The coordinated implementation of demand response technology and dynamic energy prices facilitates the interaction among multiple stakeholders in the smart integrated energy system. To achieve the optimal operation between different entities in the system, this paper proposes a multi-objective optimization model for smart integrated energy system considering demand responses and dynamic prices that reflects the preferences of multiple stakeholders. Based on the tightly coupled characteristics of a multi-energy system, a flexible two-dimensional demand response model with spatio-temporal coupling characteristics is established. By analyzing the characteristics of multi-entities joint pricing, the dynamic energy price formulated with the participation of both supplier and demander is optimized, and the dynamic price control strategy of different stakeholders under different benefit weights is obtained. A case study verifies the effectiveness of the proposed method. According to the interest preferences of different entities, different strategies and operating mechanisms can be derived, which is conducive to improving the economy and reliability of the operation of the smart integrated energy system and promoting the interaction between multiple entities.

Compared with the fundamental loss, the harmonic losses become very complicated because of the coupling between the power supply harmonics and the motor harmonics. To dispose of this problem, a multislice 2-D finite element model considering the interbar currents and a fast rotor harmonics identification method is employed to identify the harmonics of the rotor current density and flux density of the inverter-fed induction motor (IM). Considering the complex coupling phenomenon of time–space harmonics, a two-term iron model with piecewise-defined variable coefficients (PVCIL model) and a finite element analysis (FEA)-based copper loss method are then used to calculate the iron and copper loss components of the inverter-fed IM. Finally, based on the introduced methods, both the electromagnetic field and different losses terms of the same 5.5-kW inverter-fed IM under different load and supply conditions are calculated and analyzed. The experimental validation of those two losses’ calculation methods is performed on a 5.5-kW IM with different operating conditions. The results obtained in this article can provide the theoretical basis for targeted harmonic elimination and loss reduction in the design phase of the inverter-fed IM.

After the concept of the Energy Internet was proposed in the last century, it has become a topic of great interest in recent years with the development of related technologies and the growing environmental problems. At the same time, the new technology brought by it also poses new challenges for the electrical engineering specialty, which is inseparable from power plants, power grids and other power facilities. How to reform the electrical engineering specialty to better meet the challenges it brings has become a problem that cannot be ignored. This paper comprehensively analyzes the current development status of the Energy Internet, key technologies involved in the concept of the Energy Internet, and problems in current talent training. This paper proposes to carry out curriculum reform through two main lines and to further optimize the curriculum structure, thus forming a more reasonable training program.

University building energy consumption is an important proportion of the total energy consumption of society. In order to work out the problem of poor practicability of the existing benchmarking management method of campus building energy consumption, this study proposes an evaluation model of campus building energy consumption benchmarking management. By analyzing several types of feature data of buildings, this study uses random forest method to determine the building features that have outstanding contributions to building energy consumption intensity and building classification, and uses the K-means method to reclassify buildings based on the building features obtained after screening, to obtain a building category that is more in line with the actual use situation and to solve the problem that the existing building classification is not in line with the reality. Compared with the original classification method, the new classification method showed significant improvement in many indexes, among which DBI decreased by 60.8% and CH increased by 3.73 times. Finally, the quart lines of buildings in the category of new buildings are calculated to obtain the low energy consumption line, medium energy consumption line and high energy consumption line of buildings, so as to improve the accuracy and practicability of energy consumption line classification.

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the “hot spot” phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values.

With the advent of the post-epidemic era, the energy consumption characteristics of hotels have changed, which has an important impact on urban energy conservation. In order to contribute to the goal of carbon neutrality, this study discusses the energy-saving operation strategy of hotels considering the impact of the COVID-19 epidemic. Based on the energy consumption characteristics of large public buildings, this paper analyzes the energy consumption distribution and operation characteristics of hotel buildings in detail. By collecting energy consumption data from five typical large hotel buildings in a tourist city in southern China from 2018 to 2022, the impact of COVID-19 on hotel energy consumption and hotel business characteristics was discussed in detail. Combined with the economic development characteristic in the post-epidemic era, this paper explores the energy-saving strategies that hotels can adopt in the context of normalized epidemic prevention and control and obtains the optimal path of low-carbon economic operation of hotel buildings. This study reveals the energy consumption characteristics and energy-saving potential of hotel buildings, and provides enlightenment for hotel management and low-carbon development in the post-epidemic era.