The energy sector is undergoing a data revolution.

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Open access data, smart meters, IoT…  unprecedented volumes of data are now available. But is the energy sector keeping up with the pace of change? Not everywhere, but it’s getting there.
To make the most of the drive towards green, we will need to adopt data-driven approaches to ensure a clean & cost-effective Energy Transition, and avoid greenwash.

To demonstrate why - let's take a look at two case studies at two ends of the energy data value chain: balancing generation in the grid & carbon accounting consumption.
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Generation: The cost savings of high resolution PV forecasting

The University of Sheffield's use of granular data for better forecasting of photovoltaic generation in the UK is a resounding success story of the carbon and cost savings of high resolution data.

An increasingly renewable grid is an increasingly volatile grid. Dirty coal power stations were reliable. They turned on when we told them to. Wind, solar and tide are far more fickle; making generation more difficult to predict and the grid harder to balance. The cost of balancing supply and demand in the UK grid can be as high as £140 million - paid to generators to turn up or down as needed.

Sheffield University’S PV research group, Sheffield Solar, have saved the National Grid millions of pounds by improving forecasting accuracy by 10-20%, reducing the premium associated with responding to demand and supply events as they happen in real-time. Their PV forecasts cover over 1 million distribution and transmission connected PV assets.

We recently mapped Sheffield Solar’s vast dataset onto our network map:

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Consumption: Low-resolution Carbon Accounting is setting back emissions targets

The time and effort of processing this kind of big data is huge. Trust us - we know. But it is worth it. Let’s take a look at the climate-cost of low-resolution carbon accounting to illustrate why: Industrial and commercial energy consumers underreport their emissions associated with electricity consumption by an average of 30%.

Our research has shown that industrial and commercial energy consumers underreport their emissions associated with electricity consumption by an average of 30%. Just take a moment to think about that number. It’s a lot - especially when you consider that 25% of all global emissions come from electricity consumption (⅔ from I&C consumers).

If 'you cannot reduce what you cannot measure' then inaccuracy is a serious problem.

Why is inaccuracy so high? To understand, we need to look at how most corporations account for the carbon emissions associated with their electricity consumption (Scope 2 emissions under the GHG Protocol). Corporations report two calculations for Scope 2: market-based and location-based. The market-based calculation has already been the subject of some criticism (e.g. see Matthew Brander on Creative Accounting  or our last blog on the issue of untraced renewable energy certificates).

For now, let's focus on the accidental greenwash that occurs when companies use low-resolution emission factors to calculate their location-based number. This is how most companies calculate their annual emissions:

‍Annual emissions = Annual emissions factor x Annual electricity consumption

In 2020, the UK’s annual average emission factor was a record low of 181gCO2 per KWh. However, depending on the time and location, the actual emission factor varies considerably. Here’s a more accurate picture, reflecting the average emission factor of every half hour period across 14 geographic regions:

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Under the GHG Protocol companies can choose to use high resolution emission factors, calculated by carbon data specialists like Advanced Infrastructure, which give a more accurate picture of emissions as they are based on the location and times of consumption.

It is always perfectly legitimate under the GreenHouse Gas Protocol and the UK’s Streamlined Energy and Carbon Reporting (SECR), to use lower-resolution data but the risk and costs of accidental greenwashing are increasing.

Researchers at Stanford demonstrated the increased inaccuracy of using a single average emissions factor in a grid with more renewable generation: In a grid with 25% solar penetration, a large consumer could miscalculate their emission savings from a solar PPA by as much as 50% when they fail to take into account the time and location of their consumption.

Climate-related litigation and greenwash accusations are increasing. There are now over 2,086 climate laws and policies around the world and 412 climate litigation cases globally as of December 2020. Check out some of the articles linked at the bottom for some recent criticisms.

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The move to greater transparency and higher resolution data is speeding up

There are early signs of a change on the horizon.

EnergyTag  was launched only this year as an independent initiative campaigning for hourly energy certificates which will make it easier for I&C consumers to match their energy consumption profiles with the renewable generation they buy.

Companies like Google are successfully demonstrating the emission savings of their ambition to be powered by carbon-free electricity 24/7. To do this they buy power, develop on-site renewable projects and load shift their non-urgent compute to maximise their use of green energy and avoid increasing demand on dirty fossil fuel generators:

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Big data will be needed to target investments and ensure positive impact

Not wanting to be caught out by changing regulations and growing ESG scrutiny by climate-conscious investors, more companies are starting to take action on their emissions.  EnergyTag is part of a growing number of initiatives aimed at increasingly the impact of corporate targets: Science-Based Targets, Climate Group’s RE100 initiative, the UN’s Climate Neutral Now - the list goes on.

To make the most of the drive towards green, companies will need to adopt data-driven approaches to ensure they avoid greenwash and invest in truly sustainable business practices. The potential for impact is huge. That’s why at Advanced Infrastructure we make big data and carbon our focus.

Simply put, better and cleaner energy decisions are made with better and cleaner data analysis. That’s why we’ve built an energy big data platform capable of ingesting, processing and analysing the vast quantities of data concerned when you start getting into the detail of a low-carbon future.

Recent articles on emissions disclosure & greenwashing:

White & Case, Climate change disputes: Sustainability demands fuelling legal risk

Matthew Brander, Creative accounting: A critical perspective on the market-based method for reporting purchased electricity (scope 2) emissions

Simon Lewis in The Guardian, The climate crisis can't be solved by carbon accounting tricks Katja Sodomann, Tagesschau, Deutsche Bahn: How ‘green is the railway really? (In German)

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