A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications

Abstract

In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 °C above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, a graphical approach is introduced for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, it is observed that the price and CO2 intensity tends to decrease with increasing scheduling horizon. However, the effect diminishes when requiring an increasing amount of full load hours per year. Specifically, for 6000 full load hours per year, the trade-off method leads to reductions of 28% CO2 emissions and 8% costs for West Denmark, 50% and 4% for Norway, 7% and 1% for France, and −4% and −5% for Germany, when compared to the worst case for each of the two parameters. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.