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SimCCS: An open-source tool for optimizing CO2 capture, transport, and storage infrastructure

Authors: Richard Middleton*, Los Alamos National Laboratory, Kevin Ellett, Indiana University, Brendan Hoover, Los Alamos National Laboratory, Sean Yaw, Montana State University
Topics: Energy, Spatial Analysis & Modeling, Environment
Keywords: Network Optimization, Carbon Capture and Storage, Infrastructure, Energy, Climate Change
Session Type: Paper
Day: 4/8/2020
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual Track 4
Presentation File: No File Uploaded

CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades due to major climate and economics drivers: CCS is part of every major climate policy that limits global warming to 2°C and substantial CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually, requiring CCS infrastructure being deployed on a massive and costly scale. This will require careful and comprehensive planning to ensure that capture locations, storage sites, and the dedicated CO2 distribution pipelines are selected in a robust and cost-effective manner. Introduced in 2009, SimCCS is an optimization model that employs an open-access geographic information system framework to enable researchers, stakeholders, and policymakers to design CCS infrastructure networks. SimCCS2.0 is a complete, ground-up redesign that is now a portable software package, useable and shareable by the CCS research, industrial, policy, and public communities. SimCCS2.0 integrates multiple new capabilities, including a refined optimization model, spatially-novel candidate network generation techniques, and optional integration with high-performance computing platforms. Accessing user-provided CO2 source, sink, and transportation data, SimCCS2.0 creates candidate transportation routes and formalizes an optimization problem that determines the most cost-effective CCS system design. This optimization problem is then solved either through a high-performance computing interface or through third-party software on a local desktop computing platform. SimCCS2.0 is written in Java and is publicly available via GitHub to encourage collaboration, modification, and community development.

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