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

TL;DR

This paper introduces ElectricityEmissions.jl, a software framework that compares various carbon intensity signals for electricity, analyzing their impact on load shifting and carbon accounting in power systems.

Contribution

It develops a new software package that computes multiple carbon emission metrics and evaluates their effects on load shifting strategies and system-wide emissions.

Findings

Choice of carbon metric significantly affects load shifting outcomes.

Load shifting based on average emissions can reduce individual emissions but increase total system emissions.

Using different metrics leads to varying effectiveness in reducing overall carbon footprint.

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.