Electricity Maps

Environment Variables

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Host Chris Adams sits down with CEO Olivier Corradi and Tech Lead Íngrid Munné Collado of Electricity Maps, a company that leverages data to enable decarbonization of electricity grids. They discuss the complexities of carbon intensity data, the role of accurate forecasting in renewable energy, and how this data helps optimize electricity usage for sustainability. Olivier explains the origins of Electricity Maps, their goal of providing real-time carbon footprint insights, and their theory of change—targeting citizens, corporations, and institutions to create a greener future. Tune in to hear about the history, frontiers, and future of this engaging field.

Learn more about our people:

Chris Adams: LinkedIn | GitHub | Website
Olivier Corradi: LinkedIn | Website | X
Íngrid Munné Collado: LinkedIn | Website | X

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TRANSCRIPT BELOW:
Olivier Corradi: The dream is to get to street-level granularity, and this is why flow tracing, by the way, is so important, because as you increase the resolution of what you're looking at, then obviously if I'm looking at just a city, most of the electricity is actually produced outside of the city, and that's also why, by the way, the whole forecasting challenge has just become massive. Try to forecast how clean the electricity is going to be at every location on the planet. Obviously, you need really good renewable forecasts as well. And that's what civilization needs if it wants to rely significantly on these renewables that are intermittent.

Chris Adams: Hello, and welcome to Environment Variables, brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software.

I'm your host, Chris Adams. Hello, and welcome to Environment Variables, where we bring you the latest news and updates from the world of sustainable software development. I'm your host, Chris Adams. We often talk about carbon intensity, or how clean electricity is on this podcast, as one of the levers for making software more sustainable.

To know this, you need to get the data from somewhere, in a form that's easy to understand and consume. So today we're sitting down with two people who live and breathe this data from Electricity Maps. Electricity Maps is a company at the forefront of enabling a data-driven decarbonization of electricity and today we're joined by both Íngrid and Olivier from the firm.

Hey folks, how's it going?

Íngrid Munné Collado: Hi.

Olivier Corradi: Hi, Chris.

Chris Adams: Cool. Folks, we'll talk about the origins of Electricity Maps in a second, but before I do, I just want to give you a bit of space to introduce the two of you, actually. We normally go by surname first, so I think Olivier Corradi, you're ahead of Íngrid, so can I give you some space to introduce yourself first, and then we'll do the same thing for Íngrid, alright?

Olivier Corradi: Of course, and thank you so much for the invitation and for inviting us to the show. So, my name is Olivier, I'm originally a machine learning engineer and data scientist. My background has been academic, I've worked as well in the research industry with IBM Research and Simulating Electricity Grids. And I would say the most relevant thing that I'm doing now is Electricity Maps that I founded eight years ago.

Chris Adams: Okay, thanks for that, Olivier. And Íngrid, over to you, I suppose.

Íngrid Munné Collado: Hi. Hi, Chris. I'm Íngrid. I'm the Tech Lead at Electricity Maps and I'm very happy to be here. More specifically, I'm part of the grid forecast team where we built, as we have said, forecast models for renewable energy sources and other signals such as carbon intensity. I've been recently involved in replacing and improving the weather forecast data that we use at Electricity Maps to achieve better forecasts.

And yeah, outside of work, when I'm not working, I'm either going to CrossFit where I'm trying to learn how to do a pull up, which I don't know, or knitting and just making sweaters. So quite, quite contrast.

Chris Adams: Cool, thank you for telling me. I'm glad you mentioned weather, actually. I mean, as an English person, we talk about weather the same way that we breathe, I suppose, and as I understand it, you folks are both calling from Denmark, right? We're a little bit chilly today than it otherwise would be, right?

Íngrid Munné Collado: Yeah, it is quite cold for September. I think it's quite usual, but it would be a bit on the colder side.

Chris Adams: Yeah, same here. We, it's, Berlin has, someone has switched off the summer, so we now had to, like, I've cycled in with my coat for the first time today, and did not enjoy that. Okay, so I should introduce myself, actually, folks, if you're new to this podcast. So my name is Chris. I work at the Green Web Foundation as the executive director there.

I also work in the Green Software Foundation as one of the co chairs of the policy working group where we Basically, work on policy to see about coming laws and things that we might actually want to respond to or help members understand what the implications of might actually be. The other thing I'll share with you is that we try our best to have quite helpful show notes for this.

So we're going to mention various projects and papers along, and if you are viewing this in Spotify or YouTube podcasts or something, you might not see them. So be sure to look up podcast. greensoftware.foundation to see the full links and the transcript. Okay, you two folks sitting comfortably?

Íngrid Munné Collado: Yeah.

Olivier Corradi: We are, sure.

Chris Adams: Brilliant, okay, then I'll begin.

Okay, first question I'll put to you, Olivier, because I've actually been a fan of Electricity Maps for quite a while, and when I first heard about Electricity Maps back in 2017, it wasn't Electricity Maps, it was electricitymap.org. And this is something that is reserved for non profit organizations. So it's 2024 now.

And I now know that the same kind of cool map that I started talking about at conferences and so on, is now available under electricitymaps.com. And as someone working in a non profit, I end up talking and thinking about theories of change all the time. So there was a really interesting post I saw on the blog, on the Electricity Maps blog post, particularly talking about this theory of inaction.

