Planning Amongst Uncertainty: Designing CCS Infrastructure Resilient to Capture, Transport, and Storage Uncertainty
Sean Yaw – Montana State University
Richard Middleton – Carbon Solutions LLC
- Carbon Solutions LLC
Develop a CCS infrastructure design model that will account for uncertainty throughout the CCS supply chain, with a particular focus on storage uncertainty.
Impact on Carbon Storage
Output from this project will be used throughout the
CUSP region to quantify the cost of accounting and not
accounting for uncertainty in the infrastructure
Montana State University (MSU) is collaborating with Carbon Solutions LLC to develop a new approach to design CCS infrastructure resilient to associated uncertainties. The team will consider key CCS infrastructure design questions: Which sources to use? Which reservoirs to use? Where to build pipelines? SimCCS will be used to find the least expensive infrastructure options that support the objectives. The classic parameters to consider are, for capture: location, capture cost, capturable amount; for storage: location, storage cost, storage capacity; for transport: location, construction cost, utilization cost, capacity .
An example of uncertainty in CCS infrastructure design could be that the targeted storage location did not perform as anticipated (e.g., storage capacity or injectivity not as high as estimated), therefore an alternate storage must be considered but including this alternative will increase the global cost. Alternatively, an alternative storage site might be found to be cost competitive.
A previous study  developed how to design optimal infrastructure for a set of scenarios, calculated cost for each solution to accommodate all other scenarios and quantified impacts of storage uncertainty on infrastructure performance/cost.
This new focused project aims to improve the previous work by identifying priorities for uncertainty assessment (e.g., storage cost, storage capacity, capture cost). It will explore techniques for endogenously integrate uncertainty into existing models (e.g., distribution and robust optimization) and finally modify the original code (SimCCS) to serve as testbed for various approaches being developed.
- Identify priorities for uncertainty assessment
- Explore techniques for endogenously integrating uncertainty into model
- Develop SimCCS software code to serve as test bed for various approaches being developed