Scenario based intelligent desensitization and dynamic access control technology for power distribution and distributed new energy data resources

As the cybersecurity situation continues to get tougher, more and more attackers are beginning to place their attention on power system networks, and the struggle between various countries is shifting from the real world to cyberspace. Power system network security protection is crucial. Humans have become inseparable from electricity, so it is vital to ensure power security. Whether for political or war purposes, many attacks related to power systems have occurred throughout history, resulting in extremely serious consequences and extremely high property damage. And power systems require special protection due to their high speed, high energy, and high-risk characteristics. In this paper, we analyze past network attacks on electric power systems, summarize common network attacks, analyze common network security protection means in the industry, dig into the progress of network security protection research on the electric power system, explore how traditional network security protection means including firewall, antivirus software, intrusion detection, and honeypot technology are applied on the electric power system.

To achieve the national carbon-peak and carbon-neutral strategic development goals, it is necessary to build power systems dominated by renewable and sustainable energy. The future power system with a high proportion of renewable and sustainable energy is required to have large-scale, low-cost, flexible, and adjustable resources. To this end, this article aggregates user-side distributed energy storage and electric vehicles into a virtual power plant, considering the uncertainty of wind power fluctuations and the uncertainty of electric vehicle charging and discharging to establish a day-ahead and intra-day peak regulation model for combined peak regulation of virtual and thermal power plants. The bounding algorithm seeks the optimal strategy for the two-stage model of joint peak regulation and obtains the day-ahead and intra-day two-stage optimal peak regulation strategy. The simulation example shows that the virtual power plant and its day-ahead and intra-day optimal peak regulation strategy can reduce the peak regulation cost of the power system, as compared with the deep peak regulation of thermal power plants with a special supporting energy storage power station. This work provides a global perspective for virtual power plants to participate in the formulation of power system peak regulation rules.

With the rapid development of Internet technology, computer has become an indispensable part of modern society. However, with the popularization of computer technology, the problem of network security is becoming increasingly prominent. In recent years, China's network security incidents occur frequently. As a means of attack, computer deception has seriously threatened national information security, social stability and people's life. Therefore, it is of great practical significance to study the role of computer deception in network security. Its purpose is to mislead and deceive users or systems by various means to achieve the purpose of illegally obtaining information, damaging the stability of the system or carrying out other illegal activities. Based on this, this paper aims to explore the role of computer deception in network security. Firstly, the concept, content and present situation of computer network security are expounded, and then the necessity of computer network security management and protection is analyzed. Secondly, the related technology of computer deception is introduced. Finally, the application of computer spoofing in network security is studied, and the effectiveness of computer spoofing in preventing and detecting network attacks is revealed. In addition, the ethical issues of computer deception and its potential in future cybersecurity strategies are discussed. Research shows that computer deception can not only enhance the defense mechanism, but also improve the organization's cognition and response ability to network threats, and become an indispensable security strategy. By evaluating current technologies and methods, this paper provides practical recommendations for researchers and practitioners to effectively use computer spoofing techniques to improve network security.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

In order to solve the disadvantages of traditional measures for the network security. Network security software consumes a considerable amount of system resources, and dedicated security board or equipment has too much cost. This paper provides a new method of network security protection for personal users which is convenient and can overcome the disadvantages of common network security methods. A Gigabit Ethernet controller chip integrated with the security module is designed, and the security module is based on the state detection technology and misuse of intrusion detection technology. The test results on the FPGA show that the security network card controller designed in this paper occupies less system resources and has better security and protection performance. It can provide a low-cost hardware network security protect solution for individual and family users.

To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security. As a result, the precise classification of encrypted network traffic has become a crucial problem in network security. In light of this, our paper proposes an encrypted traffic identification method based on the C-LSTM model for encrypted traffic recognition by leveraging the power of deep learning. This method can effectively extract spatial and temporal features from encrypted traffic, enabling accurate identification of traffic types. Through rigorous testing and evaluation, our system has achieved an impressive accuracy rate of 96.4% on the widely used ISCXVPN2016 dataset. This achievement demonstrates the effectiveness and reliability of our method in accurately classifying encrypted network traffic. By addressing the challenges posed by encrypted traffic identification, our research contributes to enhancing network security and privacy protection.

Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations

Under the background of the construction of “three types and two networks”, the introduction of a large number of heterogeneous intelligent terminals, ubiquitous open networks, interactive real-time control services and wide area interconnected power systems have transmitted the network security risks to all aspects of the power system. The security defense capability of power system is not only the superposition of security defense equipment, but also the excellent network security team. The continuous improvement of the level of network security is inseparable from the accumulation of network security attack and defense technology and practical experience, as well as practical tools and equipment with the characteristics of the power industry. In order to meet the increasingly severe security protection challenges, actively implement the security protection requirements of the power information system, ensure the safe and stable operation of the power system, build a network shooting range suited to the attributes of the power industry, and continuously improve the ability of network security protection, consolidate the foundation of digital transformation to ensure the safe and stable operation of power system and reliable power supply.The safe and stable operation of the information system needs to carry out comprehensive and in-depth vulnerability troubleshooting. In view of the lack of power business loophole mining and security event verification shooting range environment, this paper studies the power security shooting range construction technology.

The Great Britain (GB) government is paving the path to decarbonisation by actively promoting the integration of wind power into its generation mix. This sharp transition to renewable energy, however, introduces specific challenges. The characteristics of non-synchronous wind turbines have the potential to impact grid stability and frequency security due to the reduction in system inertia. In response to these challenges, the deployment of virtual power plants (VPPs) is envisioned in deregulated power systems, which allow for the aggregation and coordinated control of diverse distributed energy resources, enhancing grid flexibility, reliability, and efficiency. Moreover, VPPs offer the provision of system inertia and frequency response services at the national level. This study drops this assumption and leverages the localised flexibility inherent in VPPs to meet the ancillary service requirements of the low-carbon power system. The formulated problem is constructed as a non-convex bi-level optimisation problem, where the upper-level problem represents the operation of a frequency-constrained unit commitment model, and the lower-level problem embodies the energy dispatches and frequency responses of a group of VPPs, guided by the dual price signals of cleared energy and ancillary services. To address the non-convex nature of the unit commitment problem, a two-fold approach is employed. First, binary commitment status decision variables are relaxed to continuous versions. Subsequently, the duality gap between the original non-convex unit commitment problem and its relaxed dual form is minimised, yielding a solution closely approximating the optimal solution of the original problem. Second, the relaxed bi-level optimisation problem is transformed into a single-level mathematical programs with equilibrium constraints by replacing the lower-level VPP problems with their equivalent Karush-Kuhn–Tucker optimality conditions. The case studies conducted in this work encompass a comprehensive scope. Initially, the study evaluates the effectiveness of VPPs in delivering ancillary services within the projected context of the GB power system for 2030. The findings reveal a substantial 28.28% decrease in system operation costs when compared to the benchmark case lacking VPP flexibility. Additionally, the integration of VPPs leads to significantly lower price signals for inertia, primary frequency response, and enhanced frequency response, reduced by 81.21%, 79.13%, and 86.27%, respectively, in contrast to the benchmark case. The analysis then delves into a sensitivity exploration, investigating the inherent flexibility of VPPs. The profit of local VPPs is expected to increase with VPP flexibility. Finally, the study illustrates how VPPs can adapt to and derive benefits from an increasing level of wind power penetration by providing a higher amount of frequency response.

Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties

With the rapid development of information technology and the advancement of smart city construction, urban management and services increasingly rely on networked intelligent systems. However, this highly interconnected network environment has also brought numerous network security challenges to smart cities. Smart cities involve a large number of Internet of Things (IoT) devices, big data platforms, cloud computing technologies, and smart infrastructure, making network security problems more complex and severe. This paper analyzes the composition and development trends of smart cities, explores the main security risks currently faced, and focuses on the security risks in key areas such as the Internet of Things, cloud computing, big data, and intelligent transportation systems. Furthermore, this paper proposes a multi-layered protection system and strategies for smart city network security, aiming to improve the network security assurance level of smart cities. The research seeks to provide practical protection measures to help smart cities cope with the growing security threats and ensure their sustainable development.

