EUROPT 2025
Abstract Submission

583. A stochastic programming model for planning CO2 transport infrastructure

Invited abstract in session TC-12: Optimization for sustainable energy systems, stream Applications: AI, uncertainty management and sustainability.

Tuesday, 14:00-16:00
Room: B100/8009

Authors (first author is the speaker)

1. Lihan Zhang

Abstract

Carbon capture and storage (CCS) can be regarded as a significant method to address climate change challenges and reduce greenhouse gases. Since some CCS technologies are novel and in the early stages, it is vital to consider the influence of uncertainty of CO2 capture rate on potential stakeholders in making informed transport investment decisions.
Firstly, one cluster model is constructed, this research aims to develop a suitable scenario tree for the CO2 capture project and apply it in a multistage stochastic programming. When constructing the tree of CO2 capture scenarios, it is important to consider possible shut down and re-establishment situations to account for the risks associated with real-world projects. By considering various cases of scenario trees with their associated probabilities, the model offers investors the flexibility to assess different potential outcomes of CO2 capture development. In addition, the flexibility of ships is considered to test whether the shutdown possibilities influence the chosen of transport mode.
To make the model more realistic, it would be meaningful to include multiple clusters and observe how they cooperate. This will foster collaboration between clusters and the development of CCS projects in the UK. We are now working on the study of how clusters in the UK can cooperate to create a transport network, taking into account the existence of CO2 capture uncertainty in each capture cluster.

Keywords

Status: accepted

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