Nodes In Power Grid Research Articles

A method for quickly determining the individual connection possibilities of power grid nodes

A method for quickly determining the individual connection possibilities of power grid nodes

Integrated Structural Hole and K-shell Algorithm for Tsallis Entropy-based Identification of Key Nodes in Power Grids

Abstract Considering the low-carbon development goals of “peak carbon emissions and carbon neutrality,” traditional energy enterprises, including oil fields, have accelerated the incorporation of new energy into their power grids. However, incorporating new energy generation into traditional oilfield power grids yields a series of safety hazards, making the stability of the oilfield power grid structure increasingly important. In this paper, a redefined theory of structural holes and the K-shell algorithm are utilized to identify both local and global key nodes in the oilfield power grid. The improved Tsallis entropy is employed to recognize these key nodes, accounting for their local influence within the oilfield power grid as well as their global status. Additionally, considering the electrical characteristics of the nodes, a set of measurement metrics suitable for oilfield power grid research is constructed. Finally, the IEEE-39 feeder system is simulated and compared with other key node identification methods. By analyzing the robustness of the topological structure and the loss load value of the power system after removing key nodes, the reliability and superiority of the proposed method are verified.

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

Cyber-Physical Attack Launched From EVSE Botnet

With the development of electric vehicles, millions of electric vehicle service equipment (EVSE) are integrated into the power grid. These EVSEs are weakly protected from cyber-attacks. By exploiting vulnerabilities, attackers could establish a botnet integrating hundred-MW-level EVSE loads from the Internet. Dynamic load-altering attack (DLAA) is powerful to amplify the load disturbance to destabilize the power grid. However, different from large-scale loads controlled by existing DLAA, the EVSE botnet is formulated by tens of thousands of bots widely distributed on various power grid nodes, rendering it challenging to be coordinated. In this paper, we analyze the approach to launching a DLAA from the distributed EVSE botnet. Firstly, we adopt a time-division model to evaluate the impact of communication limitations on the attack vectors of the botnet. Then we develop an analytical model to calculate the coordinated effect of attack vectors from different nodes and with varying phase angles; thus, we provide a foundation for the optimal coordination of distributed bots. Subsequently, considering the power grid has adopted defense measures to eliminate or suppress the attack, we formulate an optimization algorithm to make the attack robust against these potential defense measures. Numerical simulations validate the effectiveness of the proposed approach.

Toward Data Integrity Attacks Against Distributed Dynamic State Estimation in Smart Grid

With the continuous expansion of the power grid nodes scale, traditional centralized state estimation method shows certain limitations in estimation efficiency and accuracy. Recently, some power grids adopt a distributed state estimation method, in which each partition independently estimates the partial state information by partitioning the entire power system. However, the deviation of the state estimation in certain partition will result in the deviation of the estimation results in the entire power grid system. In this paper, we propose the attack strategy against the distributed state estimation in smart grids from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Moreover, the theoretical analysis of the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations are formalized. The effectiveness of the proposed attack strategy is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of a distributed system can indirectly affect the state estimation results of other partitions and attacking against the measurement value of coordination center can directly threaten the state estimation results of the entire power grid. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes two attack strategies against the distributed state estimation of power grid from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Most of the previous works fail to formalize the state estimation deviation of both partial and entire state estimation results of power grid after the attacker launches the attack against the distributed state estimation. We formalize the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations. The effectiveness of the proposed attack strategy against the state estimation of power grid is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of power grid can indirectly cause the deviation in the state estimation results of entire power grid and attacking against the measurement value of the coordination center can directly threaten the state estimation results of the entire power grid. In conclusion, the proposed attack strategies are helpful for the research community to design detection strategies in a targeted manner and can be conveniently applied to the real-world security management system of smart grid.