And it's rare to see start ups talk about this kind of stuff with a fluency, really. So, Olivier, can you tell me a little bit about basically, what this is, and how this informs you spending all the time on Electricity Maps, because I know there's a few other things that you've worked on before.

Olivier Corradi: Yeah, I'm happy that you actually give me the opportunity to articulate this. So in the early days when Electricity Map was started in 2016, there was this question that remained for a couple of years afterwards, which was, "should we actually be an NGO or should it be a for profit company?" And honestly, the conclusion is that it depends on how you want to impact the world and it depends on how you want to be funded and so on.

But the way that we've been looking at things is basically to say when a company will pay you for something you're delivering, that represents a change that is happening in the world that is sufficiently valuable that someone will pay you to do it. And people are taking you seriously when they're implementing it.

So this was a little bit what tipped it over towards more of the, let's say, for profit world. But in order to make sure that we never lost track of the actual impact we want to have, we started having like this framework that we stole from someone else, honestly, and adapted afterwards. But first of all the vision we have here is to imagine a world where we have low carbon electricity that's delivered everywhere across the world, every hour of the year.

And this was really why Electricity Maps was created, out of almost a frustration of folks looking at the electricity grid from a yearly perspective, instead of looking at it hour by hour. And of course it made sense before we had renewables. Now that we, the wind doesn't always blow and the sun doesn't always shine, we need that.

And the angle we're taking here is data-driven decarbonization, as you mentioned, because we're a mixture of folks who come from academia, who know how to build machine learning systems and tech. So this is our angle. So with that vision in mind, providing clean electricity every hour of the day, we realized there's three pressure points that we need to apply if we want to create that transformation.

The first one is, we identify them as citizens. Second one, corporations. And the third one, institutions. And there is this framework that, that depicts a triangle of inaction where each of these corners are pointing at the other as an excuse to not change. For example, citizens are saying, "well I actually would love to be greener, but the government is just not putting the right incentives for me to be cleaner," like the public infrastructure is not helping. Another piece of it is the citizens are pointing at corporations and saying, "well, I, too bad, like, flying is actually cheaper than taking the train. Like, I don't have a clean offering here."

And then the corporations on the other side will say, "well, but everyone wants to fly, right? I mean, the citizens are not ready for that change." They'll say, "well, I'll just keep operating like I am." Corporations will point at institutions as well and say, "well, the right tax schemes are not here for us to make flying cheaper, actually, so we'll just continue what we're doing."

And then institutions will be, "we don't have" like the, let's say, "social acceptance from the citizens to actually, exactly, to put these green tariffs. And on the other hand we're just a small organization and institution, sorry," and you have corporations like Microsoft, OpenAI, Google, that are just like, more powerful than governments.

And so you can basically map these out in a small triangle. And we said, okay, so how are we going to impact this? And then most of our initiatives can actually be mapped to this triangle, where on the citizen side, the app that we have of Electricity Maps is creating this awareness, creating the debates as well to make sure we have a factual understanding and can challenge our politicians in the right way.

There's the piece on corporations where we are working with them in order to ensure that green offerings can actually hit the ground, like that we have electric vehicles that can use electricity at the optimal time, all these things. And it's a win because we get a financial cut of this and we can grow our company and grow the impact.

And then finally, on the institutional side of things, we're trying to make sure that the right carbon accounting methodologies and so on are being pushed. We basically want to live in a world where we have an accounting that represents what's physically happening in the world. And one example is we have a data portal where our historical data can be used by any company who wants to do granular carbon accounting.

Again, moving away from this world where you're doing it on a yearly basis to an hourly basis. So this is the framework we've put in place and the way we're articulating internally as well how we allocate resources and prioritize and make sure that what we do leads to impact ultimately.

Chris Adams: Cool. Thank you for that. I have one question following up from this, because when I was doing a bit of research, I looked up this triangle of inaction, and I think I might need one of your help, one of your help in pronouncing the person's name. Is Pierre, is it Peyretou?

Olivier Corradi: Not too bad. Yeah. Well done.

Chris Adams: Okay, so for people who are listening, what we'll do is we'll share a link to some of the theory behind it.

He's actually got an online course. I think there is, there's a French school of business which does actually have some online resources to understand this kind of theory. So if you're Curious and you want to think about, okay, how does this work? Or if you'd like to see an actual triangle rather than have us describe it, follow the link and you'll see some of it there.

Okay, thanks for that, Olivier. Okay, so we've spoken a little bit about data, and you mentioned about data being in the open, Olivier, and I can actually confirm that when we spoke to Toby before, Toby is one, sorry, Tony is one of the colleagues at Literacy Maps. We did an interview with him around about November last year, actually, and he was talking all about this, Open Data Portal.

And back then we were like, "oh, sweet, there's all this data being published." And I'll be honest, you're a startup. I was like, "are they really going to publish the next year?" And then January came around and it actually did get published. I was like, "wow, this almost never happens." So I was really pleased to actually see a startup and a company follow through with making some of this data available because it's so, so, so useful and this is so hard to find otherwise.

I can speak as someone in a non profit who's been trying to find this data. It's so, so handy to have some of that available now. So yeah, thank you on that one. Okay, so the question, I'll leave some space because I think there was a response coming up there actually, Olivier.

Olivier Corradi: Oh, thank you so much for the shout out. You know, it's important for us as well to feel like what we're doing leads to direct impact and that we have folks that are demanding this data. And sometimes these feedback loops are not always present. So I appreciate you giving us the shout out.

