Advanced Models for Hourly Marginal CO2 Emission Factor Estimation: A Synergy between Fundamental and Statistical Approaches

Global warming is caused by increasing concentrations of greenhouse gases, particularly carbon dioxide (CO2). A metric used to quantify the change in CO2 emissions is the marginal emission factor, defined as the marginal change in CO2 emissions resulting from a marginal change in electricity demand over a specified period. This paper aims to present two methodologies to estimate the marginal emission factor in a decarbonized electricity system with high temporal resolution. First, we present an energy systems model that incrementally calculates the marginal emission factors. Second, we examine a Markov Switching Dynamic Regression model, a statistical model designed to estimate marginal emission factors faster and use an incremental marginal emission factor as a benchmark to assess its precision. For the German electricity market, we estimate the marginal emissions factor time series historically (2019, 2020) using Agora Energiewende and for the future (2025, 2030, and 2040) using estimated energy system data. The results indicate that the Markov Switching Dynamic Regression model is more accurate in estimating marginal emission factors than the Dynamic Linear Regression models, which are frequently used in the literature. Hence, the Markov Switching Dynamic Regression model is a simpler alternative to the computationally intensive incremental marginal emissions factor, especially when short-term marginal emissions factor estimation is needed. The results of the marginal emission factor estimation are applied to an exemplary low-emission vehicle charging scenario to estimate CO2 savings by shifting the charge hours to those corresponding to the lower marginal emissions factor. By implementing this emission-minimized charging approach, an average reduction of 31% in the marginal emission factor was achieved over the 5 years.