Designing a cost-effective CO2 storage infrastructure using a GIS based linear optimization energy model

Machteld van den Broek, Evelien Brederode, Andrea Ramírez, Leslie Kramers, Muriel van der Kuip, Ton Wildenborg, Wim Turkenburg, André Faaij

Research output: Contribution to journalArticleScientificpeer-review

80 Citations (Scopus)


Large-scale deployment of carbon capture and storage needs a dedicated infrastructure. Planning and designing of this infrastructure require incorporation of both temporal and spatial aspects. In this study, a toolbox has been developed that integrates ArcGIS, a geographical information system with spatial and routing functions, and MARKAL, an energy bottom-up model based on linear optimization. Application of this toolbox led to blueprints of a CO2 infrastructure in the Netherlands. The results show that in a scenario with 20% and 50% CO2 emissions reduction targets compared to their 1990 level in respectively 2020 and 2050, an infrastructure of around 600 km of CO2 trunklines may need to be built before 2020. Investment costs for the pipeline construction and the storage site development amount to around 720 m€ and 340 m€, respectively. The results also show the implication of policy choices such as allowing or prohibiting CO2 storage onshore on CO2 Capture and Storage (CCS) and infrastructure development. This paper illustrates how the ArcGIS/MARKAL-based toolbox can provide insights into a CCS infrastructure development, and support policy makers by giving concrete blueprints over time with respect to scale, pipeline trajectories, and deployment of individual storage sites.

Original languageEnglish
Pages (from-to)1754-1768
Number of pages15
JournalEnvironmental Modelling and Software
Issue number12
Publication statusPublished - 1 Dec 2010
Externally publishedYes


  • CO capture transport and storage
  • Energy systems model
  • GIS
  • Linear optimization


Dive into the research topics of 'Designing a cost-effective CO<sub>2</sub> storage infrastructure using a GIS based linear optimization energy model'. Together they form a unique fingerprint.

Cite this