ElectricityEmissions.jl: A Framework for the Comparison of Carbon Intensity Signals

TL;DR

This paper tackles the challenge of real-time carbon accounting for electricity by proposing a framework to compare multiple carbon-emission metrics. It introduces ElectricityEmissions.jl, an open-source Julia package that implements ACE, LMCE, ALMCE, and carbon-flow based metrics on MATPOWER power-system cases, enabling both load-shifting simulations and emissions accounting analyses. Through RTS-GMLC case studies, the authors demonstrate that metric choice significantly shapes shifting outcomes: LMCE commonly reduces total system emissions but complicates accounting, while ACE, ALMCE, and LACE largely reallocate emissions without system-wide gains. The work highlights the need for metric-consistent design and provides a practical tool to evaluate novel metrics and their impacts on both consumer-level and system-level emissions, with plans to extend the framework to richer generator-cost models and OPF formulations.

Abstract

An increasing number of individuals, companies and organizations are interested in computing and minimizing the carbon emissions associated with their real-time electricity consumption. To achieve this, they require a carbon signal, i.e. a metric that defines the real-time carbon intensity of their electricity supply. Unfortunately, in a grid with multiple generation sources and multiple consumers, the physics of the system do not provide an unambiguous way to trace electricity from source to sink. As a result, there are a multitude of proposed carbon signals, each of which has a distinct set of properties and method of calculation. It remains unclear which signal best quantifies the carbon footprint of electricity. This paper seeks to inform the discussion about which carbon signal is better or more suitable for two important use cases, namely carbon-informed load shifting and carbon accounting. We do this by developing a new software package ElectricityEmissions$.$jl, that computes several established and newly proposed carbon emission metrics for standard electric grid test cases. We also demonstrate how the package can be used to investigate the effects of using these metrics to guide load shifting. Our results affirm previous research, which showed that the choice of carbon emission metric has significant impact on shifting results and associated carbon emission reductions. In addition, we demonstrate the impact of load shifting on both the consumers that perform the shifting and consumers that do not. Disconcertingly, we observe that shifting according to common metrics such as average carbon emissions can reduce the amount of emissions allocated to the consumer doing the shifting, while increasing the total emissions of the power system.

Figures (5)

• Figure 1: Power generation profiles (top), average carbon emission (middle) and renewable curtailment (bottom) for June 10, 2021 in CAISO. We highlight hours with high curtailment (green) and low average carbon emissions (blue).
• Figure 2: Marginal (blue) and average (orange) carbon emissions for Chicago (data from pjm).
• Figure 3: Annual average nodal carbon emissions intensity, by metric. The buses are shown as circles, and generators are small squares arrayed around the buses. The color of the generators indicate the carbon emissions intensity of the source, while the color of the buses show the annual average carbon emissions. Locations of data centers are circled in red. Transmission lines connecting buses are shown in grey.
• Figure 4: Change in Emissions Resulting From Spatio-Temporal Shifting
• Figure 5: Cross-Metric Shifting/Accounting: Change in Accounted Data-Center Emissions