Chris Adams: Yeah, so if anyone is listening, this is open data, so it's something that you're able to build on and we'll share a link to the actual website that makes it very clear how you can use this information. So if you're trying to build something and you're looking at historical stuff, it's totally there.

Have at it. Okay, so we spoke a little bit about carbon intensity at different parts of the world and how you need to think about it on an hourly basis rather than just an annual basis. And we might talk about why when you talk about green energy saying green energy with certificates that came from solar and then saying, using that to make usage at night be counted as green might be conceptually a little bit challenging, we might say.

So that's some of the stuff we spoke about. Now, the thing I want to ask a little bit about, and Íngrid, if I can hand this over to you. When you're working as an engineer and you start thinking about carbon intensity. It's when you use, say, APIs, you just see a single number that goes up and down.

And like, it's fun. And it's very easy to underestimate just how much work can go into this and all the complexities around this. Because as I understand it, it's more than just like, looking at what the power will look at looking at generation from a single plant. Íngrid, can I just give you a chance to actually talk about what goes into sharing some of these current and historical figures? Because I know there's some forecasting work, and we'll talk about that later, but we've just spoken about open data and some of the historical stuff, and some of the context there might be useful for people who are considering downloading some of this or messing around with it themselves.

Íngrid Munné Collado: Yeah, absolutely. And I would say that without this amazing work of collecting data, any forecast, like forecast would not be possible at Electricity Maps because we need this data to be able to produce our forecast. So let's take a look at how we collect data. This is currently done by one of our teams at Electricity Maps, the Grid Modeling and Methodology team.

And our work starts by first trying to get as much real-time data and historical data from, that is available publicly. And here we're talking about governmental institutions, transmission system operators, that for people without electrical engineering background, those are the folks that manage the electricity grid at high voltage, and that make sure that demand and production is actually matches at every single second.

So we try to collect this data and now we have data for 228 zones. And when we think about it, we might say, okay, we just collect CSVs and everything is standardized and nice, but the data is really messy. So we might get data from like in PDF files, in TXT files. We have sometimes, I can tell you a bit of an experience.

When I joined Electricity Maps, I had to build the parsers for Japan. And I'm originally from Spain, so I know a lot about electrical system in, in Spain, but when I had to dig into the Japanese electrical system, I didn't know that they have eight different organizations that collect data and each and single one of them is in a different format.

And sometimes we even had to do some image recognition to get the power of the nuclear power plants that they have in Japan. So data is very messy. It contains outliers. We have missing values, wrong values. Some production modes might be missing as well, so there's a lot of work that we have to do behind the scenes to make sure that we collect this data every hour or even in lower resolutions to make sure that we can use this data and process it.

So once we have this data in the raw format, then we process it to make sure that we don't have these outliers or missing values. And that we have a complete power breakdown. That means that if Spain, for example, has nuclear, gas, coal, and like in total eight production modes, that the data we get has eight production modes because otherwise the carbon intensity values that we might show on the app or on the API, they are not going to make any sense.

So once we have this done, then we can actually go into maybe one of the core pipelines we have at Electricity Maps. That is the flow tracing pipeline. And Tony did an amazing job in the previous episode where he explained how this works. But for those who don't know, the flow tracing pipeline makes sure that we are able to trace back the origin of electricity.

Because if I plug my laptop here in Denmark, the electricity that I'm consuming is not only the one that is being produced in Denmark, but that one that is being produced in Denmark all the exchanges that happen between Denmark and the neighboring countries. So, we have this pipeline that makes sure that we can take into account all the neighboring countries and what happens at every single hour of the day to make sure that we can know the exact mix at a given hour of the day.

That's actually when we get the origin of electricity, the power breakdown, and then we can translate this number into carbon intensity by using emission factors that we update recurrently and that this actually helps us understand one, what's the carbon footprint of one kilowatt hour that we consume at a given country.

So emission factors are different based on the source we use and based on the country we are at. And that's what you see on the map.

Chris Adams: Wow, okay, so that's, let me just run through some of that then. Just to make sure I'm understanding it. So you're essentially getting a bunch of data. You're having to do, clean some of it up. Like, that's quite common. And most of us might be used to like, working with maybe text or CSVs. But I think you said that you're essentially like doing OCR, like optical character recognition in some on like, gIFs or pictures rather than actually having to read an actual number. Okay, and then once you've got that, you've got an idea of what the generation might be, but then you then need to do a bit of, like, working out where data is, where energy is being traded across borders, essentially, because in some ways the grid does have all this stuff, you just can't look at the production, for example. Because, like, I mean, the UK has, like, new connectors to other countries all the time, and Germany uses loads and loads of France's nuclear power, for example.

So there's all the stuff like that, and then once you've got this idea that, okay, there's probably this much coming from these places, you then need to think about, okay, well, what is the carbon intensity of power from a coal fired power station, or a new coal fired power station versus an old one, and stuff like that.

So there's all that other depth as well. Okay, and that all goes into a single number.

Íngrid Munné Collado: Exactly.

Chris Adams: Alright, I am kind of, so, I see why people do this now, because when I've looked at this before, I've looked at numbers saying, "oh yeah, it's just like generation," but no, there is, generation and production are two totally different things, and you do need to take into account some of this if you want a meaningful number.

