a bit more standard and I'm going to set up this analysis to do to do the calculations in a different way instead of specifying my load shape for the system as a whole which is what most people typically do in LEAP. Here I'm going to actually specify the load shape so that each individual demand device. So I'm going to specify how the what lighting looks like, what air conditioning looks like, what refrigerators look like and I'm going to specify those individually and then LEAP is going to build up the overall system load shape and that will be important as we look at policies. So if our peak is caused by air conditioning for example then we can focus on air conditioning is a really important area to do energy efficiency so that we can flatten the peak load and flatten the load shape and that will really help with making sure we can meet our loads in the future. Okay so that's the first thing I've set up there is load shapes for each demand device. So let's come down now into to see how we can actually set up the time slices that specify the variations within the year. So this is a screen that's been thoroughly redesigned in LEAP 2020 let's go to this time slices screen here under the general time slices screen and here you can see I've got a list of time slices so this is how I've divided my year up. So here I've divided it up into seasons and then within each season I've divided up into 24 hours. You weren't able to do that in previous versions of LEAP, the previous versions of LEAP couldn't look at hourly variations in the band but the new one's much more flexible and it's really easy to have different configurations of that. So for example here under the setup screen I can set up what kind of time slicing I want. There's a really easy to use button here where you can pick whether you want to do seasons, whether you want to do months, whether you want to do weeks. Within that you can do every day of the week, you can do weekdays versus weekends and then within a particular day you can divide it up into two four or 24 hourly groups and just selecting one of those will be enough to reconfigure all your time slices in LEAP. So I'm not going to do that now but it's very easy to do that. You can even configure things in even more detail. For example if you're in the Middle East the weekend is a different day than it is here in the US so you can even configure for example what's the definition of a weekend. Here we have Saturdays and Sundays but you could configure it to be Fridays and Saturdays which is how things are done in the Middle East more commonly. Okay so that's the new time slices screen it's much easier to use and it's much more flexible and much more detailed than before. Okay so once we've done the setup screen let's go back and look at how we're specifying our annual energy demands. So here's the demand tree in LEAP and here we've got a household sector. Here you can see I've divided households down into different end uses and here you can see for each end use you'll probably be familiar with this variable where we specify the overall annual energy intensity but now we have this new variable called load shape where we can specify how the shape varies within the year. So here there's an equation saying this is the yearly shape for air conditioning. So let me show you what that's drawing upon is sort of a library of different load shapes which are specified down here under the yearly shape screen. So let me show you that screen quickly. So sorry that's a little bit big. So the yearly shape screen is a place where you can build up load shapes of different demand devices. So here for example you've got air conditioning and let me show that so you can see the variation by the seasons. So here you can see air conditioning the peaks obviously going to be in the US it's going to be in the summer months during the sort of the middle of the day. Heating is going to be more important in the winter. There's the yellow curve here much less important in the summer. Lighting you as you'd expect happens in the early hours of the day and in the evening. So whereas refrigerators are a much more sort of flat load shape there's not that much variation. So you can specify these different load shapes for different devices and then leap will stack them all up to give you the overall system load shape. And it's really easy even though there's many many pieces of data here. It's really easy to import them because you can tend to find hourly load shapes for all 8,760 hours. Those things tend to be available on the internet and you can use the import feature in in leap here in order to quickly bring them in. I won't do that now because I don't want to make changes to this data set but you know in just a few seconds you can import and create these different load shapes. So once you've created those load shapes you can come back down here under your different demand analyses and allocate those load shapes to the appropriate technologies in leap. So here I've used the air conditioning load shape for the air conditioning technologies the heating load shape for heating technologies etc etc. So the lighting technologies like that. Okay so that's on the demand side. So what we want to do on the supply side is work out how we're going to meet those varying demands over time. So let's have a look at our transformation sector now. So down here I'm going to close the demand branch come down here under the transformation branch and here what we've got is a list of different processes that might be available for meeting those demands and one thing you can see that's different here compared to the old version of leap is we have this little battery icon so what we can do now is specify energy storage. So that was not available in previous version of leap 2018 but energy storage is going to be there available. It can be charged up when you've got plenty of energy or it can be discharged to help meet the demands in other time slices when you haven't got enough energy so it can help sort of flatten the overall load shape. So that's a brand new feature in leap. Now the other thing I want to show you is how you connect leap up to Nemo. It's really easy to do. Once you've installed Nemo leap will automatically see it. You don't have to do anything to manually connect to them and you can check to see if your version of leap is connected to Nemo if I go to the about screen. You can see down here yes it's found Nemo so Nemo is correctly installed and in order to use Nemo it's