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

TL;DR

This paper presents a simple, graphical method for scheduling Power-to-X systems in the day-ahead market to minimize carbon emissions and costs, supporting renewable energy integration and climate goals.

Contribution

It introduces an intuitive graphical scheduling approach for Power-to-X plants that balances cost and carbon emissions, enhancing renewable energy utilization.

Findings

Price and CO2 intensity decrease with longer scheduling horizons.

The trade-off between cost and CO2 reduction is significant and cannot be optimized simultaneously.

Scheduling effectiveness varies with the required full load hours per year.

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 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.