Risk Assessment Of Power System Research Articles

Voltage over-limit risk assessment of wind power and photovoltaic access distribution system based on day-night segmentation and Gaussian mixture model

The access of distributed power sources such as wind power and photovoltaic (PV) with randomness and uncertainty makes the operation of distribution system more complicated. It is particularly necessary to comprehensively and efficiently evaluate the risk of voltage over-limit in distribution systems with wind power and photovoltaic. Firstly, aiming at the problem of centralized sampling of Monte Carlo method in risk assessment of distribution system, a day-night segmented Monte Carlo (MC) sampling method is proposed to generate a large number of day-night sampling scenarios. Secondly, the Gaussian mixture model is used to fit the probability density of the voltage obtained from probabilistic power flow calculations, concurrently, the utility preference exponential function characterizes the severity of the over-limit, and a comprehensive evaluation model of voltage over-limit risk is constructed. Finally, the effectiveness and accuracy of the proposed method are verified by taking the improved IEEE 33-bus distribution system as an example. These results offer actionable insights for future risk assessments of new energy grid connections.

Review on Risk Assessment of Power System under Rainstorm-Flood and Disaster Control Improvement Strategies

Abstract The global warming and El Nino effects are becoming more and more obvious now. These result in frequent rainstorms, especially in coastal areas. It is necessary to deeply study evolution mechanism of rainstorm-flood disasters, assess disaster risk of power grid, and formulate efficient power grid flood control strategies. Firstly, the research progress of disaster modeling is summarized according to the disaster evolution mechanism. The differences between hydrological models and machine learning models are compared, and the possibility of combining them is discussed. Secondly, current disaster risk assessment methods of power grid and their application in rainstorm-flood disasters to quantify flood prevention capacity are reviewed. Thirdly, the power grid flood control measures are studied from the perspective of pre-disaster grid reinforcement and post-disaster repair to improve resilience. Finally, in view of the above content, the shortcomings of current researches and feasible research directions in the future are proposed.

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

AbstractPower systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System, thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters

Abstract In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.

Research on New Electric Power System Risk Assessment Based on Cloud Model

A large number of new energy power electronic equipment is connected to the new power system, and the high proportion of new energy brings huge volatility and randomness, so it is necessary to establish a systematic risk assessment system to adapt to the new power system. This paper has improved the neglect of environmental benefits and supply and demand balance in the existing indicator system and established an indicator system for the risk of the new power system from the five perspectives of safety, supply and demand, sufficiency, cleanliness, and flexibility through the analysis of the evaluation indicator system of the large power grid and the influencing factors of the new power system. The index weight of a new power system is established by means of the entropy weight–critic combination weighting method, and the comprehensive evaluation is carried out by combining it with the cloud model. Finally, the specific data of the new power system in Fujian Province from 2020 to 2022 are taken as an example to verify the feasibility of combining the combined weighting method and the cloud model, and it is concluded that the new power system in Fujian Province has been in a “low risk” operation state for the recent three years.

A Risk Assessment Method of New-type Power System Based on TabRAM

Background:: Under the "Dual Carbon" strategy, the new-type power system integrates various complex equipment, especially the addition of wind power, photovoltaic power generation, distributed energy storage and other systems, which not only brings clean and efficient energy to the power system, but also leads to potential risks in the power grid. Objective:: Traditional methods for assessing the risks of power systems mainly focus on the characteristics of traditional power grids. With the emergence of new features in the new power system, these traditional assessment methods are no longer effective, which could affect the safe and stable operation of the power system. To address this issue, this article proposes a new vulnerability index system for power systems. It also introduces the analytic hierarchy based on Moody's order graph (MAHP) quantification analysis method, and develops the Tabular data risk assessment methodology(TabRAM) risk assessment model based on an improved Transformer method. Method:: MAHP is a comprehensive evaluation method combining Moody's Chart and Analytic Hierarchy (AHP), which has high comprehensiveness and operability. TabRAM uses Bayesian neural networks (BNN) to complete prior data fitting and model complex feature dependencies and potential causal mechanisms on tabular data. The network is trained using an improved Transformer, and after pre-training, it can approximate new prior probabilistic inference in a forward pass to achieve prediction and classification tasks for new datasets. Results:: After multiple rounds of iterations and parameter adjustments, the model achieved the desired performance and was successfully applied to the risk assessment of the new power system. Conclusion:: According to the experimental results, the proposed TabRAM model demonstrates superior performance in the field of risk assessment for the new-type power system, as compared to traditional deep learning and machine learning algorithms.

Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

AbstractDuring the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.

Risk Decision for a Port Shore Power Supply System Based on Cumulative Prospect Theory and an Improved Gray Target

Considering carbon neutral development strategies, ecological ports and green growth concepts have become the future development trend of ports. As an important technology for green port construction, the stable operation of port shore power systems is particularly important. However, as a new field of transmission system, the development of port shore power is also facing many new risks and challenges. For example, the shore power system has security risks as well as technical and economic difficulties. There are also problems such as the lack of regulations on shore power systems. To ensure the safe and stable operation of a port grid system, this paper proposes a risk decision method for a transmission system in a market environment based on interval number and cumulative prospect theory. Based on the constructed risk evaluation index system of a port shore power supply system, a risk decision model based on cumulative prospect theory and an improved gray target risk model (CPT-IGT) are constructed considering the uncertainty and ambiguity of the index attributes and weight information as well as the risk attitude held by decision-makers; these models are developed with improved interval gray number and cumulative prospect theory. Finally, a case study applied to the risk decision of a port shore power supply system verified the reasonableness and effectiveness of the method. The results show that the proposed method has some advantages in dealing with language terms, representing language uncertainty, and reflecting risk decisions in different environments. The research can provide a theoretical basis for the risk assessment of a port power system in the future and provide suggestions for the green growth of the port.

The Application of Risk Identification in the Safety Production of Electric Power Enterprises

With the continuous maturity and application of electric power technology, the electric power system has gradually moved towards the direction of large power grid, ultra-high voltage, large unit and intelligence, and the operation safety problems of electric power enterprises continue to emerge. In the safety risk management of electric power enterprises, the first thing is to evaluate the risks existing in the work of power grid. Risk assessment needs to monitor the working condition of the power system and evaluate the possible risks in the actual working process. Strengthening the risk assessment of power system and improving the safety risk assessment level of power system are of great theoretical significance and engineering application value for the future development of power system. Only by establishing the safety risk assessment system of power system can we make scientific and standardized countermeasures when emergencies occur. In the process of power engineering safety risk management, the primary task is to identify the risk factors correctly and comprehensively. Only by accurately and efficiently identifying risk factors, can we correctly grasp the law of development and change of potential risks, correctly measure the size of risks, and choose effective methods to deal with corresponding risks according to the actual situation.

A Cyber-Physical Power System Risk Assessment Model Against Cyberattacks

With the integration of cyber devices into power systems, such as information communication technology devices and intelligent electronic devices, there are currently a large number of software vulnerabilities (SVs) in cyber devices, which are probably used to obtain the authority of cyber devices for malicious attackers and be carried out subsequently severe attacks on power systems. Thus, it is inappropriate to only consider the power risks as the risk assessment for the system with the strong coupling between cyber systems and power systems. In this article, we investigate a dynamic risk assessment model for the cyber-physical power system (CPPS) against cyberattacks, combining the network security vulnerability of the supervisory control and data acquisition (SCADA) system in a substation and physical consequences in power systems caused by it under malicious control. By construction, a dynamic network security vulnerability assessment method for the SCADA system is well studied by considering SVs and deployed network security defense measures, which is also corrected by the propagation characteristics. Moreover, we estimate physical consequences by minimum shedding loads in N-1 contingencies caused by the SCADA system in a substation under malicious control. Finally, simulations on modified IEEE 14-bus and IEEE 118-bus systems verify the proposed model's effectiveness, highlighting the importance of network security in CPPS.