again very easy to do. If I just come to the module branch electric generation if I come to one of the scenarios there's this variable could optimize. So in this case when you're doing your normal modeling in leap your normal simulation modeling that variable would just be set to no just the words no but in this case we set it to tell to tell leap to use Nemo and to use the Cplex solver. So as Jason mentioned Nemo supports all sorts of different solvers it supports free solvers which are free which is great but they tend to be rather slow but it also supports what I call these sort of industrial strength solvers. So in particular at the moment it supports Cplex and Grobe which are expensive but very fast solvers and we're going to try and make sure that Nemo and Leap support as many solvers as possibly can. You have to buy the solvers separately unfortunately they're not part of Leap but at least you have the flexibility to be able to select them and setting them up is really easy you know you just click on this orange button over the right and here you can select you know no to do simulation yes just to use the default framework and solver and then here here are the different solvers you have available so we do still support the older solver that we've used for many years Osmosis but we're generally transitioning now to make greater use of Nemo so in this particular model I've set up Nemo to use Cplex and I will say that if you want to do storage modeling you do have to use Nemo we don't support that under Cplex okay so let's I'll try and show you some results in a second but you can see here I've set this up on the supply side to have a range of different technologies there's nuclear technologies there's natural gas there's wind there's solar and there's energy storage and I'm going to let Nemo decide which of those technologies to build and how it wants to operate those different technologies so before I show you some results let me just quickly show you what scenarios I've set up here so here we have a baseline scenario so the baseline scenario is sort of a projection of growth into the future but it has very few policies so it doesn't have much energy efficiency it's not trying to switch away from gasoline transport to electric vehicles it's not doing very much energy efficiency at all on the demand side and then but it's still it's using Nemo on the supply side to work out what kind of electric generation mix it should have the policies scenario explores really quite sort of rapid introduction of energy efficiency and electrification so on the one hand we're trying to do as much energy efficiency as we can we're switching to efficient lightings efficient air conditioning there's efficiency going on in the industrial and services sector and then on the transport sector we're very rapidly switching from gasoline vehicles to electric vehicles so in both of those scenarios I'm using Nemo but I've blocked Nemo from doing any energy storage in those first two scenarios so I just want to show what it would look like if you don't do energy storage and then in this scenario it's exactly the same as the policy scenario but I've just switched on the ability for Nemo to do storage and that's really easy to do it's just there's one variable where you set the maximum capacity that you're allowed to build in these scenarios I set it to zero for storage and in this one I just allow it to do as much as it likes and then I've finally I've got one other scenario where I'm setting additional emission constraints so I just want to see how Nemo will behave if I tell it it has to achieve much lower greenhouse gas emissions so let's look at some of the results I think I might need to open up this other one that I've already pre-calculated because I think I was making some changes there okay so let's try and look at some results the first one I want to show you is is the overall electricity demand so here's the electricity demand in the baseline scenario so you can see it's it's just gradually growing over time it's not doing much efficiency and all of the sectors are growing you know it's like it's sort of like a rapidly developing middle income country something like that let's look at the policy scenario though which is a bit different so the policy scenario you can see first of all that the growth in the household sector is much lower than it was in the baseline scenario that's because I've done a lot of energy efficiency investments I'm doing all sorts of much more efficient air conditioners more efficient lighting things like that so that brings down the demand but on the other hand I've said let's switch from gasoline to electric vehicles so let's look at what would be implied by having lots more electric vehicles that causes a big growth in electricity okay so that's the total annual demand over time but what does that look like within each year so let's look at a different chart now that looks at the time slices so that's this one here I can see I'm running out of time rapidly so here you can see for example for the different sectors how the demand varies within time so you can see here for example there's big peaks in the summer in the household sector if I zoom in on the household sector by kicking up here you can see a lot of that's caused by this air conditioning so really big peaks in the baseline due to the air conditioning but if we do our policies scenario it's much less right it's because we've invested in efficient air conditioning so we've brought down the peak the overall load shape it's still very peaky but it's much less peaky than it was before or if we look for the sector as a whole though here's our household sector but now we've got this big growth in the orange one which is the transport sector so that's sort of the electric vehicles growing over time okay so how are we going to meet that demand over time well so let's look at a different variable here let's look at the power generation over time so here's our baseline again so the power generation over time you can see Nemo has chosen different plants and it uses different plants in different time slices in order to identify the sort of minimum cost of providing the electricity so you can see here it's using solar during the day when solar is available so in Leap you can set the availability of your power plants to be different in different seasons and different times of day so the solar is available during the day the wind varies as well but more by season rather than by the day