Identification of Critical Nodes in Power Grid Based on Improved PageRank Algorithm and Power Flow Transfer Entropy

Identifying critical nodes in the power grid is a crucial aspect of power system security and stability analysis. However, the current methods for identification fall short in fully accounting for the power transfer characteristics between nodes and the consequences of node removal on the security and stability of power grid operation. To enhance the effective and accurate identification of critical nodes in the power grid, a method is proposed. This method is based on improved PageRank algorithm and node-weighted power flow transfer entropy, referred to as IPRA-PFTE. Firstly, based on the power flow and equivalent impedance between nodes, and the introduction of virtual nodes, an improved PageRank algorithm is obtained. Then the node-weighted power flow transfer entropy is derived by considering the uniformity of the transfer power flow distribution in the system following the removal of a node. Finally, the importance of nodes is obtained by combining the improved PageRank algorithm with the node-weighted power flow transfer entropy. The method’s effectiveness and accuracy are validated through simulation using the IEEE 39-bus example and subsequent comparison with existing methods.

Toward dynamic stability assessment of power grid topologies using graph neural networks.

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.

Research on Step-down Resonance Suppression and Filtering Device

In recent years, offshore wind farms have developed rapidly, and their installed capacity is getting bigger and bigger, and the offshore distance is getting farther and farther. In order to meet the needs of large-capacity and long-distance offshore wind power transmission, the AC submarine cable has the characteristics of long distance, high voltage and large cross-section. Along with the significantly improved transmission capacity, its equivalent capacitance is also increasing, which further reduces the resonance frequency of the offshore wind power AC transmission system. If the parallel resonance frequency of the offshore wind power transmission system is close to the main harmonic current frequency band of wind turbines, it will cause harmful system resonance and harmonic amplification, significantly increasing the harmonic current of grid-connected lines and the voltage distortion rate of nodes in nearby power grids, and seriously affect the safety of equipment and the safe and stable operation of offshore wind power transmission system. A step-down resonance suppression filtering device applied to the offshore wind power transmission system is proposed. The AC cable is connected to the low-voltage 35 kV side of the compensation winding of the centralized control center to eliminate the resonance risk of the 220 kV system and filter out the harmonic current. Compared with the existing technical scheme, the device has the advantages of simple control, reliable operation, stable resonance suppression effect, low operation loss and the like, and has great engineering popularization value.

Identifying critical nodes in power grids containing renewable energy based on electrical spreading probability

The identification of critical nodes is important for safe operation and accident prevention in power grids. With the accelerated development of renewable energies, the uncertainty of renewable energy has brought greater challenges to the node importance assessment of the power system. The electrical spreading probability method is proposed in this paper to identify the critical nodes in the power grid containing renewable energy. First, the uncertainty factors of renewable energy are established through probability density functions, and Monte Carlo simulation and stochastic DC optimum power flow are adopted to effectively deal with the impact of uncertain power output from solar and wind energies. Then, considering the system topology and load loss after cascading failures calculated by Monte Carlo simulation, a method is proposed to calculate the probability of nodes being infected. Finally, according to the Susceptible–Infected model, a method depending on the propagation ability of nodes is proposed to identify the critical nodes in the complex power system. The effectiveness of the proposed ESP method is verified through simulation examples of the modified IEEE39 power system and the modified IEEE118 power system.

Assessing the comprehensive importance of power grid nodes based on DEA

The safe and stable operation of the power grid is crucial to guarantee basic human demands and promote sustainable economic and social development. To better maintain the sound operation of the power grid, it is meaningful to analyse node importance and identify critical nodes based on the topology of the grid and its performance under cascading failure scenarios. Based on complex network theory, this paper proposes four node importance assessment metrics from static and dynamic perspectives. It performs a comprehensive importance assessment of power grid nodes based on data envelopment analysis (DEA) technology and then further identifies critical nodes in the system. A case study is conducted to validate the methodology. Results show that the suggested strategy may successfully identify the grid's key nodes with high accuracy and differentiation degree. This study is of great value for formulating reasonable emergency plans and improving power grid resilience.

