Quantification and Evaluation of Power System Flexibility under Different Time Scales

Guest Editorial: Situational awareness of integrated energy systems

Power grid is developing towards the direction of intelligence. Nevertheless, the giant grid scale, complicated structure and external natural factors result in the potential of large-scaled power failure. Traditional black start power gives priority to hydropower. In areas with shortage of hydro power resources, suitable black start power is insufficient. The key of black start is able to realize the safe and stable operation of wind power and energy storage cogeneration system. Due to the combination of wind turbines and energy storage is a small system, energy storage, as a black start power source, starts the wind turbines together to supply power for the remote load. Therefore, it is necessary to run the balance and stable operation of a small system. In this paper, PSCAD simulation software is employed to conduct modeling of wind power and energy storage system, and the self-starting process of wind storage cogeneration system is analyzed by using resistance sensor as the load. The result verifies the wind power storage system's initiation process.

In the distributed load frequency control (DLFC) scheme, the compromised areas by attackers may endanger the stable and reliable operation of the entire power system. A credibility‐based secure DLFC strategy is in this work proposed to sustain the stable operation of the power system under false data injection attacks. In this secure DLFC method, the gradient‐variation‐based credibility metrics are defined to detect potential attacked measurements. Then, the communication isolation decisions are made based on the adaptive thresholds. To compensate the impact of the communication isolation of misbehaving areas, predictions of future measurements by using the average gradient variation of stored healthy data are feeded back to the area controller. Simulation results of a four‐area power system show the proposed secure DLFC can identify and cut off the communication of the misbehaving areas and still maintain the stability of the power system.

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

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.

In order to alleviate the serious pollution problems caused by traditional thermal power generation, it is an inevitable trend to incorporate clean and renewable energy sources such as wind power into power system dispatching. However, the unpredictability and volatility of wind power posed enormous challenges for the stable operation of power systems. Therefore, it is a popular research topic to improve the traditional scheduling strategy and maintain the stable economic operation of the power system. This paper studied the joint optimization scheduling problem of power system with wind power, hydropower and thermal power, and discussed the effect of hydropower on system scheduling with different adjustment capabilities.

In the process of the construction of China's new power system, we will vigorously promote the research and development of UHV power equipment and the wide application of power electronic devices. UHV power equipment has complex insulation structure and huge volume, bear impact energy load of wind power and photovoltaic of new power system for a long time. It will cost a lot to carry out on-site operation and maintenance tests. Digital twin technology is becoming more and more perfect, and new power system construction is gradually introduced from automobile, aviation and other manufacturing industries. Based on this, this paper introduces the digital twin technology into the high-end power equipment of the new power system, and carries out on-site operation and maintenance simulation test and functional response analysis under high current, high voltage and multi harmonic loads according to its twin model. From the four sensing dimensions of mechanical vibration, gas composition, optical vision and electrical parameters, the improvement of intelligent sensing technology of new power system equipment is analyzed, and the interaction between on-site operating parameters and digital twin model data is realized. On the other hand, GPU computing power expansion technology supporting digital twin multi-source sensing technology is proposed, which can effectively support the dynamic behavior simulation monitoring of equipment from 10-5 seconds to 103 seconds, and the operation life evaluation strategy of high-end equipment is proposed. This paper focuses on the 3D construction of the digital twin model of the high-end equipment of the new power system, and its research method can be extended to the construction of the whole network digital twin model of the new power system. The research results can provide theoretical guidance and technical reference for the application of digital twin technology in high-end power equipment scenarios, and effectively support the safe and stable operation of the new power system with "double high characteristics".

As power big data plays an increasingly important role in the operation, maintenance, and management of power systems, complex and covert false data attacks pose a serious threat to the safe and stable operation of the power system. This article first explores the characteristics of new power systems, and the challenges posed by false data attacks. The application of big data technology in power production optimization, energy consumption analysis, and user service improvement is then investigated. The article classifies typical attacks against the four stages of power big data systems in detail and analyzes the characteristics of the attack types. It comprehensively summarizes the attack detection technologies used in the four key stages of power big data, including state estimation, machine learning, and data-driven attack detection methods in the data collection stage; clock synchronization monitoring and defense strategies in the data transmission stage; data processing and analysis, data integrity verification and protection measures of blockchain technology in the third stage; and traffic supervision, statistics and elastic computing measures in the control and response stage. Finally, the limitations of attack detection mechanisms are proposed and discussed from three dimensions: research problems, existing solutions, and future research directions. It aims to provide useful references and inspiration for researchers in power big data security to promote technological progress in the safe and stable operation of power systems.