and then so it's using the wind and solar when it can it's also using some existing hydro that it had lying around and then in the end it's using the natural gas so it has to fire up the natural gas a lot to meet the peak demand okay so let's look at the policies but with storage now and you'll see it's very different so here now we're trying to meet our demand but we've allowed Nemo to build some storage so you can see here instead of having to use nearly as much natural gas now it can rely on storage so there's some time slices of the year when there's lots of solar or wind available when it puts electricity into the battery or into the storage and then there's other times of the year when it can use that storage to help meet the peak demand so overall it ends up using the purple the natural gas much less than it did in the other scenarios so that's a much more realistic simulation of what you might need to do to get to sort of much lower emissions pathways and to make use of storage and to make use of energy efficiency so let's see so I think that's about what I wanted to show you oh yes now there's one other very quick thing what does that mean in terms of your emissions so let's just look at one more chart the greenhouse gases by the way I'm using the favorite charts feature in Leap here so I'm quickly switching between different charts just because time is so limited here okay so here's the overall emissions in 2050 from these different scenarios so you can see the baseline scenario had much higher emissions when we went to the policy scenario we dramatically reduced those emissions in part because we were doing lots of electrification and lots of energy efficiency so there's no gasoline vehicles anymore you know they've been replaced by electric vehicles in this orange bar but then the policies with storage let you go even lower in part because you're making use of the storage you don't have to dispatch the natural gas as much to meet your peak demand so getting rid of the dispatch of the dirtiest plants so it helps you squeeze emissions down even further and then the emission constraints scenario is like the policies with storage scenario but it's just squeezing even further so it's making even greater use of storage and wind and solar in order to make the overall demand so there you have it that's a quick demonstration of how you can use three of Leap's new features the flexible time slicing the demand side load shapes and the Nemo optimization framework to examine policies for a transition to low greenhouse gas emissions I should say that this demonstration dataset this fictitious demonstration dataset will make that available and distribute it along with Leap 2020 so you can all play around with this dataset yourselves okay so that's that part of the demonstration I'm going to switch over now and hand over to Sylvia who's going to tell you about MAC curves and decomposition over to you Sylvia thank you Charlie let me share my screen now okay so I will give you a very quick demonstration of two new types of analysis and visualizations that are available in Leap 2020 and you can access both of them in the summary's view in Leap so the first one that I will show you is the MAC curves or the marginal abatement cost curve report the MAC curves are a very useful tool for comparing the costs and the abatement potential of various mitigation options which are usually very context specific so this tool is very helpful for policy makers to prioritize which measures to pursue and to prioritize those that are the most cost effective and the most ambitious ambitious ones so as you can see in this example the MAC curves are comprised of a series of blocks each one of these blocks represents an individual measure which has also been modeled as a separate scenario in Leap so here in the legend you can see a very short description of the mitigation measures that are being considered in this example and for each block the width of the block indicates the avoided emissions so the widest bars represent those measures that have the highest abatement potentials and on the other hand the height of each bar indicates the incremental cost per unit of emission reductions so in the chart the blocks are ordered from left to right according to their cost so at the left you can see those measures that are the most cost effective ones which could even have net cost savings if they appear below the x-axis and to the right you can find the measures that have the higher cost so how to read this for example if we look at the electric vehicles you'll see that this policy has a unit cost of about 50 us dollars per ton you can also see this in the table view which makes it easier to see the actual numbers and it has a potential of around 25 million tons of co2 equivalent the chart also gives you the average cost which is displayed as a line so actually the area enclosed by this line and the x-axis represents the cumulative cost of implementing all of these measures now you'll see that in this example most of the measures are from the demand side so they are related to changes in technology in the household in the transport and in the industrial sector mainly however implementing measures in the supply side or in the transformation sector can also have an impact on the cost or the potential of demand side measures so to explore that let's go back to the analysis view and we will now add a scenario in the supply side so in the list of scenarios you'll see that I have already added a scenario here for the power sector which considers a shift towards a more renewable power generation matrix and also a reduction in the transmission and distribution losses so what I'll do is that I'll select the scenario from the list and tick here where it says including mac report and now we will go back and see how that changed the mac curve but before going back I'll just mention that because of the way in which LEAP generates the mac curves it is only possible to include individual scenarios so that means that this mitigation scenario for example which inherits all of these individual measures cannot be included as such in the mac curve so even if you tick here you will get an error message when you go back to the summary's view so now let's go back to the summary's view and every time that you change something in the dataset we need to manually refresh the mac curve and this is because LEAP generates these mac curves using a retrospective approach so what LEAP is doing now is that it