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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.

Regional integrated energy system (RIES) can realize multi-energy conversion and complementation so as to improve energy efficiency, which also brings more security risks. The regional integrated energy system security region (RIESSR) is a security analysis method to describe the safe area for the operating points of RIES based on the N-1 guideline. As a controllable device, energy storage (ES) which is installed in the energy hub (EH) plays an important role in improving system security. Therefore, this paper establishes the model of practical security region of RIES integrating energy storage, and studies the impact of ES on total supply capability (TSC) and practical security boundary. Finally, a specific case is set to simulate and verify the model. By comparing the scenario with ES and the scenario without ES, it can be seen that the solution result of TSC increases and the security region extends across quadrant when the RIES is integrated with ES system. The capacity and location of the ES also impact on TSC and RIESSR.

With the increasing installed capacity of renewable energy in the energy system, the uncertainty of renewable energy has an increasingly prominent impact on power system planning and operation. Renewable energy such as wind and solar energy is greatly affected by the external weather. How to use a reasonable method to describe the relationship between weather and renewable energy output, so as to measure the uncertainty of renewable energy more accurately, is an important problem. To solve this problem, this paper proposes a renewable energy scenario generation method based on a conditional generation countermeasure network and combination weighting method (CWM-CGAN). In this method, the combination of AHP and the entropy weight method is used to analyze the meteorological factors, the weather classification is defined as the condition label in the conditional generation countermeasure network, and the energy scenario is generated by the conditional generation confrontation network. In this paper, the proposed method is tested with actual PV data, and the results show that the proposed model can describe the uncertainty of PV more accurately. [ABSTRACT FROM AUTHOR]

With the rapid growth of energy consumption, how to utilize energy in an efficient and cheap way becomes an intensive problem. This paper proposes an optimal operation strategy to reduce system fuel costs and increase system stability by independently considering cooling loads and adjusting CHP heat to power ratio seasonally. In this paper, a mathematical model of CHP operation is introduced to reveal the relationship between the supplementary volume of diesel oil and CHP heat to power ratio. Meanwhile, by analyzing the influence of seasonal factor on energy consumption, CHP heat to power ratio is optimized seasonally. Then, by independently considering the impacts of the cooling loads on system operation, the particle swarm optimization (PSO) algorithm is used to optimize the operation strategy of each device. Finally, this paper validates the positive effects of storage devices on improving system economy and stability under the premise of the time-of-use gas price. Results show that system fuel costs can be reduced by 5.2% if the seasonal factor is considered. Additionally, by optimizing the operation strategy, the peak valley gap of electrical loads in summer reduces by 40.7%. Moreover, the proposed strategy successfully utilizes storage capacity to shift loads and respond to gas price. [ABSTRACT FROM AUTHOR]