Íngrid Munné Collado: And if I can say something just to give you a rough number, a country like France, it has interconnectors like with eight different countries. So imagine if you only consider the production or generation in France without considering all the interconnectors around, you might get a completely different picture.

Chris Adams: Ah, okay, alright, thanks for providing extra context because, yeah, I can see how complicated this gets very quickly in that case. Alright, okay, one thing that you mentioned before was that there are all these complicated scrapers, and when I looked at Electricity Maps a few years ago, I was surprised by there's quite a lot of it which is open source, so like, don't believe me?

Look at the repo, for example. There's some of this out there so you can see just how messy the data might actually be or possibly contribute if there's a gap, right?

Íngrid Munné Collado: Yeah, actually, for example, when I had to work in the Japanese parsers, I got help from people in Japan. That they actually would point me to the right CSV file. Oh, and at some point the CSV changed the URL where it was stored. And it was not until someone, a contributor, helped me out and said, "Hey, check this link instead."

So we have, so all our parsers are open source and they are hosted on GitHub, on the contrib repo. And we are extremely happy about all the contributors that help us out, like be able to get this 228 zones that we have now, on the map.

Chris Adams: Okay, go yeah, Olivier, I was going to ask if you anything you wanted to add, because I believe you did a bit work on some of the academic underpinnings for some of this.

Olivier Corradi: I just wanted to add as well that if we tie it back a little bit to the triangle of inaction, what is happening here and why this is so exciting is that we're basically enabling some of the citizens to also act on climate change by being able to contribute with their unique expertise, which helps us.

There's no way Electricity Maps with, we're a bit more than 20 people now based in Copenhagen. There's no way we would have been able to cover the world if not for the help of all these wonderful contributors that have helped us all along, and it gives them also a way to contribute something meaningful where maybe before they didn't have that opportunity.

Chris Adams: Cool, and Olivier, we spoke a little bit about flow tracing, and I understand this is the you published a paper about this years and years ago, and that was one of the things to basically, the methodology that you're talking about, yes, there's some proprietary code, but the general approach that's being taken, it's in the public for people to understand, so they can challenge it and interrogate it and say, well, yeah, this is, I disagree with this thing and this is why I think this might need to be changed in the future, right?

Olivier Corradi: Absolutely and our philosophy has always been if there's something where we think we can move faster with the help of others, then let's open it up because then we can allow for contributions and so on. If it's something where we know that if we open it up, it's going to take us a lot of efforts to handle the contributions, then we don't open it up, and that's why some of the internal pipelines that Íngrid described are proprietary, because it's just faster for us to change a couple of things and not worry about what will happen if we open it up. But in general, we always try to be open, because we are trying to create a global consensus on how we account for things, so that can only be achieved through openness.

Chris Adams: Okay, you're singing from the same hymn sheet as us, like, we, the Green Software Foundation has a big thing about Open, the software carbon intensity metric is Open. If you want to pay to download it, you can buy it from the ISO for 63 Swiss francs. I don't know why you would, but that's available for people, and me, working at the Green Web Foundation, we use open as a lever, so we publish almost everything we can, either under open source or under open licenses. So, Íngrid, if I may, can I just come back to you, because we spoke a little bit about historical stuff, and you mentioned that forecasting is now a big thing, because the thing about renewable energy that we kind of alluded to before is that it changes over time. The sun goes up, sometimes the wind blows, storms move around, stuff like that.

So, can you just tell me a little bit about why this idea of forecasting is maybe more of a focus for you folks now and maybe explain a little bit about how, I guess, the sausage gets made and how that can be difficult.

Íngrid Munné Collado: Yeah, of course. So, as Olivier mentioned in the beginning, we want our data to be as actionable as possible. And now imagine that we are software developers and we run cloud jobs. And at the same time, we want to be aware of our carbon footprint. And we know that our cloud jobs might last five, six hours.

So, real-time data and historical data does not really help us achieve, like, be aware of what's the carbon footprint of our cloud job in the future, like, when is the right time to schedule this? And EV chargers might also think the same, like, when is the right time to charge? So, we saw that real-time and historical data would not help us achieve that.

Then we realized, okay, then we need to provide them with forecasts, and we know that we are providing a global API with data from all over the world for all bidding zones. Then, if we, let's go back to this software developer who wants to schedule a cloud job, because I think that's going to help the audience understand.

I can choose a data center in Sweden, but I can also choose a data center in Texas. So we can, we have the power of choosing which data center we want to run our cloud job. But at the same time, how do I know which one is the best? I don't have any idea. And at the same time, okay, but is it better to run my cloud job at 9 in the morning or 9 in the evening?

And then that's when we realized, okay, forecasts can actually solve that, but we need to provide it globally for all the zones in the world. So that's why we decided to focus on forecasting carbon intensity for the next 24 hours. That means, like, day-ahead. So we run the pipeline every hour and we provide forecast for the next 24 hours.

But then the next challenge came up. When we realized, "wait a second, is carbon intensity a metric that everybody can understand?" If I tell you that your cloud job used, I don't know, 200 grams of CO2, you don't, it's very difficult to relate. But if I tell you that if you schedule your cloud job at 9 am,

you're gonna run on 90 percent of renewable energy share. Instead of running 9 pm and then the renewable energy share is going to be 10%, it's very easy to understand and say, "oh, you know what, I'm going to do it at 9 in the morning because the renewable energy share is 90%." That's when we realized, okay, we can't just focus on carbon intensity forecasts, we need to do that with renewable energy forecasts and more specifically, wind and solar power forecasts.

