Industrial power has a large load base and considerable adjustment potential. Enterprises with a high degree of automation and adjustable potential can automatically adjust the production status according to the peak load, frequency of the power grid and the demand of new energy consumption, so as to realize automatic demand response. This paper analyzes the opportunities and challenges of industrial demand response under the integration of Industrial Internet and Energy Internet. At the same time, the development direction of industrial demand response under the new situation, such as comprehensive demand response, adjustable load resources and other technical and policy aspects are prospected.

With the development of commercial complexes, higher demands are being placed on the stability and security of internal power systems. As a core component of distribution systems, high-voltage transformers play critical roles in energy conversion, power distribution, and fault isolation and protection. Meanwhile, the power consumption environments of critical users have become increasingly complex, characterized by diversified load types and frequently changing operating conditions, which present greater challenges to the reliable operation of high-voltage transformers. In this context, relying solely on a single signal source for fault diagnosis is no longer sufficient to meet practical requirements. Therefore, the introduction of multi-modal information fusion technology has become essential for improving diagnostic accuracy and comprehensiveness. To enhance the accuracy and intelligence of fault diagnosis in high-voltage transformers, this study proposes a multi-source information fusion-based fault diagnosis method based on the Short-Time Fourier Transform (STFT), Residual Network (ResNet18), and Bidirectional Gated Recurrent Unit (BiGRU), termed STFT-ResBiGRUNet. The proposed model utilizes fundamental electrical and environmental parameters, such as three-phase current, voltage, active power, reactive power, temperature, and humidity, as input features. By integrating the Efficient Channel Attention (ECA) mechanism, key feature representations are enhanced. Moreover, the model combines local feature extraction with the modeling of global temporal dependencies, thereby enabling efficient fault diagnosis of high-voltage transformers. Experimental results demonstrate that the proposed model exhibits significant robustness and high accuracy in noisy environments, outperforming state-of-the-art classification models.

Energy saving and emission reduction have become targets for modern society due to the potential energy crisis and the threat of climate change. A distributed hybrid renewable energy system (HRES) consists of photovoltaic (PV) arrays, a wood-syngas combined heat and power generator (CHP) and back-up batteries is designed to power a typical semi-detached rural house in China which aims to meet the energy demand of a house and to reduce greenhouse gas emissions from the use of fossil fuels. Based on the annual load information of the house and the local meteorological data including solar radiation, air temperature, etc., a system model is set up using HOMER software and is used to simulate all practical configurations to carry out technical and economic evaluations. The performance of the whole HRES system and each component under different configurations are evaluated. The optimized configuration of the system is found

Ubiquitous Internet of Things includes criteria, applications, and technologies for providing standard data. The system can be used to establish a comprehensive data database to facilitate people to better analyze, organize, and use these data, so as to improve the reliability and sharing of data, to provide better services for users. The purpose of this study is to propose and establish a specific and reliable data exchange program to ensure the security of data exchange. Data security is to ensure the reliability of specific security exchange process. The emphasis of this study is the reliability analysis method and the verification method of exchange process behavior. Based on the analysis of all abnormal phenomena in the Internet of Things traffic, the basic characteristics of network traffic, the basic properties of network traffic, and the theory of multiterminal power communication network anti-interference model construction and noninterference model, the simulation experiment of anti-interference and security authentication method is carried out. The results show that, with the increase of the number of antennas, the false detection probability decreases from 10⁻¹ to 10⁻⁴, which can achieve better performance in the detection of active users. When network is used in applications, HTTP+SSL is the most widely used application for data authentication and security authentication. The market of anti-interference technology is developing rapidly. The complex annual growth rate almost doubled in the international market, and the market scale was significantly expanded, with an annual growth rate of about 50.