Multi‐Timescale Scenario‐Driven Hierarchical Capacity Configuration of Integrated Energy Systems via Multi‐Source Data Fusion

The increasing penetration of renewable energy and growth of flexible loads have introduced considerable uncertainty and complexity into integrated energy systems (IES). To address these challenges, this study proposes a multi‐timescale scenario‐driven hierarchical capacity configuration framework in which multi‐source data fusion is employed to improve data quality, extract joint features and generate representative scenarios for system optimisation. First, a variable‐order bidirectional Markov interpolation approach based on time‐segmented probability models is developed, incorporating anomaly probability information to reconstruct missing data. Then, multi‐source data fusion of random variables is achieved through time‐ and frequency‐domain feature extraction, principal component‐based dimensionality reduction and mini‐batch clustering, thereby generating representative scenarios for IES configuration. On this basis, a hierarchical capacity configuration framework is established. The upper level optimises equipment selection and installed capacity at the daily scale with objectives of investment benefit and system reliability, whereas the lower level refines these decisions at the hourly scale with objectives of environmental performance and operational economy. The multi‐objective optimisation problem is solved using nondominated sorting genetic algorithm‐III (NSGA‐III). Simulation results indicate that the proposed method significantly improves economic efficiency, environmental performance and reliability of IES by leveraging representative scenarios that accurately capture both uncertainties and dynamic characteristics.