Because that increases user engagement and the actionability of our forecasts.

Chris Adams: Ah, okay, that's quite a subtle change then, so, and I understand that because intuitively it's something that I just become so, kind of, you take it for granted, right? If someone is coming to this new, yes, explaining carbon intensity is conceptually quite complicated, but how much is running on clean energy is quite a bit easier to understand very quickly.

Íngrid Munné Collado: Exactly. if I can add on that, then when we said, let's start on renewable energy forecasts, it's a problem that has been not solved maybe because research evolves very fast, but it's a problem that has been laying around for years. I've worked on that field for many years. Because energy is traded on this day-ahead market, so people need this forecast to be able to trade energy in day-ahead markets or intra-day markets.

So when I started working at Electricity Maps on that field, I came from my previous job, and I came in, completely biased and thinking that this challenge would be easy to solve, because in my previous job, we had to do renewable energy forecast of specific assets. And then I would have the perfect setup for a machine learning engineer.

That means I knew the location of the wind turbines. I knew all the data regarding the wind turbines, like the blades, the installed capacity, if it was under maintenance or not. I also had access to multiple weather forecast data and I only had to build models for Denmark or the UK or Netherlands, so it was very focused, but when I joined Electricity Maps, the problem is completely different.

Here we focus on building a renewable energy forecast at country level or, bidding zone level without knowing the location of the assets, without knowing what's installed capacity, if the turbines are under maintenance or not, and that makes the problem very difficult because we know that renewable energy forecast is very linked to weather.

And if you don't know the exact location, then that's another challenge. So, I think in the grid forecasting, we did a really good job on finding a solution that generalizes well enough, and that allows us to generate this forecast for wind and solar for all zones in the world.

Chris Adams: Ah, okay, thanks for providing that extra context. So, I'm just going to check if I understand some of the terminology you used there. You said like a day-ahead market and an intraday market. So, so basically, as I understand it, if you maybe run a wind farm, for example, the day-ahead, you're going to say, "well, I reckon we can sell this many kilowatt hours or megawatt hours of power tomorrow," and that's what you'll make a bid in, and that's, there's consequences for either underbidding or overbidding for that kind of number, so that's why you'd care.

And the intraday is a bit like the kind of shorter term thing, so, you might make one big bit but then you might say there's a little bit of flexibility or you say well okay I, okay cool. And you mentioned this term bidding zone. Now bidding zone is a little bit like a country but it's not always a country.

So like America has all these different bidding zones because it has different grids and there's, that's the kind of, when you folks have mentioned the word zone that's kind of what you're referring to. It's not quite a country but it's more related to like, is there a kind of a unit of carbon intensity for a particular grid region, right?

Íngrid Munné Collado: Exactly. I mean, if we focus in Spain, for example, Spain, Portugal, France, Germany, a zone is the same as a country. But if we look at Sweden, it's split into four zones, into four bidding zones, and Norway into three. So if you check out our data, then you can compare between zone and country.

Chris Adams: Okay, and within a given country you can have radically different carbon intensity, and we might talk a little bit about some of that a little bit later then. Okay, cool.

Olivier Corradi: The dream is to get to street level granularity. And this is why flow tracing, by the way, is so important, because as you increase the resolution of what you're looking at, then obviously, if I'm looking at just a city, most of the electricity is actually produced outside of the city. And that's also why, by the way, the whole forecasting challenge just becomes massive.

Try to forecast how clean the electricity is going to be at every location on the planet. Obviously, you need really good renewable forecasts as well, and that's what civilization needs if it wants to rely significantly on these renewables that are intermittent.

Chris Adams: Wow, okay, I didn't realize street level was the dream for this, blimey. Okay, so we spoke a little bit about software and cloud jobs and stuff like that, but it's also worth just briefly touching, like, this is used outside of the cloud world. And I think one of you mentioned EVs as one of the examples here.

Could we just briefly touch on that? Because that might be one of the things which is concrete that lots of people might experience or might know someone who might have something which is, like, impacting them. Because yeah, EVs are becoming more popular now and it's probably one of the biggest new large uses of electricity in most people, in houses for example.

So yeah, Íngrid, maybe you could talk a little bit about that and then we can move on to some of the other questions.

Íngrid Munné Collado: Yeah, of course. So one of our clients is Monta. And we have a very great success story with them. Monta, for those who don't know them, is a global operating platform for EV. And one of the solutions is the smart charging feature, where they offer users to shift their charging according to the carbon intensity of the grid, the share of renewables, and that's by using our forecasts, or the price of the electricity grid.

And users are completely empowered to choose and to prioritize which signal they want to follow, if it's mostly price or low carbon or high share of renewables, and by doing that, those users took action in decarbonizing the grid. And the results are quite impressive because 70, 000 charging sessions were optimized for low carbon or high renewable share, and there was a 48 percent growth in user engagement, according to Monta, and that means that they optimized for low carbon charging, and in this process, 200 grams of CO2 were avoided for each charge on average.

So these are the numbers and this is the feedback that we're always willing to get because when we create, when we produce this forecast and we spend so much time building these forecast models is because we really want to know that people use our data and that they really use our data for this use cases.

So we were really happy to know that.

