Development of an Inexact Dynamic Source–Sink Matching Optimization Model for CCUS Cluster System Planning Under Uncertainty

Carbon capture, utilization, and storage (CCUS) clusters present a critical pathway for regional low‐carbon transition. Yet, most CCUS clusters proposed several years ago remain at the conceptual design stage due to systemic complexities involving uncertainties and dynamics. We therefore developed an inexact dynamic source–sink matching (SSM) optimization model for CCUS cluster systems planning under uncertainty. Aligning with technological maturity and large‐scale infrastructure needs, this study specifically denotes CO2 utilization as CO2‐enhanced oil recovery (CO2‐EOR) and exclusively considers pipeline transport. Through integrating interval linear programming (ILP), chance‐constrained programming (CCP), and mixed‐integer programming (MIP) into an optimization framework, the model can not only address uncertainties expressed as interval values and probability distributions but also solve siting, timing, and capacity‐expansion problems within a multi‐period and multi‐option context. The model was then applied to a long‐term CCUS cluster case study in East China. Interval solutions linked to constraint‐violation risk levels were generated to support SSM schemes for CCUS cluster planning. Moreover, when pi${{p}_i}$ increases from 0.01 to 0.1, system costs decrease from $4790.61–$6446.68 to $4652.55–$6273.40 million, which indicated that a desire for cost reduction would increase system instability risks. Optimal CO2 allocation from sources to sinks and capacity‐expansion plans were provided through a compromise among system optimality, reliability, and costs, thereby robustly reflecting the system complexities and uncertainties. Therefore, the developed model can tackle dynamics and interactions of CCUS cluster and help decision‐makers identify adaptive SSM strategies for supporting medium‐ or long‐term planning of CCUS cluster projects.