In order to ensure the safe and stable operation of power system, enrich the means of power grid analysis and control, and expand the application of deep intelligent learning methods in power grid systems, the application of deep learning intelligent machine learning method in frequency prediction of large power grid is explored. First, on the basis of deep learning, the frequency response mode of large power grid is analyzed and the key characteristic quantities that affect the frequency response mode are extracted. Second, the deep belief neural network (DBN. DNN) frequency prediction model is constructed. Also, the training and testing of the model are introduced. Finally, the input and output based on the DBN.CNN prediction model and the network structure design of the model are analyzed. The prediction performance of the model is evaluated. The results show that when the number of neurons in the hidden layer is 50, the model achieves the optimal prediction effect. Increasing the number of training samples helps to improve the modeling ability and prediction accuracy of the model. For frequency prediction problems, the number of training samples should be set to ≥400, and the number of hidden layers corresponding to the model should be 5. When the number of hidden layer neurons is 10, the prediction accuracy of the DBN/DNN network is poor. When the number of hidden layer neurons is 50, the model can achieve the best prediction effect. Overall, the DBN.DNN prediction model has good prediction performance. The RMSE of the forecast data is O. 0073Hz can basically meet the actual application requirements. Therefore, the frequency prediction method based on deep belief neural network has certain advantages in accuracy and efficiency.

Geomagnetic storm disasters have a significant impact on the safe and stable operation of power systems. Serious geomagnetic storm disasters can lead to cascading accidents in power systems, up to and including voltage collapse accidents. Therefore, it is necessary to analyze and evaluate the operational risks of power systems under magnetic storms. Based on the mechanisms of the cascading failures caused by geomagnetic storms, the technical requirements for the power system safety assessment of geomagnetic storm disasters are analyzed, and an evaluation method is proposed. The key problems and evaluation systems to be solved are also discussed in this paper. A simulation method for the interactions between geomagnetically induced currents (GIC) and power systems is proposed, and a simulation is carried out based on MATLAB and the PowerWorld simulation software. The simulation results show that this method can accurately reflect the impact of faults on power systems and their cascading failures. The present research results provide a reference for further quantifying and preventing the power grid risks caused by magnetic storms.

Modern power system is a typical cyber-physical system (CPS). The stable operation of the modern power system relies increasingly on the well-functioning cyber system. Therefore, it is important to include the possible effects of the cyber system as we assess power system stability. Since our main purpose of considering the cyber system is to study its possible effect on power system operation, and cyber system mainly affect the physical system through affecting the information flow, we mainly focus on the information flow as we model the cyber system. In the proposed model, the details in cyber system operation is not considered to reduce redundant calculation burden. Instead, cyber elements and cyber contingencies are classified and modeled based on their effect on the information flow, reflecting their possible effect on the operation of the power system. We can simulate the CPS by integrating the proposed cyber system model with the power system model and conducting mixed simulation. Case study is carried out in the AVC system in the IEEE-14 bus power system. The result shows the proposed cyber system model and CPS simulation method can be used to analysis the possible effect of cyber contingencies on the power system.

The increasing share of variable renewable energy (VRE) generation poses challenges to power systems. Possible challenges include adequacy of reserves, planning and operation of power systems, and interconnection expansion studies in future power systems with very different generation patterns compared to today. To meet these challenges, there is a need to develop models and tools to analyze the variability and uncertainty in VRE generation. To address the varied needs, the tools should be versatile and applicable to different geographical and temporal scales. Time series simulation tools can be used to model both today and future scenarios with varying VRE installations. Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at Technical University of Denmark, Department of Wind Energy capable of simulating both wind and solar generation. It uses a unique combination of meteorological time series and stochastic simulations to provide consistent VRE generation and forecast error time series with temporal resolution in the minute scale. Such simulated VRE time series can be used in addressing the challenges posed by the increasing share of VRE generation. These capabilities will be demonstrated through three case studies: one about the use of large‐scale VRE generation simulations in energy system analysis, and two about the use of the simulations in power system operation, planning, and analysis.This article is categorized under: Wind Power > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure

The improvement of China's economic development level has actively promoted the development of various industries, and the demand for electric energy has also gradually increased. For power supply enterprises, ensuring the safe and stable operation of the new power system is the prerequisite to meet the high demand for power supply. The new power system can be applied to electrical equipment such as electric power, with the functions of monitoring, automatic detection and maintenance. The new power system's information security related to power supply system stability. We settle this problem by strengthening the safety management strategy of power system and developing automatic risk monitoring technology, which can effectively remove the failure on the one hand, and generate fault alarm on the other hand, which will further reduce the adverse impact of the failure on the operation of the entire power supply system.

In a modern power system, stable operation of the electrical system is a major concern. For the stable operation of power system, it is desirable to access the effect of unforeseen events and identification of more sensitive nodes. The most outstanding job of distribution engineers is to simulate the power system for corrective action. Probabilistic power flow (PPF) is a tool that can effectively access the performance of power system network over most of its working conditions taking into account the unforeseen events. In this paper, a new PPF model is developed to evaluate power system network taking into account the uncertainty with input random variables, such as wind energy, loads, generation outage, and branch outage. This model is based upon the two well-known methods, Monte Carlo simulation (MCS) and point estimation method (PEM). For the sake of, computational efficiency and results accuracy Box-Muller sampling equation was used with MCS and $2m+1$ concentration scheme was used with PEM. The proposed model was investigated by using modified IEEE 14-bus standard test system.

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.