Rural integrated energy systems (RIESs) suffer from volatile renewable generation, surging agricultural peak loads, and unevenly distributed forecast errors, which severely undermine scheduling robustness and economic efficiency. Conventional fixed-scenario robust scheduling cannot dynamically adapt to error variations, leading to either excessive conservatism or insufficient operational safety. This paper proposes a forecast-error-driven multi-time-scale hybrid robust-scenario scheduling framework with a three-layer closed-loop architecture: day-ahead multi-scenario robust baseline scheduling, intraday error-driven rolling correction, and real-time dynamic-simulation calibration. Case studies on a practical Yunnan rural system verify that within ±25% forecast error, the proposed method reduces operating costs by 12.8%, increases the renewable energy accommodation rate by 22.6%, lowers the dynamic constraint-violation rate below 0.05%, and maintains the average solution time within 9 s, achieving a superior balance of resilience and economy for complex rural operations.

Against the backdrop of China’s “carbon peaking and carbon neutrality” goals, the abundant renewable resources in rural areas offer natural advantages for developing distributed integrated energy systems. However, the economic feasibility of advanced technologies such as carbon capture and hydrogen energy storage in rural applications requires further evaluation. This paper proposes a rural integrated energy system (RIES) architecture that integrates hydrogen energy storage and carbon cycling, encompassing six types of energy and material carriers—electricity, heat, gas, hydrogen, carbon, and biomass—and establishes a mixed-integer linear programming (MILP) model aimed at minimizing the total annual cost. Taking a typical rural village in northern China as a case study, capacity configuration optimization is conducted for five technical scenarios, and the impacts of the local generation ratio target, photovoltaic (PV) curtailment penalty coefficient, hydrogen storage efficiency, hydrogen energy subsidies, and equipment investment costs are analyzed.The results indicate that the scenario equipped with both carbon cycling and hydrogen storage achieves the lowest total annual cost, approximately 6.8% lower than that of the conventional RIES, with the local generation ratio increasing by about 4.0 percentage points and the PV curtailment rate dropping to 2.2%. Among these, carbon cycling is the primary source of cost reduction, while hydrogen storage further improves local power supply contribution and PV utilization. Sensitivity analysis indicates that increasing the local generation ratio can reduce electricity purchases from the grid, but at the cost of higher equipment investment; the PV curtailment penalty mainly affects PV capacity configuration and the level of PV curtailment, while hydrogen energy efficiency, subsidies, and equipment cost reductions influence the economic attractiveness of hydrogen energy equipment. The findings can provide a reference for the synergistic deployment of carbon cycling and hydrogen storage in RIES.