The extensive construction and promotion of 5G base stations (5GBSs) have led to a surge in communication energy consumption, as 5G energy consumption is about three to five times that of 4G. To reduce the energy consumption of 5GBS, this article incorporates 5GBS into power demand side management and proposes a flexible resource collaborative optimization method that integrates 5GBS with virtual power plants (VPPs). Firstly, starting from the basic structure of 5GBS, the adjustable potential of 5GBS and the paths for participating in VPP optimization were analyzed. Secondly, with the minimum operating cost as the optimization objective and considering the constraints of 5GBS and various decentralized resources, a demand side decentralized resource collaborative optimization model for VPP integrating 5GBS was established. This model can be solved using the Non-dominated sorting genetic algorithm II (NSGA-II) algorithm. The results indicate that the participation of 5GBS in power demand side management can reduce their comprehensive energy consumption and operating costs.

With 5G-powered Power Internet of Things growing rapidly, power terminal devices struggle with energy limitations in processing computation-intensive tasks, increasing carbon emissions. Task offloading faces spectrum constraints. To address this problem, a nonorthogonal multiple access-based edge computing model is proposed to reduce energy consumption and carbon footprint, in which some terminals act as cluster heads, optimizing energy usage by relaying tasks to base station. To achieve low-carbon development, we propose a joint offloading strategy, channel selection, and power control method to minimize energy consumption and latency. Experimental results show that our proposed method reduces computation and transmission burdens, proving cost superiority.

In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system's recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.

Power information data transmission is prone to packet loss rate, and communication protocol is required to improve the transmission capacity of power information data. Therefore, a low delay transmission scheme of power communication information based on 5G networking is proposed and constructed, the power communication information data through 5G networking technology is processed and is sent to the corresponding data application module for data classification and mining, and then the information data is converted in different formats to the same data transmission format. TCP communication protocol as the communication protocol is selected for data online transmission to describe the low delay transmission of power communication information. The chaotic sequence is set based on 5G networking, and the connection weight matrix is calculated. Then the power communication information sequence is encrypted in real time according to the row column substitution rule. On this basis, the data encryption module, dynamic key generation module and shared key update module are combined to realize the low delay transmission of power communication information. The experimental results show that with the increase of the network area and the number of nodes, the data transmission volume can be gradually increased. The change range of this method is relatively small, which can effectively improve the feasibility of the low delay transmission scheme of power communication information.

We make the source network load storage access power wireless private network, this paper proposes a source network load storage access power wireless private network technology based on 5G ultra dense network. The multiple rotation scheduling and self-organizing learning methods are used to establish the deployment model of the source network load storage access node of the power wireless private network under the 5G communication mode. According to the routing control algorithm design of the 5G ultra dense networking node, combined with the integration analysis of the access load parameters, the source network load storage access model of the 5G ultra dense networking under the dynamic load distributed control mode is established. Through the method of optimal control of reactive power and voltage of distribution network, the transmission link equilibrium structure model of 5G source network load storage access to power wireless private network is constructed. Combined with the coverage analysis of link topology structure and the benefit maximization constraint analysis of production and consumption users, the active and reactive capacity analysis of transaction between production and consumption user groups and multiple production and consumption users is adopted. Combined with the energy storage characteristics analysis and power flow parameter calculation of the source network load storage access power, the 5G ultra dense networking and private network access to the source network load storage access power are realized. The test shows that this method has better power balance dispatching ability and larger output power gain when it is applied to the design of source network load storage access power wireless private network.

通过建立"软硬兼施、轻重并济"的融合型密码应用机制,搭建了以密码基础设施为底层支撑的自主可控密码应用环境,形成了密码技术与新型电力系统融合创新的统一密码应用标准机制,为电网关键信息基础设施及重要信息系统提供全面的密码安全保障,有效促进密码应用机制在能源电力绿色转型上的深度应用.

Power Internet of Things (IoT) is an important support for digital innovation service of power energy internet, covering all aspects of power system. Power IoT security defense system may have customer data information leakage during transmission because of the use of traditional means of isolation. This paper proposes a reliable transmission and application security architecture for power smart IoT based on energy interconnection, aiming to solve the reliable transmission and security authentication problems existing in power systems. The paper first analyzes the security risk of the grid wise IoT system, proposes an effective power IoT security transmission scheme, and evaluates the safe and reliable transmission of the grid wise IoT system. Then designed a safe and reliable transmission of the grid smart IoT system, to deal with the traditional power network transmission security and communication security problems. The final application is in the construction of the security transmission platform of the wisdom park of Shanxi Electric Power Company, which provides the corresponding security protection capability in the power IoT through the situational awareness security measures of each layer, and realizes the reliable transmission and security application of the source network load storage and other links in the power IoT environment.