In recent years, with the construction of new power systems, the traditionally relatively closed power grid needs to be opened in an orderly way. The new power system forces more and more heterogeneous mass terminals to be exposed and deployed in the external network, and the power information system interacts frequently with the external system, increasing the attack surface, easy to be external mapping network key information, and increasing pressure of unknown attack. In order to realize the effective protection of network key information, reduce the risk of illegal detection and network attack, and provide technical support for the security protection of new power system. The first part of this paper is to solve the new attack threats faced by the power information system infrastructure by studying the network attack prevention and monitoring technology based on active intelligent defense. The second is to solve the increasingly serious problem of network scanning feature recognition by studying the defense strategies based on feature hiding and information confusion. In this way, the network security protection ability can be improved, and the construction of new power system can be strongly supported.

This article describes problems related to the operation of a virtual micro power plant at the Faculty of Electrical Engineering (FEE), Czestochowa University of Technology (CUT). In the era of dynamic development of renewable energy sources, it is necessary to create alternative electricity management systems for existing power systems, including power transmission and distribution systems. Virtual power plants (VPPs) are such an alternative. So far, there has been no unified standard for a VPP operation. The article presents components that make up the VPP at the FEE and describes their physical and logical structure. The presented solution is a combination of several units operating in the internal power grid of the FEE, i.e., wind turbines, energy storage (ES), photovoltaic panels (PV) and car charging stations. Their operation is coordinated by a common control system. One of the research goals described in the article is to optimize the operation of these components to minimize consumption of the electric energy from the external supply network. An analysis of data from the VPP management system was carried out to create mathematical models for prediction of the consumed power and the power produced by the PVs. These models allowed us to achieve the assumed objective. The article also presents the VPP data processing results in terms of detecting outliers and missing values. In addition to the issues discussed above, the authors also proposed to apply the Prophet model for short-term forecasting of the PV farm electricity production. It is a statistical model that has so far been used for social and business research. The authors implemented it effectively for technical analysis purposes. It was shown that the results of the PV energy production forecasting using the Prophet model are acceptable despite occurrences of missing data in the investigated time series.

In view of the coordinated control and scheduling of distributed energy, such as controllable units, energy storage devices and fans in distribution networks, a virtual power plant source and load storage optimization scheduling model with uncertainties, demand response and network security constraints under centralized control is proposed. Firstly, the wind turbine, gas turbine, energy storage battery and load are integrated into a virtual power plant, and price-type demand response measures are implemented on the user side to improve the load curve. Then, aiming at maximizing the operating profit of the virtual power plant, the opportunity constraint model is used to describe the uncertainty of the wind turbine, load forecasting and internal power balance, and consider the operational constraints and network security constraints of each unit. The scheduling scheme is generated based on the reasonable control and coordination of the output of each component. Finally, the quantum particle swarm optimization algorithm with inertia weight adaptive adjustment is used to solve the model. The feasibility of the model and the effectiveness of the algorithm are verified by an example analysis of the IEEE 9 node improved system.

The virtual power plant brings together all kinds of scattered and adjustable power sources and loads, and forms a virtual “power plant” through digital means for unified management and dispatch, and at the same time participates in the power market as an important role. The air-conditioning load in intelligent buildings is an important adjustable load of the virtual power plant. The adjustment of the air-conditioning load can greatly improve the power load curve, realize peak shaving and valley filling, and avoid excessive power fluctuations of the electricity load in summer. This paper introduces a virtual power plant intelligent building air-conditioning load control system and its control strategy, which maximizes the comfort of air-conditioning service objects while realizing power regulation.