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An Interview with Christopher Jackson

from Advanced Infrastructure

Technology Limited

November 21, 2022

Christopher Jackson is the Co-founder of Advanced Infrastructure Technology Ltd. The company uses novel big data and digital twinning techniques to provide granular insights which support the Energy Transition. The company’s current projects range from real-time tracing of emissions associated with the electricity consumption of multinational data centres, optimising the siting of low carbon technologies like electric vehicle charge points, to smart local energy systems modelling at scale.

 

Can you give us an overview of the High Resolution Photovoltaic Forecasting Toolbox

you've been testing with PLATOON, and how the PLATOON funding has benefitted its development?

 

So the Toolbox use is a novel approach to forecasting photovoltaic. Generally, the state of the art is to use satellite imagery and essentially cloud cover as a proxy for the installation of any particular site location on which a PV station is sited. So essentially, you're trying to know how much sunlight is landing per square metre, and you're using satellites to look at the formation of the cloud and guess where this cloud is going to be in the future. So the limitation of that is really the limitation of our ability to forecast where clouds form. And it's not very good actually, because this is very difficult to predict.

 

So the current approach is to, say, for the large solar farms, I'm simply going to put a light meter and odometer on my utility-scale PV farm, and I'm going to measure it, and I'm actually going to use that to figure out my immediate PV output. And that's fine for the utility-scale plants, but it's not possible for the rooftop systems. It's not practical to put a light meter on top of your house for a 2-3 kW PV system. It is okay for 70 megawatt systems. And the problem is that there are a lot of small sorts of photovoltaic systems. I mean, particularly in the UK, there are about a million, and therefore throughout Europe, there's of the order of 10 million. And they are all different orientations and all different configurations, and they do actually affect the ability of the energy operators, the national grids, to balance the supply and demand because they're lighting up and turning down in a completely unpredictable manner.

 

So the problem is not so much with the utility-scale; it's with the residential rooftop systems causing sort of gigawatt size fluctuations in the electricity generation mix and portfolio. So we can't install a light meter on top of everyone's house, but a PV system is of itself a light meter of sorts. It generates more power the more sunlight falls on it. So it's effectively an odometer in and of itself. So what our system does is it samples a small percentage of the PV systems scattered all over the country and uses their output to forecast the light falling in that area and then uses that to forecast the power output of all the PV systems in that sort of square kilometre. And that's the clever approach that we take. Most PV systems, residential ones, are not monitored, but that's about sort of 5% or 6% of them, and that's sufficient to infer the out-turn of the remainder.

 

Regarding how the PLATOON funding has benefited its development, it's funded the scaling of that solution from just Great Britain to essentially the rest of Europe. And it's allowed us to purchase access to the PV output database, which has another few hundred PV systems scattered around Europe, which we can use as measuring devices for sunlight effectively and insulation. And then, we can infer the output of the surrounding PV systems. So it's paid for that extension of the platform European-wide to the EU 27.

 

Part of your award from PLATOON was an invitation to join the Technology Transfer Programme. How do you feel this has benefitted your company?

 

We worked with the institute Pupin in Serbia, and they were very helpful because the Balkans are particularly under a poorly mapped area of Europe. So this is a real test of how we could apply our approach in areas of scarce data. So they were very helpful in signposting us to the data sources and to local information around PV systems and databases. They were also very flexible because we originally proposed that the scope of the project was just to look at Serbia, and we made the point that actually it's more sensible to do this at a European scale because the data sources are common across Europe. And also, the transmission grid is highly integrated in the Balkan region. So it makes sense to treat it as a larger entity from a transmission grid point of view.

 

What do you believe the most important impact of PLATOON will be?

 

Now we have a pan-European model, I think the most important impact will be getting this used by various stakeholders. In our case, having our data shared and published by multiple stakeholders, including, let's say, Solar Power Europe: these are the industry bodies such that they know that this method of forecasting is used. Also, getting this used by the National Grid operators will be a really important outcome.

 

What advice would you give to other companies trying to develop disruptive technologies?

 

I think it's really important to tighten the scope of what you're doing and really identify what's valuable. So it's important to decide what you are not doing just as much as it's important to decide what you are doing. And if you don't know exactly what you are doing: first try articulating what you don't do, and it may help you retain more focus.

 

Your company's stated mission is 'to create a greener and fairer Energy System by making big data, transparency, and traceability accessible to all users of the energy system.' Can you give us some examples of how your data has been used to achieve this?

 

Yes. So we use the location of renewable generation as one of the major data components for calculating the carbon intensity of power. And currently, this is done at a national level. But if you know the actual locations of generation, then you can map this to ideally postcode level. That is the ultimate goal we have. And as we move towards carbon taxation and better carbon accounting, subnational data is becoming more important.

 

Your company has produced the first interactive live map of the UK's entire electricity network, based on 'hyperlocal' data and real grid physics. What made you decide to offer this resource for free?

 

It's really important to build confidence and credibility in the outputs of digital twins and data analytics to be open around the methodologies. So we, where possible, publish open source as much as we can.