Electricity Maps Blog
December 23, 2024
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7 minutes
This blog post is part of a series of 7 articles uncovering all you need to know about marginal signals. Are you planning to integrate them into your solution or doing so already? Read more about what you should consider when using them in practice here.
After introducing the concept of marginal signals, the methodology used by Electricity Maps, we discussed why marginal emissions factors are unsuitable for scope 2 carbon accounting and the challenges of using them for real-time decision-making. In this article, we will continue to dive into the limitations of marginal emissions factors, specifically optimizing electricity consumption using marginal signals often does not lead to a reduced carbon footprint.
The topic of carbon-aware electricity consumption has become more widespread with a growing number of applications in carbon-aware computing. An important consideration is choosing the right signals for emissions reporting and decision-making. So, average or marginal emissions factors?
For reporting, there is a clear consensus about using average grid emissions. Marginal emissions factors are unsuitable for scope 2 accounting and have been ruled out by major regulations and leading standards. These standards instead require the use of average emissions factors, of which flow-traced emissions factors are the most accurate version by capturing imports and exports at an hourly level.
However, as highlighted by a study recently published by researchers from the University of Massachusetts and MIT, “there is a lack of consensus on the right signal for carbon-aware optimizations”. Today, both average and marginal carbon intensity signals are used for decision-making.
This study highlights the implications of choosing one signal over the other, by comparing Electricity Maps’ flow-traced (average) emissions factors with WattTime’s marginal emissions factors across 65 grids worldwide. It shows that both signals are statistically different with very low correlation between them, which in practice means “optimizing for one signal could lead to more carbon emissions from the other signal’s standpoint”.
To summarize:
As a consequence, optimizing based on marginal emissions factors could increase carbon emissions calculated on average emissions factors and thus the carbon footprint. In other terms, optimizing consumption with a marginal signal may increase a consumer’s carbon footprint after optimization.
This blog post aims to illustrate the results of this study with examples taken from US and European grids.
This section focuses on the impact on the carbon footprint of choosing between average and marginal emissions factors for load-shifting strategies.
As mentioned in the introduction, standards and regulations require using average emissions factors to calculate the carbon footprint of electricity consumption and report on (corporate) carbon emissions.
Shifting flexible electricity loads (such as computing jobs) in time is one important tool to reduce individuals' and companies’ carbon footprint. We quantify below the change in the carbon footprint from a simple time load-shifting strategy realized over 2023 in 6 grids in the US and Europe: California, Texas, Mid-Atlantic, Germany, Great Britain, and Spain.
As illustrated in Figure 1, optimizing based on hourly average emissions factors will reduce the carbon footprint in all grids. This reduction fluctuates from 7% in Mid-Atlantic US to more than 35% in California. The three European grids considered here yield more than 25% of carbon footprint reduction.
Optimizing based on marginal emissions factors increases the carbon footprint in four of the six grids considered here. Even though this optimization yields a carbon footprint reduction in Spain and Great Britain, it remains below the reduction obtained when optimizing based on hourly average emissions. In Texas and Germany, shifting loads based on marginal signals increases the carbon footprint by 10%.
The largest grid in the US (PJM grid) is taken as an example here as the PJM grid operator publishes marginal emissions factors on their openly accessible data portal (note that a comparison with WattTime’s marginal emissions factors is not possible as they are not made available publicly). For flow-traced (average) emissions factors we use Electricity Maps’ historical data, which is available for free on our data portal.
Even though the PJM grid operator publishes these marginal emissions factors, they warn about their use: “Because of the various constraints involved, PJM cannot make any guarantees as to the accuracy of the information”. Directly measuring marginal emissions factors is in practice impossible and will be discussed in a future blog post.
Figure 2 shows that marginal and average emissions factors in PJM exhibit very different fluctuations in the first days of January 2024.
On the 2nd of January, average emissions factors reached a low shortly after noon, at a time when the share of renewable energy on the grid was the highest. After a reduction in wind generation, average emissions factors increased during the 3rd of January until reaching a peak during the evening when electricity demand peaked and solar generation was null.
Marginal emissions factors reached their lowest value of -900gCO2/kWh at precisely the time when average emissions were the highest. A negative marginal emissions factor means that the higher the electricity demand is on the grid, the less greenhouse gases are emitted into the atmosphere. While this is an expected behavior of the signal, it is counter-intuitive and hard to grasp, which highlights how the grid’s complexity makes marginal emissions difficult to use in practice.
Shifting a flexible electricity load over these four days with marginal emissions factors would have likely redirected consumption to times highlighted in green. However, most of these times coincide with times of high average emissions factors which are precisely the emissions factors used to calculate the carbon footprint. This leads to an increase in consumers’ carbon footprint increase after optimization.
This example illustrates how the lack of correlation between the two signals means that shifting loads with marginal emissions will not reduce (and may even increase) the carbon footprint. Researchers from the University of Massachusetts and MIT found that “among 65 regions, 36 regions (55.4%) exhibit a negative correlation between their average and marginal carbon intensity signal and only 1.5% have a strong positive correlation.”
For most of the grids worldwide, shifting loads in time based on marginal emissions factors would increase the carbon footprint.
Carbon-aware computing is not only about shifting loads in time though. In many cases, there is also flexibility in moving loads to different locations. Temporal and spatial load shifting is what Google uses Electricity Maps forecasts for. The researchers also considered the implications of choosing one signal or the other for spatial load shifting, and their conclusion remains the same: the two signals give opposite indications of where to shift loads.
With more applications of carbon-aware electricity consumption, choosing the right signals for emissions reporting and decision-making becomes crucial.
Average grid emissions make consensus for carbon footprint calculations and emissions reporting is on average grid emissions, with flow-traced emission factors being the most accurate variant. Regulations require their use and prohibit the use of marginal emissions factors.
Both average and marginal emissions factors are used today to optimize electricity consumption. However, optimizing based on one signal may increase emissions from the other signal’s standpoint. For this reason, optimizing consumption with a marginal signal may increase a consumer’s carbon footprint while optimizing based on flow-traced grid emissions will always reduce it.
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