Third-party eavesdropping is a unsolved problem in the process of data transmission in the physical layer of IoT (Internet of Things) in Power Systems. The security encryption effect is affected by channel noise and the half-duplex nature of the wireless channel, which leads to low key consistency and key generation rate. To address this problem, a reliable solution for physical layer communication security is proposed in this paper. First, the solution improved the key consistency by dynamically adjusting the length of the training sequence during feature extraction; Second, using an iterative quantization method to quantify the RSS (Received Signal Strength) measurements to improve generation rate of the key. Finally, based on the short-time energy method for the extraction of wireless frame interval features, by monitoring the change of inter-frame interval features, we can quickly determine whether there is an eavesdropping device into the link. Simulation results show that the reciprocity of legitimate channels R (R will be explained in detail in the following) is improved by 0.1, the key generation rate is increased by about 70%, and the beacon frames are extracted from the wireless link with good results compared to the methods that do not use dynamic adjustment of the pilot signal during the channel probing phase. The result shows that this method can effectively prevents third-party eavesdropping, effectively improves the key consistency and generation rate, and effectively implements beacon frame detection.

With the constant development of big data，cloud computing，Internet of Things and other mobile internet technologies，the new power system with the deep integration of new digital technology and traditional power technology has become an effective means to promote the green transformation of energy and electricity.The introduction of new digital technology also puts forward new requirements for security of energy and electricity. As the edge data transmission in Internet of Things in the power system plays an important role in the underlying data collection architecture of the new power system，it is particularly important to ensure its security and stability. In the traditional data transfer in Internet of Things of the power system，wireless communication technology is susceptible to channel reciprocity，noise and other factors，which lead to the poor key consistency and generation rate. In order to solve this problem，a reliable key generation method for information transmission security in Internet of Things of the new power system is put forward. On the one hand，the length of the training sequence in the channel probing process is dynamically adjusted to improve key consistency；on the other hand，the iterative quantization method is used to quantify RSS measurements to improve the key generation rate. Simulation results show that，this method can effectively improve the key consistency and generation rate，guard against third party eavesdropping，which provides security guarantee for edge data transmission in Internet of Things of the power system.

Distributed combined cooling, heating and power systems (DCCHP) are the main form of distributed energy systems. Currently, predicted values are used for source load data when calculating the DCCHP cost. However, the limitations of various load forecasting methods and renewable energy output forecasting methods can result in errors in the forecast data, which in turn affects the accuracy of the costing of the combined supply system. This paper establishes a comprehensive model of DCCHP with participating components, uses the Latin hypercube sampling technique to simulate fluctuations in the prediction errors of various types of load and renewable energy output forecasts, and uses the scenario reduction technique to extract typical scenarios that can cover most of the errors, and then performs cost calculations for each typical scenario to obtain the system cost with the prediction errors weighted by probability. Finally, simulations and sensitivity analysis are carried out for a typical DCCHP. The results show that the proposed method is able to calculate the actual cost of the DCCHP for different prediction values with different accuracy, providing an effective reference for practical system planning and design.

The access of distributed generation and demand response in active distribution network makes the operation of power grid complex and changeable. The intermittent and uncertain output of some distributed generation makes the voltage fluctuate greatly, which brings difficulties to the prediction of voltage. In order to reduce the impact of uncertainty of distributed generation output on voltage and obtain the real-time operation status and future development trend of active distribution network quickly and accurately, this paper proposes a voltage situation awareness method based on grey correlation analysis. A large number of historical data are used to establish the grey correlation model to predict the voltage situation and trend of distribution network, which provides the basis for the active voltage control.

Distributed combined cooling,heating and power system (DCCHP) has proved to be an effective form of regional comprehensive energy system because of its high energy efficiency.Meanwhile,with the development of demand response (DR) technology,it has become a hot topic to improve the performance of energy system by controlling user's behavior.This paper establishes the regional energy management system (REMS) and proposes an energy management strategy of DCCHP based on DR.At user side,the system loads are classified and the DR control model is established with considering user satisfaction and dynamic compensation cost.According to the characteristics and composition of cold,heat and power load,the REMS subsystem of energy management and control takes the minimum DR compensation cost and load peak to valley difference as the objective to optimize the initial load curve shape.At energy supply side,the optimized load data is input to the DCCHP equipment output scheduling subsystem,and the equipment outputs are controlled by the object of minimum total cost of DCCHP.The simulation results show that the proposed strategy can effectively improve the energy efficiency,reduce the cost of the system,and realize the optimization of both sides of supply and demand.

目前,计算分布式冷热电联供系统成本时,源荷数据均采用预测值.但各类负荷预测方法和可再生能源出力预测方法具有局限性,会造成数据预测存在误差,进而影响联供系统成本计算的准确性.建立参与元件较为全面的联供系统模型,采用拉丁超立方采样技术模拟各类负荷及可再生能源出力预测值的预测误差波动,并用场景削减技术提取出可涵盖绝大多数误差的典型场景,对每种典型场景进行成本计算后按概率加权得到计及预测误差的系统成本.最后,对典型冷热电联供系统进行仿真及灵敏度分析,结果表明,对于不同精度的预测值,所提方法均能计算出符合实际的联供系统成本,为实际系统规划与设计提供有效参考.