## 1 Introduction
Additionally, [PERSON] [17] developed a data-driven DRO model that constructs an uncertainty probability distribution set using a combined \(1\)-norm and \(\infty\)-norm, thereby avoiding the complexity of probability density functions. They also enhanced uncertainty modeling by forming high-fidelity renewable energy scenarios, improving both the operational economy and computational efficiency of the system. However, static optimization models, such as DRO, struggle to address intraday wind and solar forecasting deviations and sudden changes in load demand. Therefore, research has shifted to a \"day-ahead-intraday\" two-stage scheduling framework. [PERSON] [18] adopted model predictive control (MPC) in the intraday stage using day-ahead scheduling results for real-time adjustments through rolling optimization and feedback correction, providing a reliable and adaptive solution. Nevertheless, differences in the response times of various energy systems, such as electricity and heat, make it difficult for a single MPC timescale to address the complexities of multi-energy coordination scheduling. To overcome this, [PERSON] [19] proposed a hierarchical MPC design where the heating system is scheduled hourly at the upper layer. At the same time, electricity and storage devices are regulated on a minute-by-minute and hourly basis at the lower layer, thereby achieving multi-energy coordination scheduling. Building upon the literature review and to position our contribution precisely, we provide a comparative summary in Table 1. This table highlights how our study advances the field by uniquely synergizing key technologies and methodologies. It reveals that prior studies often lack the simultaneous integration of CCS technology within an HCNG framework, while investigations into more flexible and practical hydrogen blending strategies remain insufficient. Furthermore, while some works address uncertainty with DRO [18] or employ multi-timescale scheduling [19], the combination of both within a comprehensive HCNG-CCS-P2G system has been overlooked. Therefore, to bridge these gaps and fully leverage the advantages of IESs in mitigating renewable energy fluctuations and multi-energy load uncertainties, this paper proposes and establishes an HCNG-CCS-P2G system. This system integrates day-ahead DRO with intraday MPC rolling optimization methods, achieving optimized scheduling across different timescales through the coordinated operation of multiple energy flows. Based on the temporal variation characteristics of hydrogen blending, the dynamic hydrogen blending (DHB) strategy is further classified into Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB), exploring their effectiveness and application value in various scenarios. The main contributions of this paper are as follows:  
1. A novel two-stage DRO-MPC framework that optimizes source-load uncertainties and orchestrates multi-energy flow coordination across distinct timescales, thereby substantially enhancing the system's capability to manage uncertainty risks;
2. Demonstration of the HCNG-CCS-P2G configuration's advantages in improving low-carbon performance and economic efficiency, providing a practical framework for power system decarbonization;
3. Investigation into DHB's effectiveness in day-ahead and intraday scheduling within an IES context, elucidating its regulatory role in mitigating system uncertainties and boosting economic outcomes;
4. As an engineering approximation of CHB, the superior performance of THB in both intraday and day-ahead scheduling is validated, providing a simplified engineering solution for practical operations.