Wind speed interval prediction based on multidimensional time series of Convolutional Neural Networks

Wind power, as an economical and clean energy source, has rapidly infiltrated into modern power grids. Wind speed prediction is a pivotal technology for wind power integration, which has attracted much attention from researchers and practitioners. Nevertheless, traditional methods only focus on point prediction, which is far from meeting the requirements of power system risk assessment and risk control. To fill this gap, we propose an interval prediction and analysis system based on data preprocessing, deep learning, convolutional neural networks, and interval prediction. It can effectively predict and analyze the uncertainty in short-term wind speeds. First, an original wind speed sequence is divided into high-frequency and low-frequency data using a data preprocessing strategy. After deleting the high-frequency noise data, the denoised multidimensional time series is entered as input into an optimized convolutional neural network. Subsequently, the lower and upper bounds of the interval prediction are obtained using the interval construction principle and variance of the training data set. To verify the effectiveness of the proposed hybrid system, a 10 min wind speed data set of the Shandong wind farm in China is used as an example for analysis. The experimental results show that the proposed hybrid prediction system can not only effectively predict the variation trend of wind speed series, but also accurately predict the local characteristics and volatility of wind speed with strong randomness.

Simulation and risk assessment of power system with cascading faults caused by strong wind weather

Global warming has led to the frequent occurrence of extreme weather events and threatened the stable operation of the power system. The faults caused by extreme weather events are of long time and massive scale characteristics, which pose new challenges to assessing power system operation risk. The model of the relationship between strong wind information and power grid faults was constructed considering the uncertain of line galloping and complexity of line trajectory. A meteorological-electric hybrid simulation approach was constructed with alternative and adaptive simulation strategies to simulate the cascading faults scenarios under the extreme weather of strong wind. The risk assessment method and index combining risk and fragility were proposed based on the proposed hybrid simulation approach. Case studies were performed to verify the feasibility of the proposed risk assessment method.

Research on power system risk assessment considering large-scale wind and solar access

The grid connection of new energy will bring some risks to the safe operation of power system. In this paper, the wind-output power model is established according to the wind-output characteristics, and the generator set, line and load probability models are established considering the uncertainty of other components of the system. From the perspective of power grid operation and new energy consumption, the traditional operation risk indexes such as voltage overrun and voltage collapse and the new wind and light abandoning indexes are selected, and the non-sequential Monte Carlo method is used to sample the operation state of the power system after wind and wind connecting to the grid, and the index is calculated. Finally, the influence of wind grid connection on system operation risk is analyzed to provide guidance for large-scale new energy grid operation risk assessment.

RBFNN assisted transient instability-based risk assessment of cyber-physical power system

Wide area monitoring and control of modern power system sprawling over large geographical area, has emerged as a viable technology employing phasor measurement units (PMUs) as its backbone. The PMUs are optimally placed to make the whole power system observable. Due to tripping of some generating units, or transmission lines the power balance gets disrupted. As a consequence, the power system becomes prone to risk of transient instability. Also, loss of observability becomes an additional risk due to tripping of transmission lines, which is instrumental for contributing to indirect observability. The purpose of this paper is to develop a predictive approach for identifying vulnerable generating units for proactive control actions to ensure transient stability. The methodology involves use of extended Kalman filter (EKF) for processing real-time PMU data for predicting the dynamic capability of generating unit so as to assess the power swings during transients arising due to contingencies. The radial basis feed-forward neural network (RBFNN) assisted algorithm categorizes generating units into marginal and risky categories based in their trend of swings with respect to dynamic capability curve of generating unit. The case-studies pertaining to IEEE-39 bus system under different contingencies, validate the efficacy of the proposed approach for real world applications. Since, the PMUs communicate synchronized data to PDCs the cyber system is an integral part of the wide area monitoring and control system. Therefore, a cyber physical approach takes into account the loss of observability as a risk from the cyber perspective. Both the risk associated with the physical as well as the cyber layers have been investigated.

