Deep-dive on grid signals: How to source them? What to consider?

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Compliance with regulation & Auditability

Two other important considerations when sourcing grid signals are compliance with regulation, as well as the verifiability and auditability of data. Remaining compliant with leading standards such as the Greenhouse Gas Protocol, SBTi, and CSRD is crucial so your company and your customers can leverage this data in their emissions reporting. 

To be compliant with current regulations, make sure to use appropriate data depending on the approach taken. As described by the GHG Protocol, a location-based method reflects the average emissions intensity of grids where consumption occurs, while a market-based method includes contractual instruments. When taking a location-based approach, you should use average carbon intensity data. When taking a market-based approach, you should use residual mix data or accepted fallback values, which depend on regulations but often include average carbon intensity data and fossil-fuel-only carbon intensity data. 

Hourly flow-traced carbon intensity data is compliant with today’s leading regulations as well as upcoming changes that will require higher accuracy and granularity. You can learn more about possible upcoming changes to the GHG Scope 2 Guidance in their recent blog post.

Familiar with marginal carbon intensity and wondering if you could use them in your solutions? Marginal carbon intensity estimates the short-term change in emissions induced by a change in consumption. This signal can unfortunately not be measured and thus cannot be validated against a ground truth. It led to major standards and regulations prohibiting their use, and we advise against using these factors. 

Where to source the electricity grid signals

Data on the electricity generation mix, load, exchanges, and sometimes even prices is generally available from grid operators or government agencies worldwide. In Europe, the network of grid operators (ENTSO-E) centralizes data in its transparency platform, and the US Energy Information Administration provides data for all US grids. 

Some data sources, like the IEA and Ember, provide data on a global scale. However, this data lacks granularity as it is only available on a yearly or monthly resolution (besides some regions and signals where the IEA provides hourly data).

Even though this data is freely available, it is scattered across multiple data providers, and gathering it on a global scale requires ingesting data from 100+ different providers. Beyond the large number of data sources, multiple challenges exist. 

Data ingestion

Data comes with different data formats and schemas, time frequencies, and delays between providers. It also comes through different tools: APIs, PDF files, CSV files, Images, online dashboards,... Data delays mean data must be estimated in most places to be available in real-time. Data can also be later updated, up to several months after the initial publication. This varies from one data source to another, and in such cases, data must be refetched to ensure the most up-to-date data is used.

Data availability

Data availability significantly changes from one grid to another. While some grid operators provide up to 5-minute data for electricity generation and exchanges, others only report yearly or monthly numbers. Beyond granularity, not all electricity data is available from each source. In some grids, multiple data sources must be combined to gather electricity generation, load, electricity exchanges, and electricity prices data, each often available with different granularities.

Data quality

This data is available openly and for free, but also without quality guarantees. Integrating with these data sources means dealing with data outages, data gaps, and data errors, such as outliers and inconsistent data. As an example, this research paper from 2018 listed shortcomings of the ENTSO-E Transparency Platform. Ingesting data from these data sources requires having the necessary quality checks in place, as well as the ability to replace missing data with estimations to guarantee completeness.

Additional transformations

One of the key considerations when selecting grid signals is tracing back flows across interconnected grids. This is usually not provided by the data sources themselves and must be implemented after data ingestion and quality validation. It requires gathering not only electricity generation data but also data on electricity exchanges. If one wants to use the electricity carbon intensity, they also need to combine this data with emissions factors, which represent another heavy collection process with its own challenges. Multiple modelling and calculation steps thus happen between data collection and the actual data used. 

Overview of data providers:

An important note before this table: it was built to the best of our knowledge, and is simply meant to showcase the different providers by needs and services. If someone believes some of this information is incorrect, please contact us.