Chris Adams: Ah, thanks. Okay, so you mentioned one thing that I think was quite interesting. You mentioned, like, the cost being a thing that might change at different times of day and, broadly speaking, this is because, as I understand it, when energy is really when there's lots of green energy, renewable energy on the grid, it will be relatively cheap and somewhat green.

So, in the UK, for example, we have something like this Octopus Energy. I'm not in the UK, I'm in Germany, but I grew up in the UK. So, Octopus Energy is one company that's been doing a bunch of stuff like this about having agile and intelligent tariffs. So there's essentially a financial reason as well as a kind of basically an environmental reason for doing some of this.

And I think what I've heard in the UK, for example, I believe on an interview recently I heard just by doing some of this, essentially, when controlling some EVs, for example, I think the figure was something like 1.2 gigawatts of demand they had control over. Now that's basically the size of a nuclear power station in many cases, so that's like a significant amount of flexibility on the grid that would otherwise have to come from burning loads of fossil fuels.

Right? Okay, so maybe I can allow us to talk a little bit more about the cost thing because I know that when we talk about this, when we talk about green software, you can make an argument that yeah, you should do it because it's good for the planet, but there's also a real cost fact which comes into this that I think is actually growing and maybe this is something that I understand that you folks have been looking into as well as one of the ways to address more this triangle of inaction and align some of the incentives for more kind of grid complementary activity perhaps.

Íngrid Munné Collado: Yeah. So one of the issues we face is that we know that we want, we are implementing climate action and we want people to use our data for, in order to decarbonize the grid. But sometimes the entry barrier can be quite high when we just go there with, "hey, you have to save CO2." But sometimes it might be easier for users to understand that, "hey, if you look at the electricity prices, that you have also seen that they are not constant, you might be able to save money and CO2."

And that's very, that's a very good entry point as we mentioned in the Triangle of Inaction to increase user awareness because the main, for people it's very easy to understand price and how much they are going to pay at the end of the month and if they are going to save money and therefore it's also nice for us to get to them by I'm Price and then explain that, "hey, by doing that, you might also have an impact on CO2," but we have also a blog post where we mentioned that just optimizing for price doesn't mean that you're also saving CO2.

It's you need to implement some what we call co-optimization, where you want to either prioritize one or the other. And at the same time, maybe you might be able to save more CO2 if you optimize for higher renewable share, and at the same time, you can also save money by doing that. So that's why we think that price is a problem worth solving and that we also need to explore that area.

Chris Adams: Ah, I see. And if I understand it, and if we follow the kind of path from before, we spoke about, like, a lot of us might be used to just paying a single price for power, but the price might change depending on where you are geographically as well, and that has some implications too, like, this is one thing that I guess, this is probably the newer world we're moving into, in that, yes, there might be, like, night time tariffs, but it feels like there's a lot more dynamism, both temporally and spatially for some of this.

Íngrid Munné Collado: Absolutely. And we saw that when the war in Ukraine started, that as soon as we saw that there were less gas resources, prices skyrocketed. And for example, in Spain, where there was a price cap

The situation was solved also because Spain has other sources of getting natural gas. But countries like Denmark, Germany, they really struggled with that. And the prices really doubled for some months. And it's not only political conflicts. It's also availability of the power plants. I it was two weeks ago where Finland had some unexpected maintenance in one of the, of their, of the largest nuclear power plants.

And that cost their head prices to double. And also in Texas, we have seen extreme weather events that causes prices to change from

Chris Adams: More than double,

Íngrid Munné Collado: yeah, more than double from negative prices at some point because there, there's one area in Texas that has a lot of renewables and another area in Texas that doesn't rely on renewables and due to those extreme weather events, they have to turn off some of the power plants and they, then this affects the prices.

And something I want to mention, I'm an electrical engineering at heart. So I want to talk about the power of interconnectors, and we might think that just by increasing renewables, this is going to make prices to be cheaper, but the problem is not going to be solved, and it's not going to make prices cheaper if we don't have nice interconnectors that make these flows between countries possible.

That's why prices are just changing so much because there's so much happening and going on right now, both on installing new energy sources, weather events that we're seeing, unfortunately, due to climate change and also the lack of interconnections at the moment.

Chris Adams: Okay, so you mentioned two things that I think are interesting there. So one thing was this idea of, okay, the price can change massively. Like the Texas example, I think if we look at, like, the cost of power, right, it might be between 20 to maybe 60 US dollars per megawatt hour in Texas. For example, you're one, you mentioned, like, there's a bunch of wind in the panhandle in the kind of northwest, and then there's around Houston, there's loads and loads of demand where people use all the power, right?

And I think the figures I've heard were something in the region of negative two and a half thousand dollars at one point, and then 30 minutes drive away, the cost is three and a half thousand positive. So like, you got a almost four, five, thousand dollar swing in the pricing here. And one of the reasons is just because it's a bit like network connectivity.

You know, the pipe isn't big enough. And this is one thing that we have to kind of work around, essentially, thinking about this.

Íngrid Munné Collado: And the same issue is happening in Germany, and like, Germany doesn't have these price mechanisms that we have in the US, but in Germany, there are also, like, huge problems due to transmission capacity, because the, most of the solar power production is, takes place in one part of the country, while the demand is concentrated in the completely other opposite.