is calculating all of the scenarios to identify which one is the most cost effective one then it plots that one first at the left of the chart like I showed you before and then it recalculates all of the remaining scenarios but this time assuming that the first one has already been implemented and this is very important because it is what let's LEAP consider possible interdependencies between different options so for example how supply site measure can have an impact on the demand side and vice versa so here it's now refreshed and you will see in the chart view that this measure that we just implemented for the power sector is now the one with the highest abatement potential and that actually implementing this measure had an impact on other demand side measures so if we look again at the electric vehicle policy this one let's see it in the table view you'll see that it now has about seven times the abatement potential that it did before when it was implemented in a more carbon intensive system and the cost is about one fifth of what it was before so it's now a much more cost effective policy so this is just an example of how LEAP can consider these interdependencies between different options the MAC curves in LEAP are highly customizable so through this manage summaries button you can add additional MAC curves and have each one with different settings you can update the settings here for example you can change which scenario to be used as the counterfactual or which branches in LEAP should be used to represent the cost and the impact and for example instead of looking at GHG emissions you might be interested in looking at or ranking policies based on their potential to reduce other types of pollutants so you might want to look into black carbon or particulate matters or other types of pollutants so you can apply different filters available in LEAP to create such a report I already saved one for the particulate matters so let me show you that one and you can see here how the same list of measures may rank very differently and some measures for example reducing open burning of waste might not be so important for reducing the GHGs but it definitely has a lot of potential for reducing particulate matter so this is something interesting to analyze when you're trying to prioritize mitigation measures now the second report that I will show you is the decomposition analysis and this one you can also find here in the summaries view and this type of report can help you analyze the trends in scenarios by decomposing those trends into various contributing factors and LEAP does that based on the IPAT and the KAYA Identity Methodologies you can learn more about those methodologies here in the help system within LEAP but in general the default report that is included in LEAP and breaks down the trend in the GHG emissions which is the indicator shown here in the Y axis the first column right here shows the starting value in 2010 for GHG emissions and the last bar shows the value in 2040 so you'll see there was an increase in the emissions over time and all of these intermediate bars represent those contributing factors that explain this trend so in this case there's the population there's the GDP per capita there's the final energy consumption per unit of GDP and the primary energy per unit of final energy consumption and both of these are a measure of energy intensity and finally there's the emissions per unit of energy which is a measure of the carbon intensity the colors in this chart indicate whether each of these factors contribute to an increase or to a decrease in the indicator so here in the baseline you'll see how there was an increase in the population in the GDP per capita which is a measure of wealth and in the carbon intensity but there was a decrease in the energy intensity now besides from comparing two years for a single scenario you can also change the view and you can change and you can compare two different scenarios for a given year so for example in 2040 here we have the baseline and the mitigation scenario so you'll see that in the mitigation scenario which had all of the measures that we saw before in the MAC curve there were lower emissions in 2040 and this was as a result of a further reduction in the energy intensity and in the carbon intensity you'll see that the population and the GDP per capita didn't have an effect or didn't contribute to this decrease in emissions because they were the same in both scenarios you can also see this report as a bar chart so you can see for each year what was the corresponding contribution of each of these factors and just like the MAC curves you can add additional reports and you can edit in this case you can change which are the contributing factors and you can change also what is the ultimate indicator that you're trying to analyze or to explain so again you could select here black carbon other type of pollutant or even premature deaths if you are using the LEAP-IBC module with this another feature here so I think this is all for now and back to you Charlie so you can give the last demo thanks Sylvia okay I hope you can see that so what I'd like to do now the third part of the demonstration is going to show you how to apply the new mapping features so deep has for a long time allowed you to examine the annual average environmental loadings associated with your scenarios including how these might change over time as different sectors grow or different technologies policies and measures are implemented so this is done using emission factors specified for each different pollutant and at each different technology in LEAP's tree structure which you can see over there on the left LEAP 2020 introduces a new feature which goes further than this which allows you to specify the geographic distribution of emissions sorry to interrupt I don't think you're sharing the right screen oh okay thank you let me try again no I'm not am I okay I thought I did should be that one yes okay sorry about that okay don't worry you didn't miss anything so so LEAP 2020 goes further and it allows you to look at geographic distribution of emissions and how those might change over time in scenarios and it also lets you look at those results directly within LEAP in the form of maps so our hope is that this new feature will be useful in helping to identify emerging emissions hot spots as well as for tracking and monitoring progress on reducing the emissions burdens faced by different communities and obviously that's becoming a