Cloud-Edge Collaborative Scheduling with a Focus on Clean Energy

With the promotion of the national "double carbon" goal, the power system is developing towards the direction of low-carbon transformation. In order to achieve the goal of "striving to peak carbon dioxide emissions by 2030 and striving to achieve carbon neutrality by 2060", we must actively promote the consumption of a high proportion of renewable energy from the grid, which is the most urgent issue to be addressed. In this chapter, access to clean energy will involve multiple aspects such as source, network, load and storage. However, due to the intermittent, random and volatile nature of wind power and photovoltaic power generation, the challenges faced by significant users of the power system are enormous. Traditional energy Internet dispatching adopts centralized dispatching. In this paper, by deploying edge nodes, the autonomous decision-making and autonomous collaboration of power grid nodes at all levels are enhanced, and the collaborative integration level of source, network, load and storage of smart power grid is improved. Aimed at the current power supply and demand imbalance, new energy access problems and energy storage problems. In this paper, a service adaptation algorithm based on dynamic priority is proposed based on the scenario of load storage integration of source network under renewable energy access. Experimental results show that, compared with other algorithms, this algorithm has lower scheduling time and execution time and better performance under the condition of ensuring the highest clean energy consumption rate and first-order load priority response.

Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence

This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks.

Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

Identification of vulnerable nodes in power grids based on graph deep learning algorithm

AbstractComprehensive and timely identification of vulnerable nodes is of great significance to ensure the security of the power system. With the development of power grids, traditional identification methods for vulnerable nodes with high operational risks can no longer meet the actual operation needs in terms of model accuracy, and it is prone to ignore some critical nodes. In view of this, a method based on graph deep learning is proposed for grid vulnerable nodes identification to realize fast and accurate identification of vulnerable nodes. Firstly, the GraphSAGE algorithm is used to aggregate the operation states of each node and its neighbours. The node attribute information and topology information are mapped to the output layer at the same time to form a comprehensive representation of the node state through non‐linear transformation. Then according to the similarity between the states, the improved K‐means algorithm is used to cluster the nodes so as to realize the identification of vulnerable nodes. The distances between the nodes and the vulnerability centre are calculated to rank the node vulnerabilities. Finally, the effectiveness and feasibility of the proposed method are verified by the simulation results of the IEEE 30‐bus system and a practical power system.

Security feedback trust model of power network demand response terminal triggered by hacker attacks

With the rapid expansion of power system scale, demand response business is promoted to develop. More and more demand response terminals are connected to the smart grid, smart grid is an intelligent system that allows the grid to effectively perform its functions. Its data can be used in intelligent decision-making during grid operation, which may be attacked by hackers in practical applications, causing security problems of demand response terminals of the power network. The security feedback trust model establishes trust relationship through trust mechanism, which can effectively ensure the security of interaction between nodes and demand response terminals of the smart grid. Therefore, a security feedback trust model of power network demand response terminal triggered by hacker attacks is proposed. Analyze the role of smart grid in power grid, and use convolutional neural network in artificial intelligence technology to enhance the flexibility of smart grid. Aiming at the security problem of the demand response terminal of the power network being attacked by hackers, based on the trust theory, the security feedback trust model of the demand response terminal of the power network is designed through the main services provided by the security feedback trust model, the trust information storage of the power network nodes and the summary of the main work. Establish the identity trust relationship, adopt the distributed verifiable signature scheme, update the power grid node certificate, update the identity trust relationship, and revoke the identity trust relationship based on the trust evaluation and threshold value to prevent hackers from attacking the power grid demand response terminal. Based on information theory, trust is established and measured. Entropy is used to represent the trust value. Behavior trust evaluation and composition mechanism are introduced into the security feedback trust model of power network demand response terminals to achieve the credibility of identity and behavior among power network nodes. The experimental results show that the proposed method can judge the hacker attacks, reduce the impact of hacker attacks on the trust of power grid nodes, and improve the interaction security between power grid demand response terminals and power grid nodes.

Application of a Tuned Inductor in a DC Power Supply with an Active Compensation Function

This work focuses on the use of a one-phase direct current (DC) power supply equipped with a shunt active filter feature, which enabled the possibility of compensation (minimisation) of reactive and distortion power, generated by a group of loads, that was connected to the same power grid node as the power supply. A tuned inductor, which was included at the input of the controlled current source (constituting the main part of the power supply) allowed for an improvement in the quality of the compensation process, compared to a device with a fixed inductive filter This resulted in a visible reduction of the nonlinear distortions of the grid current. The improvement was made possible by extending the frequency response of the current source, which allowed to increase the dynamics of the current changes at the input of the power supply. This solution represents a new approach to such power devices. This work describes the principle of operation of the proposed converter solution and presents selected test results for a laboratory model of an electric system with this device.

