A First Shot at Time-Dependent CO<inf>2</inf> Intensities in Multi-Modal Energy Systems

Abstract

CO 2 emission reduction and increasing volatile renewable energy generation mandate stronger energy sector coupling and the use of energy storage. In such multi-modal energy systems, it is challenging to determine the effect of an individual player's consumption pattern onto overall CO 2 emissions. This, however, is often important to evaluate the suitability of local CO 2 reduction measures. Due to renewables' volatility, the traditional approach of using annual average CO 2 intensities per energy form is no longer accurate, but the time of consumption should be considered. Moreover, CO 2 intensities are highly coupled over time and different energy forms due to sector coupling and energy storage. We introduce and compare two novel methods for computing time-dependent CO 2 intensities, that address different objectives: the first method determines CO 2 intensities of the energy system as is. The second method analyzes how overall CO 2 emissions would change in response to infinitesimal demand changes. Given a digital twin of the energy system in form of a linear program, we show how to compute these sensitivities very efficiently. We present the results of both methods for two simulated test energy systems and discuss their different implications.

Hourly CO2 Emission Factors and Marginal Costs of Energy Carriers in Future Multi-Energy Systems

“…The existing methods differ with regard to the input data and the models used (empirical data and statistical relationship models vs. power system optimization models), the time horizon (historical vs. future), the temporal resolution (from less than one hour to one year), the consideration of imports and exports as well as the regional differentiation. Whereas in several analyses, e.g., [8,9], methods are presented to determine hourly average and marginal emission factors of electricity based on power system optimization models, the work on integrating these methods into the modeling of MES is limited. Considering the increasing integration of various energy carriers in future MES, these methods need to be expanded from electricity and district heating to other energy carriers such as methane and hydrogen.…”