very important topic as issues of environmental justice are discussed in the debate over what shape a transition to a more sustainable future should take these new mapping capabilities also represent a foundation for some very important intended improvements to LEAP that we hope to make over the next couple of years in particular they'll allow for getting greater insight into the geographic distribution of health and ecosystem impacts which is something we've been doing over the last few years with the IBC module the integrated benefits calculator module which unfortunately I'm not going to have time to show you today so these new geographic capabilities are built around three areas in LEAP first there's a new geographic mapping screen that's used to create a set of proxy GIS data sets so these are data sets containing socioeconomic data that can be used as a basis for downscaling national level emissions to a more local scale so for example you might think that your household emissions are going to be the emissions from your household sector for say particulates they're likely to be distributed across your country in proportion to where the households are so if you've got a data set telling you how many people live in each grid square of your country that's a good basis for thinking about where the emissions from the household sector are going to come from secondly there's a new geography variable built into LEAP that's used to specify how energy use and emissions at a particular branch in LEAP are going to be allocated within the area so for example you might allocate the household emissions in one way but you might allocate your transport emissions in another way so LEAP has a language for allowing you to specify that kind of allocation and then third is a whole new gridded map chart type in LEAP that lets you look at the results of the calculations in the form of map much like you would if you were doing sort of using a standard GIS tool so I'm going to show you each of those three elements now but before I go on I just want to say that a lot of what we've done here in this feature is built on an amazing open-source GIS component called MapWinGIS which is available from the website website mapwindow.org I particularly want to thank Paul Memes who's the developer of MapWinGIS who gave me a lot of help in implementing these features in LEAP so let's look at how mapping works in LEAP the first thing you're going to need to do is to set up maps in LEAP is to make sure that you've downloaded and installed the separate MapWinGIS components we will provide links so that you can download and install those once LEAP 2020 is launched in the next few days once they're installed LEAP should recognize them automatically you don't have to do any extra setup you can simply use the about screen in LEAP down here to see if it's using them and you can see here I have already got them installed okay so the next thing we need to do is to set up our mapping in LEAP so previously in LEAP you could only use mapping if you were doing a multi-regional data set but now you can actually use maps even in a single region data set but the first thing you'll need to do is set up the mapping so let's go to the setting screen and here you can see there's an option to map results to a grid so I've got that switched on once that's switched on I'm going to go over here to the mapping tab and here I can set up the map shapefile so a shapefile is a standard GIS format it's developed by Esri many many years ago it's a completely standard way of representing geographic information on maps so here I've chosen a shapefile that contains the outlines of every country in the world and I'm going to use that as a basis for saying which of the countries in my analysis so this particular data set I'm showing you here is a multi-regional data set it has it's I should say it's completely fictitious data the values I'm going to show you in this demonstration are not actual values for the countries I'm going to show you I've merely picked three random countries just so I can show you some illustrations on a map but none of the analysis is for those countries so please don't think I'm showing you any real numbers here it's just to demonstrate the mapping system so what we're going to do here is first we pick the data set containing the shapefile so that has the outlines of each of the countries I can pick the field that contains the label for the countries I can choose an image layer which is going to be sort of the background image of the map you can either have a static background image or probably it's better just to use the built-in tiling feature which allows which which shows you the background map at various different resolutions and it pulls it in automatically over the internet and then finally you're going to set up the resolution of your results so in this case I've done point one by point one degrees which is fairly fairly detailed uh one thing you'll find is that this particular feature does use a lot of memory so you have to be a bit careful with how detailed you make it in particular you probably want to use the 64-bit version if you're going to do mapping because it does use a lot of memory and the 32-bit version will only allow allow you to look at fairly small countries with fairly big grid squares but here I'm using the 64-bit version so I'm using point one by point one degrees okay so the next thing you need to do is we need to we need to say we need to choose a shape that corresponds to each of the countries in that in our regions so in this particular dataset I'm pretending that I'm doing analysis for Bangladesh, Bhutan and Nepal so in my regions screen I need to map of those three different regions within LEAP which are countries to different shapes in that in that shape file so here I've said Bangladesh maps to something called Bangladesh in the shape file Bhutan corresponds to Bhutan, Nepal corresponds to Nepal okay so the next thing I want to do is to actually choose some socioeconomic data that's going to be the basis of allocating my emissions so here I'm going to go to this brand new screen called geographic mapping so under geographic mapping I can choose I can set up a whole list of different datasets that contain socioeconomic data so here I've just got two by way of example I've got a population dataset and a roads dataset so I'm going to use the road dataset to allocate my transport emissions I'm going to use my population dataset to allocate my household