And they are now experiencing with curtailments and extreme negative prices because they don't have this transmission capacity. They also don't have storage capacity, and there are no market mechanisms that control how we increase this solar power production in the grid and how we just export this to other areas.

Chris Adams: Ah, okay. All right, thanks for the extra context. For people who are still with us, we'll share a link to the previous episode, because we spoke with chap Philipp Wiesner, who was building a bunch of this work to simulate these kind of grids to give an idea of what the pricing might actually be with different services.

And if you have data centers, how adding some storage might actually change the cost and the carbon of running various software services. So we'll add a link for that. Olivier, while you're here, I want to ask one question, if I may, about basically, optimising for carbon intensity, because that's what lots of engineers are kind of trained to do somewhat.

And I, inside the Green Software Foundation, there is a kind of standard called Software Carbon Intensity. It's the thing that you're supposed to, or that you might optimise for, essentially. And it basically lets you use two different ways of thinking about the carbon intensity of electricity.

And so one of these is called a average carbon intensity and another one is called marginal intensity and they are slightly different. And it can be quite counterintuitive when you're first coming to this because it can give you somewhat different answers or incentivize different kinds of actions.

For someone who's coming to this for the first time, can you provide a little bit of like background on how to navigate some of this and how to think about some of this? Because it's something that. I think a lot of people come to and they scratch their head quite a lot because it can be a bit confusing having two numbers which can suggest you do totally different things sometimes.

Olivier Corradi: Yeah, well, we might get into some of the weeds, so I'll try to keep the, sort of, the discussion a little bit high level so we don't get too technical. But I think the way that I'm trying to explain this, so, on the average side of things, the first thing that I will say, actually, is that the word average can be a little bit misleading, because it sounds like, like you're taking an average over a period of time, which you're not.

Actually, what we're doing here is computing what we call the flow trace signal. So it's like taking the production locally, looking at what's imported, and then concluding on what is the constitution of the electricity that I'm getting at. So it's a representation of all the power plants on the system, basically.

So that's what we'll refer to as average. And on the marginal side of things, the story goes that if you're plugging an electric vehicle to charge at a particular time, then it's not all the power plants in that system that will ramp up to give you that additional electricity. It's the one that's called the marginal.

And a loose way to define it is to say it's the cheapest power plant that still has capacity to ramp up, to produce more. So, in theory, that marginal concept makes a lot of sense, because we're saying that seems to reflect what is physically happening on the system. If you actually go a little bit more into the details, and that's where the differences start to pop up quite starkly.

Well, if you are plugging, for example, your iPhone on this, on, to charge right now. Then it's not this marginal power plant that will ramp up. In all likelihood, nothing will happen on the grid. It's just the frequency of the system that will change a little bit. But you're not shoveling a little bit more coal in the coal power plant to burn off a little bit more, right?

So, emission factor would be zero. Like, no impact on the grid, you could say. If you go the other way, and you say, now I plug, imagine a metaphorical plug on a data center that's using like 90 percent of the consumption in the grid, Then you can't have only that small power plant that has spare capacity, it's just not going to be enough to ramp up this data center.

You would need all the power plants on the grid to ramp up in this what if scenario where the data center didn't exist. And then the last example is, if you have an electric vehicle and you're plugging it in, then the electricity will be delivered instantaneously to you. And that's just not a change that a coal power plant or a gas turbine can react on.

And that starts to create some additional complexity when you think about, okay, but can this be predicted? Can the plugging in of my electric vehicle, could that have been predicted by the market? And if it's already predicted, then it's part of the business as usual scenario. It's not marginal anymore.

So when you actually try to go down to all the details, it becomes hyper complicated. And we've tried really hard to talk to all the power system engineers, the electrical engineers and the TSOs in Europe as well. And one of the most fruitful discussions we had was with 50 Hertz that concluded that they don't think you can actually reliably identify or verify what the marginal power plant is because depending on what market you're looking at, if it's day-ahead, intraday, real-time, depending on the magnitude of the changes, depending on all the interconnectivity, the marginal power plant is just a concept that philosophically makes sense, but from a data perspective, it's just hard to measure, and that's also, I believe, why most of the regulations recently on the hydrogen regulation in both the US and in Europe that is documenting what is it, what signal should you be using, whatwhen you want to prove that your hydrogen is clean, then they are settling on an average signal. So if you take a step back from all of this, you're having an argument that philosophically for me makes sense. Of course, you want to make sure that the short term impact of what you're doing is minimized.

When you start looking at the data, you have a signal that is difficult to audit. And we've been working on this for eight years now, six years, sorry, creating a marginal signal and trying to verify it. And I've just seen enough that it can be manipulated in many ways. And that's why it's a little bit difficult.

So to get back and conclude a little bit and to answer your first question, which is how would we navigate this thing? I think the advice we're generally giving is get both signals, plot the data next to each other, and depend on the use cases, ask yourself "Is user acceptance important?" Are you going to show this in an interface?

Because if you are going to show it in the dashboard of an electric vehicle, in a country like France, which is majority nuclear, or in Ontario, lots of decarbonized electricity already, it's going to be a tough sell to tell them, look, your EV is being charged on gas, which is on the margin. So this user acceptance is important.

The second piece is auditability. Are you thinking of being generally directionally correct? Or do you need an auditor to prove that the data is correct from a scope 2, scope 3 perspective? And these are typically things they're going to inform depending on if you're willing to have something that is philosophically more accurate, but in practice more wobbly or if you want to have something that's just simple to explain and sort of abides by the regulation.

