Robust optimization of a decarbonized integrated energy system with hydrogen generation considering the impacts of carbon capture strategy

Integrated energy systems (IES) are pivotal for achieving carbon neutrality, yet the uncertainty of carbon capture processes remains a critical planning challenge. To address this, this study proposes a two-stage robust optimal planning model for a decarbonized IES, solved via the column-and-constraint generation (C&CG) algorithm. The model uniquely compares two distinct carbon capture strategies and integrates a logic-based heat storage operation strategy to mitigate renewable fluctuations. Furthermore, six scenarios representing gray, blue, and green hydrogen pathways are systematically evaluated using the analytic network process (ANP). The results demonstrate that: (1) Strategy 2 (total amount matching) outperforms Strategy 1 (real-time matching), creating a significant flexible load margin that reduces grid peak demand and total costs; (2) pure green hydrogen scenarios are identified as physically infeasible under current conditions due to winter energy droughts, highlighting the necessity of stable power sources; (3) the ANP evaluation confirms that Scenario 1 (the blue hydrogen pathway) achieves the highest comprehensive score, offering the most robust trade-off between deep decarbonization and economic feasibility.