Optimal Scheduling of Flexible Power-to-X Technologies in the Day-ahead Electricity Market

TL;DR

This paper presents a scheduling framework for power-to-X technologies that optimizes CO2 emissions and electricity costs in the day-ahead market, using models for local production units and exploring trade-offs.

Contribution

It introduces a novel co-optimization framework for balancing CO2 emissions and electricity prices in power-to-X scheduling, with realistic unit models and trade-off analysis.

Findings

Non-linear trade-off between CO2 emissions and cost.

Weighted optimization improves emission-cost balance.

Potential benefits of emission-price trade-off in scheduling.

Abstract

The ambitious CO2 emission targets of the Paris agreements are achievable only with renewable energy, CO2-free power generation, new policies, and planning. The main motivation of this paper is that future green fuels from power-to-X assets should be produced from power with the lowest possible emissions while still keeping the cost of electricity low. To this end we propose a power-to-X scheduling framework that is capable of co-optimizing CO2 emission intensity and electricity prices in the day-ahead electricity market scheduling. Three realistic models for local production units are developed for flexible dispatch and the impact on electricity market scheduling is examined. Furthermore, the possible benefits of using CO2 emission intensity and electricity prices trade-off in scheduling are discussed. We find that there is a non-linear trade-off between CO2 emission intensity and cost, favoring a weighted optimization between the two objectives.