Chris Adams: Ah, okay, all right, so if I try to kind of summarize it, it sounds like the marginal thing it's conceptually attractive and kind of fun and it might give me some and in very ways it's basically gives me this idea that I can make relatively small changes rather than some of the systemic changes that might be needed for some of this.

But from an audit, from an auditing point of view, because you're comparing some of this to essentially a counterfactual which might not exist, it's actually very difficult to say, "well, yeah, I definitely made this impact and if it weren't for me doing this these people wouldn't have switched this stuff on," for example.

So you kind of need to have a degree of kind of clairvoyant level of information for this to actually really check this. Okay, cool. Speaking of clairvoyant, I've just realized the reason I've used this term is it makes me think of, there is a paper by a research student, I think her name, Tammy, I'm so sorry if I pronounce her name incorrectly, Thanathorn Sukprasert, she wrote all about using the SCI and using these different signals and how they, when they agree and when they do not agree, and if you are curious about this as someone listening to this, we'll share a link to that paper because it's a really good paper, she's also doing another one which is all about, okay, what are the possible savings from carbon-aware computing?

And that is also a really fun paper to read, and maybe, Tammy, if you're free, we'd love to have you on the podcast to talk about some of that, because there's quite a lot of fun to read there. Okay, cool. Thank you for that, Olivier. I realize that was quite detailed, but it, I appreciate you talking about the fact that in other sectors, there's stuff we can look at, because, as I understand it, this whole shift towards clean or green hydrogen is somewhat comparable to data centers in the fact that you have a very concentrated amount of energy being drawn in one place. So, in the same way a data center might be tens or hundreds of megawatts, you might see something similar with, like, creating hydrogen.

Is that the idea behind some of that?

Olivier Corradi: Absolutely, and if if the way that I'm thinking about this if you really want to simplify it, if we take a step back, the opportunity we have ahead of us is large, abundant renewables that are going to be the cheapest and the fastest way for us to expand the system. I mean, nuclear is great as well, and hydro and so on, but it's just, it takes more time, and it's a little bit more expensive when you put it in directly.

And so, if we put all this renewable in the system, we better well make sure that every flexible appliance out there is aligning their consumption to the time at which the renewables are creating that electricity, because else we're just hindering their deployment. You know, it's less batteries that we need.

And I think that's generally also the thinking behind the hydrogen. We want to make sure we're electrolyzing the hydrogen at the times where all these abundant renewables are producing electricity. And so that's the simplest heuristic and the fastest way for me to explain the systemic change we're undergoing and also why we're focusing on these renewable power forecasts.

There's a lot of value there. And by the way, why they also typically align with the price of electricity.

Chris Adams: Ah, okay, thank you. I do hope we still have some people with us, because that was a bit of a deep dive. But as sometimes when we're coming to new fields, working in software engineering, you do need to kind of engage with the details sometimes. Folks, I think we're coming up to the time that we had allocated for this.

And this has been loads and loads of fun. I've really enjoyed this. If people do want to know more about what either of you are doing, can I just give you a bit of space to talk about, like, Follow me on either Twitter, LinkedIn, Mastodon, whatever, like yourselves personally. And then if there's anything you would direct people's attention to, yeah please do, and then I think we'll wrap up actually, so maybe Íngrid if I hand over to you and then you Olivier, we'll wrap up with you okay?

Íngrid Munné Collado: Absolutely, so if people want to know what I do, they can check my LinkedIn, and it's Íngrid Munne, M U N N E, and I'm also on X or Twitter with the same name. So looking forward to that.

Chris Adams: Thank you, Íngrid. And Olivier, for you.

Olivier Corradi: Likewise, like Íngrid, you can reach me on LinkedIn and Twitter. If you search for my name, you'll find me. And I think in terms of resources to watch, we try to publish blog posts that are going deep into the topic, that are thoughtful. We don't publish a lot, but when we do, it's like We, we at least try to have serious research there, so check out our website, check out our blog, and we have a couple of guides on carbon accounting as well if you want to go deeper on the topic, and a few videos on YouTube if you want to nerd out more on some of the things we just discussed.

Chris Adams: Okay, we'll add a few links for all of those and the thing I'll also share is that in the Green Software Foundation, there are ongoing discussions in the standards working groups to discuss all this stuff. So you can see all the kind of back and forth around this, so if you do want to engage with this and possibly join to actually take part in that conversation, there is that available.

So you can see what discussions have come before and how people arrive at deciding which carbon signal to be following. Alright, I think that takes us to our time. Folks, I really enjoyed this trip, and we'll make sure that everything, or as many things as we remember, are in the show notes for people who want to continue this quest to learn more about building more sustainable software. Thanks a lot, folks, and have a lovely time in Denmark. Ta ra!

Íngrid Munné Collado: Thank you. Bye!

Olivier Corradi: Bye-bye.

Chris Adams: Hey everyone, thanks for listening! Just a reminder to follow Environment Variables on Apple Podcasts, Spotify, Google Podcasts, or wherever you get your podcasts. And please, do leave a rating and review if you like what we're doing. It helps other people discover the show, and of course, we'd love to have more listeners.

To find out more about the Green Software Foundation, please visit greensoftware.foundation. That's greensoftware.foundation in any browser. Thanks again, and see you in the next episode!

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