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Integrated energy systems （IES）， characterized by multi-energy complementarity， can enhance energy utilization and improve economic efficiency through effective scheduling. To achieve optimal scheduling for IES， the energy characteristics of heating and cooling system networks are considered， and an energy transport model for these systems is developed. By leveraging the flexibility of heating and cooling loads and the energy storage properties of the thermal networks’ working medium， a virtual energy storage model for the IES， integrating cooling， heating， and power systems， is developed. The heating and cooling systems are integrated as scheduling resources in the system’s optimal operation， with the objective of minimizing daily operational costs. An optimal scheduling method for IES， considering virtual energy storage， is proposed to achieve energy complementarity among the systems. Simulation results demonstrate that this method effectively balances the mismatch between output and load using the virtual energy storage system， responds proactively to time-of-use tariffs， reduces the peak-valley difference in purchased electricity， and significantly lowers the system’s economic costs.

Given the basic data anomalies and significant redundancy in line loss management within distribution networks， a technique for identifying and correcting abnormal line loss data based on segmented regions is proposed. In view of the redundancy of terminal data， a Kalman filter algorithm is employed to fuse terminal redundant data. Then， by traversing the distribution transformers of various line nodes in the distribution network and using local outlier factor （LOF） algorithm， the operational data are detected. Based on the topological relationship of the distribution networks， the Girvan-Newman （GN） algorithm is used to segment the abnormal nodes. By analyzing the neighboring node measurement data and the imbalance index of the segmented regions， the boundary of the regions is dynamically adjusted until the segmented regions meet the estimated observability conditions. The final division result of segmented regions is obtained， and abnormal data are solved using the measurement model， constraint model， and estimation model within the regions. Finally， an example of the 10 kV Shixin line and Shijin line in a province in Northwest China is used to validate the proposed method. The results demonstrate that the proposed method can identify and correct abnormal line loss data within distribution networks.