Biblio
This paper puts forward a dynamic reduction method of renewable energy based on N-1 safety standard of power system, which is suitable for high-voltage distribution network and can reduce the abandoned amount of renewable energy to an ideal level. On the basis of AC sensitivity coefficient, the optimization method of distribution factor suitable for single line or multi-line disconnection is proposed. Finally, taking an actual high-voltage distribution network in Germany as an example, the simulation results show that the proposed method can effectively limit the line load, and can greatly reduce the line load with less RES reduction.
The correctness of security control system strategy is very important to ensure the stability of power system. Aiming at the problem that the current security control strategy verification method is not enough to match the increasingly complex large power grid, this paper proposes a cyclic verification method of security control system strategy table based on constraints and whole process dynamic simulation. Firstly, the method is improved based on the traditional security control strategy model to make the strategy model meet certain generalization ability; And on the basis of this model, the cyclic dynamic verification of the strategy table is realized based on the constraint conditions and the whole process dynamic simulation, which not only ensures the high accuracy of strategy verification for the security control strategy of complex large power grid, but also ensures that the power system is stable and controllable. Finally, based on a certain regional power system, the optimal verification of strategy table verification experiment is realized. The experimental results show that the average processing time of the proposed method is 10.32s, and it can effectively guarantee the controllability and stability of power grid.
The security issue of complex network systems, such as communication systems and power grids, has attracted increasing attention due to cascading failure threats. Many existing studies have investigated the robustness of complex networks against cascading failure from an attacker's perspective. However, most of them focus on the synchronous attack in which the network components under attack are removed synchronously rather than in a sequential fashion. Most recent pioneering work on sequential attack designs the attack strategies based on simple heuristics like degree and load information, which may ignore the inside functions of nodes. In the paper, we exploit a reinforcement learning-based sequential attack method to investigate the impact of different nodes on cascading failure. Besides, a candidate pool strategy is proposed to improve the performance of the reinforcement learning method. Simulation results on Barabási-Albert scale-free networks and real-world networks have demonstrated the superiority and effectiveness of the proposed method.
The power grid is considered to be the most critical piece of infrastructure in the United States because each of the other fifteen critical infrastructures, as defined by the Cyberse-curity and Infrastructure Security Agency (CISA), require the energy sector to properly function. Due the critical nature of the power grid, the ability to detect anomalies in the power grid is of critical importance to prevent power outages, avoid damage to sensitive equipment and to maintain a working power grid. Over the past few decades, the modern power grid has evolved into a large Cyber Physical System (CPS) equipped with wide area monitoring systems (WAMS) and distributed control. As smart technology advances, the power grid continues to be upgraded with high fidelity sensors and measurement devices, such as phasor measurement units (PMUs), that can report the state of the system with a high temporal resolution. However, this influx of data can often become overwhelming to the legacy Supervisory Control and Data Acquisition (SCADA) system, as well as, the power system operator. In this paper, we propose using a deep learning (DL) convolutional neural network (CNN) as a module within the Automatic Network Guardian for ELectrical systems (ANGEL) Digital Twin environment to detect physical faults in a power system. The presented approach uses high fidelity measurement data from the IEEE 9-bus and IEEE 39-bus benchmark power systems to not only detect if there is a fault in the power system but also applies the algorithm to classify which bus contains the fault.
In order to design an end-to-end data security preservation mechanism, this paper first proposes a grid terminal data security management model based on master-slave Blockchain, including grid terminal, slave Blockchain, and main Blockchain. Among them, the grid terminal mainly completes data generation and data release, the receiving of data and the distributed signature of data are mainly completed from the slave Blockchain, and the main Blockchain mainly completes the intelligent storage of data. Secondly, the data security management mechanism of grid terminal based on master-slave Blockchain is designed, including data distribution process design, data receiving process design, data distributed signature design and data intelligent storage process design. Finally, taking the identity registration and data storage process of the grid terminal as an example, the workflow of the data security management mechanism of the grid terminal based on the master-slave Blockchain is described in detail.
Reliable and secure grid operations become more and more challenging in context of increasing IT/OT convergence and decreasing dynamic margins in today's power systems. To ensure the correct operation of monitoring and control functions in control centres, an intelligent assessment of the different information sources is necessary to provide a robust data source in case of critical physical events as well as cyber-attacks. Within this paper, a holistic data stream assessment methodology is proposed using an expert knowledge based cyber-physical situational awareness for different steady and transient system states. This approach goes beyond existing techniques by combining high-resolution PMU data with SCADA information as well as Digital Twin and AI based anomaly detection functionalities.
Wide integration of information and communication technology (ICT) in modern power grids has brought many benefits as well as the risk of cyber attacks. A critical step towards defending grid cyber security is to understand the cyber-physical causal chain, which describes the progression of intrusion in cyber-space leading to the formation of consequences on the physical power grid. In this paper, we develop an attack vector for a time delay attack at load frequency control in the power grid. Distinct from existing works, which are separately focused on cyber intrusion, grid response, or testbed validation, the proposed attack vector for the first time provides a full cyber-physical causal chain. It targets specific vulnerabilities in the protocols, performs a denial-of-service (DoS) attack, induces the delays in control loop, and destabilizes grid frequency. The proposed attack vector is proved in theory, presented as an attack tree, and validated in an experimental environment. The results will provide valuable insights to develop security measures and robust controls against time delay attacks.
In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.
The CPS-featured modern asynchronous grids interconnected with HVDC tie-lines facing the hazards from bulk power imbalance shock. With the aid of cyber layer, the SCPIFS incorporates the frequency stability constrains is put forwarded. When there is bulk power imbalance caused by HVDC tie-lines block incident or unplanned loads increasing, the proposed SCPIFS ensures the safety and frequency stability of both grids at two terminals of the HVDC tie-line, also keeps the grids operate economically. To keep frequency stability, the controllable variables in security control strategy include loads, generators outputs and the power transferred in HVDC tie-lines. McCormick envelope method and ADMM are introduced to solve the proposed SCPIFS optimization model. Case studies of two-area benchmark system verify the safety and economical benefits of the SCPFS. HVDC tie-line transferred power can take the advantage of low cost generator resource of both sides utmost and avoid the load shedding via tuning the power transferred through the operating tie-lines, thus the operation of both connected asynchronous grids is within the limit of frequency stability domain.
With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.