Research on Risk Assessment of Power Grid Based on Realtime Simulation System

In order to ensure the safe and stable operation of the power system, the importance of power grid calculation in simulation analysis has become more and more prominent. It is particularly important to carry out power system hidden danger investigation and risk assessment for large power grids. This paper builds a "power system full-digital real-time simulation platform" based on ADPSS. Through the modeling of the actual power grid and full fault scanning, the existence and probability of risk, the scope and degree of loss are evaluated and measured, and the operation of the power grid is analyzed. The safety and stability of the power grid, while identifying weak links in the power grid. This assessment method can provide technical support for the investigation and management of hidden dangers in the power grid, and has certain practical significance and positive guidance for the stable operation of the power grid.

Risk Assessment of Power Systems Considering CVaR

In view of the randomness of component fault and load, considering the deficiency of traditional single reliability index, this paper introduces the Conditional Value-at-risk method based on Monte Carlo simulation, and constructs the probability model of risk assessment considering the forced outage rate of unit and line. In order to quantify uncertainty caused by random factors, the value-at-risk and conditional value-at-risk of power system security was proposed and used as a grid security index in the model combined with reliability index, which can accurately describe secure operation level in power system under different operation parameters, and reflect the severity of the accident and weakness and the grid, for further research on safe operation of power system to offer reference and decision-making basis.

Dynamic Cascading Failure Model for Blackout Risk Assessment of Power System With Renewable Energy

To assess the blackout risk of power system with high penetration of renewable, the existing cascading failure models need to be improved for capturing the dynamics and relays of renewable generation. In this paper, a dynamic model of cascading failure considering the utility-scale and distributed renewable energy is proposed. With the solution of dynamic equations for power system, the logics of relays are simulated for components such as transmission lines, conventional generators and renewable generations. The failure interactions among sources, networks, and loads are analyzed more comprehensively. In the proposed model, to capture the impact of renewable energy on the system dynamics, the dynamic equations for the utility-scale renewables are constructed with the second generic generation model of WECC (Western Electricity Coordinating Council), and the interactions among distributed renewables and the transmission system are considered in the amount of net load at buses. And to capture the tolerance of renewables for disturbances, the simulation logic is constructed for the voltage relays and frequency relays of utility-scale renewables and the anti-islanding relay of distributed renewables. The presented model is verified on the IEEE 39-bus system. The results show that renewable energy has a significant influence on the cascading failure risk.

Overview on Risk Assessment of Power System under Typhoon Disaster

With the frequent occurrence of typhoon disaster and the resulting large-scale power outage losses, the risk assessment of power system under typhoon damage has been widely concerned. In this paper, we summarize the research status of risk assessment of power system under typhoon damage. Firstly, we introduce several classical wind field model. Secondly, based on the static classical wind field model, we introduce the research progress of typhoon dynamic wind field model. By summarizing the existing risk assessment theory of typhoon disaster, it is divided into system level and component level. In the systematic risk assessment theory, it is further classified into the traditional risk assessment theory based on reliability principle and the new risk assessment theory based on toughness index. Finally, we make a conclusion and outlook on the research of the topic.

Risk Assessment of an Electrical Power System Considering the Influence of Traffic Congestion on a Hypothetical Scenario of Electrified Transportation System in New York State

With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.

Comprehensive Risk Assessment Method of Power Grid Based on Grey Relational Weight Game Theory

Taking the risk assessment of power system which include new energy grid and AC/DC hybrid structure as the research object, the paper proposes a comprehensive risk assessment method based on game theory and grey relational projection. Firstly, by analyzing the risk factors faced by modern power grids, the risk assessment index system is reconstructed from three aspects, grid structure, operational status and energy supply. Secondly, by creating combination weighting model based on game theory, the subjective weight values and the objective weight values of indexes are integrated to eliminate the weight deviation caused by single weighting method. Thirdly, based on grey relation projection model, the projection values of risk index samples on ideal states are calculated, and the overall risk of samples is represented by its superiority to negative ideal state. The proposed method could identify operation modes with high risk for power grid, which is significant to safe and stable operation of the power grid. Finally, by case analysis, the effectiveness of the proposed method is proved.