emissions so those datasets are standard format GIS raster files they you can see that these different files they don't even have to be the same resolution that's okay because LEAP is actually going to rescale and create a common set of raster datasets that it will use for its calculations and that it will use for showing the results on on a map once it's done its calculations yeah you do want to be careful though if you use too small a number then you know you might end up with very high memory use and again it's best to use the 64 bit version but they don't have to have a common cell size LEAP will organize that for you itself so once you've added this list you can click the refresh button and it will generate a common set of maps one thing of the other thing you have to be careful of is that these these files in this case they're TIFF files which are a standard GIS graphic raster file it does support a bunch of different file formats another very common one is netcdf it doesn't support that yet we're working on that and we hope to add that in the near future but it does support five or six different standard GIS file formats one other thing you have to be careful of is each of those files has to have extents that cover the area you're modeling so in this case both of those are global data sets so they cover all of the three countries that I want to model okay so the next thing you've done now once you've set up those those proxy data sets is you can say you can allocate those two different branches in LEAP so let's have a look down for example at the household sector so here under urban cooking for example we've got two different kinds of stoves and probably if you've used LEAP in the past you'll be very familiar with specifying the emission factors over here under the average environmental loading variable but now in LEAP 2020 we have this new variable called geography so geography is where you can specify how you're going to allocate those total emissions amongst the grid squares of your data set so here for example I'm allocating the emissions from natural gas goes on the basis of population or under transport I'm going to allocate my car emissions on the basis of roads so the roads data set contains the number of square kilometers in each grid square and that's a reasonable proxy for how we're going to allocate our emissions you can even on the supply side you can allocate the emissions from your power plants maybe if you have specific power plants you could even put in a specific latitude and longitude so you could say this power plant is in this particular grid square so when LEAP then does its calculations it's going to take the changing emissions over time and it's going to allocate them out based on these rules to each of the different grid squares so let's look at some results okay I'm going to look at one that's already calculated I think I made some changes there then we don't have to wait for it to calculate hopefully this works okay so here we are in the results view in LEAP and you can see there's a standard set of bar charts showing you the total emissions from each of our different regions again these are fictitious numbers please don't please don't believe these numbers there's different numbers for different sectors but what we have now is a new kind of report called a gridded map report so let's select that one so this is a brand new type of report that you can see in LEAP that shows you how the emissions are spread out over the region that you're modelling and I think I've got another one here that maybe looks slightly nicer that one first so this is like using sort of standard GIS package you can see that most of the emissions are fairly even across most of the points but there's a few places like up here and up here where we've got a few point sources that making the emissions higher and there's all sorts of options for changing how you visualise these results so you can change the background image you can change the number of divisions you can select an area or you can pan the whole map to look at different parts of the area you can and you can change the way that the colour coding is done so here we're using sort of linear divisions but another way of doing it would be to show equal counts so there's the equal numbers of squares of different colours and you can use the background here we're using the Microsoft Bing maps to give us a tile background it supports various different backgrounds you can have no background at all or you can pull in like one of the open street ones for example so another really nice way of looking at these results is you actually want to see what are the numbers underlying these different grid squares so what you can do is change to the split view so the split view shows you a map up the top but then it shows you the individual squares and the emissions in those squares down below and if you hold down the control key as you navigate across the map at the top it will actually home in on the particular square that you're looking at on the top and it will even show you the trend in the square that you've highlighted over in a little chart on the right so there you are that's the mapping view in Leap that's what I wanted to show you we've only just started exploring the possibilities of this new feature but we think it's going to be quite an exciting new feature for exploring sort of emerging hot spots and also sort of giving communities an idea of what different transition pathways might actually mean for them in a much more localized scale down sense so that's the mapping feature okay so those are the three main features we wanted to show you today I just want to wrap things up now by showing you with a few final thoughts and then we're going to turn to questions and answers after that and I know we're over running here by a few minutes so I apologize for that but we're going as fast as we can so let me just mention quickly a few other new features that we didn't get a chance to mention today so those are only three of the things that are in Leap 2020 but another really major feature is this new ability to look at indoor air pollution health impacts monitoring so Leap has this new built-in capability that complements the previous feature of looking at ambient air pollution and its health impacts we've now added a module to also look at indoor air pollution it's based on the HAPIT methodology which basically is looking at the members of households and their level of exposure to pollutants and