  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 degree Celcius 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, we introduce a graphical approach 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, we find that the price and CO2 intensity tends to
decrease with increasing scheduling horizon. The effect diminishes when
requiring an increasing amount of full load hours per year. 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.