Interval TrendRank method for grid node importance assessment considering new energy

It is of great significance to identify the important nodes accurately and rapidly for preventing accidents in the power system. In this paper, an Interval TrendRank algorithm for identifying key nodes in complex power grids is proposed. The algorithm takes into account the scheduling role of information networks, the instability of new energy generation, and the system topology. The algorithm uses an Extreme Learning Machine based on Genetic Algorithm optimization for new energy generation power prediction. The algorithm uses a TrendRank value function to represent the importance of system nodes. The TrendRank value can iteratively calculate the TrendRank interval value according to the weighted distribution strategy of internally linked nodes, and then rank them. The comparison of four performance metrics fully verifies the effectiveness and superiority of the method.

Power Balance Partition Control Based on Topology Characteristics of Multi-Source Energy Storage Nodes

Aiming at the power balance control of multi-source energy storage grid in the case of a high proportion of new energy grid connection. In this article, a power grid dynamic partition method based on the Markov energy field principle and a priori knowledge model is proposed. Combined with the coordinated dispatching of power grid source-load, a two-layer power balance partition control model based on the topological characteristics of multi-source energy storage nodes is established. First, in the upper-layer model, the energy homogenization method of multi-source energy storage nodes is studied, and the Markov energy field model of power grid node partition based on energy interaction constraints between nodes is established to partition the power grid initially. Combined with the prior model of node dynamic partition, the initial partition is dynamically optimized to realize the dynamic partition of the multi-source energy storage grid. Then, in the lower-layer model, the source-load coordinated dispatching model in the power grid partition area is established to realize the dynamic partition control of the power grid. Finally, based on the real operation data of a northeast power grid and IEEE39 node system, a dynamic partition power control simulation model of a multi-source energy storage power grid is established. The simulation results and analysis show that the dynamic partition power control strategy proposed in this article can effectively improve the regulation ability and economy of the power grid.

Control and Optimization Algorithms for Lattice Power Grids With Multiple Grid-Forming Converters

The growth of renewable energies, together with their power converter interfaces, reshapes power systems into more-electronics power systems. Along with this paradigm shift is the requirement of grid formation by grid-tied power converters. However, existing grid architectures only allow parallel operation of grid-forming converters, which excludes high-voltage applications. This article proposes novel lattice power grids that combine the advantages of multilevel converters and power grids, thereby allowing both serial and parallel connectivity with modularity and scalability. Further, we propose control and optimization algorithms for lattice power grids by use of graph theory. In particular, we investigate H-bridge-based lattice power grids and achieve several objectives, including desired voltages and currents between any two selective nodes in lattice power grids as well as efficiency optimization by minimizing switching actions. To achieve these objectives, this article details control and optimization methodology for square lattice power grids. Finally, the proposed algorithms and lattice power grids are validated via simulation results.

Comparison of different methods of spatial disaggregation of electricity generation and consumption time series

Energy system models involve various input data sets representing the generation, consumption and transport infrastructure of electricity. Especially energy system models with a focus on the transmission grid require time series of electricity feed-in and consumption in a high spatial resolution. In general, there are two approaches to obtain regionalized time series: top-down and bottom-up. In many cases, both methodologies may be combined to aggregate or disaggregate input data. Furthermore, there exist various approaches to assign regionalized feed-in of renewable energy sources and electrical load to the model’s grid connection points. The variety in the regionalization process leads to significant differences on a regional scope, even if global values are the same.We develop a Methodology to compare regionalization techniques of input data for photovoltaics, wind and electrical load between various models as well as data assignment techniques to the power grid nodes. We further define two invariants to evaluate the outcome of the regionalization process at the NUTS 3 level, one invariant for the annual profiles and one for the installed capacities. This Methodology enabled us to compare different regionalization and assignment workflows using simple parameters, without explicit knowledge of grid topology. Our results show that the resolution of the input data and the use of a top-down or a bottom-up approach are the most determinant factors in the regionalization process.