how that translates into impacts and both of the methods are compatible with each other they're both using the relative risk functions of the global burden of disease approach and here you can see just sort of an example of the kind of results you can get out where we're looking at premature deaths you can disaggregate them in different ways you can look at the indoor deaths versus the deaths from outdoor and Leap has a built-in methodology to try and help you avoid double counting them because both of those there is overlap between indoor and outdoor air pollution but it has a method for avoiding double counting we've also updated the integrated benefits calculator module in Leap so it now can provide allow you to look at impacts of air pollution disaggregated both by age and by gender so that can be really a powerful way of looking at some of the development implications of different pathways so you can see how much are your policies going to benefit children or women within households who are doing most of the cooking and what might different transitions mean for them in terms of their health and then finally I just want to mention there's a whole slew of other improvements to Leap lots of usability improvements lots of quality improvements I know many of you who use Leap a lot will appreciate if it doesn't crash as often as it used to we think it's going to be much more robust we've built in all sorts of methods for testing it in a much more rigorous way and we think it will be a much more usable and robust system going forward just one last slide then on what's next so we're continuing to develop Leap and these are some of the things we're working on for the next year or so so first is a new plugin architecture we're moving to try and make Leap much more modular and that should broaden the number of people who can take part in developing new methods that are used within Leap I'm the main developer of Leap and sometimes I'm a bit of a bottleneck on how much we can do with Leap because I'm the main coder of the tool so this new plugin architecture should free things up a bit to allow more people to take part in the development of Leap we're also looking in the near term at improving Leap to make it much more useful for teams of people so in the old days people used to use Leap as individuals but now increasingly many governments have teams of people working on climate change and air pollution abatement so we found it's really important to support people when multiple people want to use Leap together so we'll be working on that Jason is making all sorts of improvements to NEMO he's mentioned some of them already but some of the things he's been working on include modeling of networks and power flows multi-objective optimization making the optimization even faster than it already is and all of those things it's really nice having NEMO be sort of something that's developed in-house because as we make these improvements to NEMO we'll very quickly be able to bring them into Leap and make them usable to a very broad audience and then finally I just want to mention that SEI has a new major new initiative which Jason is the co-leader of on integrated climate and development planning and through that we're trying to link up Leap to make it much much better at examining the macroeconomic and development implications of energy scenarios so looking at things for example like equity and the jobs implications of different scenarios that's something that I don't think any model is particularly good at at the moment but it's going to be something that's increasingly important as people think about transitions to more sustainable scenarios and I think it's particularly timely to be thinking about those things now as the world is starting to think about sort of rebuilding itself as we start to get past this immediate crisis of the COVID pandemic so that's something we're going to be working on over the next couple of years so what we'd like to do now is move to give you a few minutes I know we're running over time here but we're going to take a further five minutes if people have questions and answers and I can see some people have already posted questions and I'm going to turn it over to Sylvia now who's going to ask some of us to answer some of those questions Sylvia over to you Yeah so we've been receiving a lot of questions thank you everyone for posting them and for participating we've already answered some of those so you can see them in the Q&A window and I think also Charlie what we can do is that we can post all of these questions in the website or yeah some some of them at least there are some that are very related we have questions about energy storage time slicing and load curves GIS mapping and the LEAP IBC module but I don't know if you want to take on some questions now yeah do you want to just read out a couple of them and we'll just try and do a few of them now before we I think we've got like five minutes maybe okay let's see and I'll let you choose who to answer okay so maybe the last question then that came in is in LEAP well this is about LEAP IBC if there will be a facility to input and analyze at city level impacts yes so that's something we'll work on at the moment is an urban version of LEAP IBC so for those of you who've used the IBC module in LEAP that's a module that lets you look at the health and ecosystem impacts associated with your emissions scenarios up until now that's always had to work at the national scale but we are developing a version of it which should be able to work at the city scale we've actually gone quite far with that already and it's being tested out already in Accra Ghana but we're trying to generalize that to make it more useful in other in other situations as well and I can see a related question there from Gregor's is asking is IBC still dependent on having 2010 as a start year it is at the moment but very soon it won't be Gregor's we're adapting that so you'll be able to use any year as the starting year Sylvia okay another question about the time slicing and the load profile is if the new version of LEAP can set the time slices for load profile to 15 minutes no now at the minimum at the moment is hourly yeah we don't we haven't gone down below the hourly level it would be nice but it would also be incredibly memory intensive to go beyond that at this point okay another question is if it is possible to compare scenarios when using the new time slice view yes I didn't show that but certainly when you're