Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing

TL;DR

The paper tackles the problem that private PPAs and market-based attribution can obscure the true carbon impact of electricity, leading to double counting and inaccurate carbon-intensity estimates essential for carbon-aware computing. It uses a data-driven framework with ElectricityMaps data to show how location-based and market-based attribution diverge, quantifying potential double counting up to 66.07% and CI estimation errors up to 194%. Through residential and commercial case studies, it demonstrates how residual grid mix and market-based accounting can mislead carbon-aware load shaping, including EV charging. The work highlights the practical implications for cloud computing, EV charging, and other flexible loads, calls for better visibility into residual energy mixes, and outlines future work to develop robust optimization approaches that accommodate multiple CI signals without overestimating emissions reductions.

Abstract

Many organizations, including governments, utilities, and businesses, have set ambitious targets to reduce carbon emissions for their Environmental, Social, and Governance (ESG) goals. To achieve these targets, these organizations increasingly use power purchase agreements (PPAs) to obtain renewable energy credits, which they use to compensate for the ``brown'' energy consumed from the grid. However, the details of these PPAs are often private and not shared with important stakeholders, such as grid operators and carbon information services, who monitor and report the grid's carbon emissions. This often results in incorrect carbon accounting, where the same renewable energy production could be factored into grid carbon emission reports and separately claimed by organizations that own PPAs. Such ``double counting'' of renewable energy production could lead organizations with PPAs to understate their carbon emissions and overstate their progress toward sustainability goals, and also provide significant challenges to consumers using common carbon reduction measures to decrease their carbon footprint. Unfortunately, there is no consensus on accurately computing the grid's carbon intensity by properly accounting for PPAs. The goal of our work is to shed quantitative and qualitative light on the renewable energy attribution and the incorrect carbon intensity estimation problems.

Figures (8)

• Figure 1: Carbon intensity shows spatial and temporal variations. The regions and periods with high renewable energy have lower carbon intensity.
• Figure 2: An illustration of different scenarios that can arise in a residential environment when attributing carbon-free energy to different entities in the electric grid.
• Figure 3: An illustration of different scenarios that can arise in a commercial environment when attributing carbon-free energy to different entities in the electric grid.
• Figure 4: Hourly average energy production from solar + wind as a percentage of overall energy generation for 123 ElectricityMaps emap regions in 2022. Each circle represents a region.
• Figure 5: Hourly average energy production from solar + wind as a percentage of overall energy generation for the same regions as in Fig. \ref{['fig:avg_solar_wind_percentage_emap']} in 2020 and 2022.