showing the LEAP results view has all sorts of capabilities for helping you compare scenarios you can look at how one how one scenario is different from another scenario or you can see how you you know what what energy or what emissions you avoid in one scenario versus another scenario and you can do that for all of the time slicing reports as well so I don't think I showed that as an example but yes all of those kinds of capabilities of the results view apply to time slices as well very good there's another question here which I find interesting it says and normally LEAP is using single system load curve at the base year and applies it across all years in the planning horizon currently Brian Joseph is on a research modeling modeling the effect of COVID pandemic in the power sector in the Philippines where in the system and or sectoral electricity demand is greatly disrupted and changed so he's asking if it is possible to input different load shapes for pre COVID COVID and post COVID periods on the power sector well that feature I showed you I think the way you would do that is you would have to say how is the how are the end uses and the technologies likely to change in a pre and post COVID setting but that demonstration I gave earlier I think is shows you exactly how to do that so you could but you could do something similar to that sample data that I showed earlier and and you know that will then the the load shape will change over time based on different technologies coming in or going out so yeah have a look at that data set and I should say you know that data set I will make that available on the LEAP website and you'll be able to download it and play around with it in the in the 2020 perfect so another question about the mapping says how the LEAP 2020 takes into account local meteorological conditions like wind flow and rainfall and chemical reactions in the atmosphere when producing the gridded maps right so at the moment it doesn't because it's only going as far as emissions it's showing you what are the emissions in each grid square but that's sort of you raise a really good point because if we want to start modeling concentrations or if we want to start modeling impacts then the emissions coming out of the need to be input into a model that thinks about all those things it thinks about the meteorology meteorology and the chemistry and that's something that IBC does at the moment but IBC does it in a kind of a black boxy way so IBC has built into it meteorology and it has a sort of a global atmospheric geochemistry model but our ambition and one of the reasons we've been doing this mapping is so that we can start to make that less of a black box so that you can actually you know have as the emissions as the emissions vary over time you can feed that into model and look at all those issues but for now we've only taken it as far as emissions for now great and maybe we can take the last question and then we can reply the rest in the website since we're already above time so and Felicia is asking if LEAP 2020 is capable of modeling CCHP three generation not sure I know what CCHP is confined cooling heating and power is it yeah it has it does have some capabilities for looking looking at that it's not it's not got a super level of detail for that there are maybe other tools I think Balmoral is another modeling tool it's very good at looking at distributed heating and cooling it does have some capabilities on the demand side for doing that but it might not have a lot of detail on those kinds of issues anything else I can see ones here saying how can we generate a GIS shapefile for example for a region usually you can just find GIS shapefiles you know there's lots of them on the internet but it's also if you have any standard GIS software you can also edit them very easily I'm not a GIS expert myself but I understand they're very easy to manipulate and LEAP makes use of all of the standard shape shapefiles and other GIS shapefiles so any standard GIS package should be able to help you do those things there's lots of good free ones as well there's something called QGIS which is an open source GIS software tool that you could use so there's lots lots of things you can use out there and there's lots of good international data sets which we've linked to in the LEAP help files that you can draw upon as well containing sort of socioeconomic data which would be a good basis for for doing the mapping there are also a few questions on the LEAP, WEAP and NEMO integration so maybe a quick word on that what was the question so basically that the new map given the new map features if there are any major updates on the LEAP and WEAP integration and also if LEAP and NEMO can still link with WEAP LEAP and NEMO can certainly link with WEAP in this particular version of LEAP we haven't made any major changes to how LEAP and WEAP connect to each other they still can be connected to each other as they were before we have some ambitions in the next year to improve the LEAP and WEAP linkages we feel that's kind of like our first version of it but particularly given the improvements we made to the time slicing in LEAP I think it would be very good to revisit the way that LEAP and WEAP are linked together but that would be something that's coming over the next year so for now it's pretty much the same as it was before but you can certainly use you know the NEMO model in any sort of integrated LEAP model for those of you who don't know what I'm talking about WEAP is the sister tool of LEAP which is SEI's integrated water resource planning tool and those two LEAP and WEAP can be used together to do integrated energy and water resource planning anything else maybe I think we're basically right after time at this point so let me just wrap it up very very quickly then you can see on the Q&A page that's the the LEAP website energycommunity.org the SEI website is also listed there LEAP 2020 will be available this week on that website we're just doing a few final bits of tidying up but it's ready to go and if you look for it later this week you should be able to download it so I hope you will all give it a try and I hope this webinar has been useful to you thank you everyone for attending we've enjoyed telling you about LEAP and we hope you'll be in touch with us and let us know how you're using LEAP and let us know some of the exciting things you're doing with it so that's all from us for now thanks for coming and we hope to hear from you again soon bye bye for now