Information Flow Based Cyber-Physical Power System Modeling and Simulation

The integration of ICT into power systems has increased the interdependencies between the two systems. The operation of power system depends on several ICT-enabled grid services which manifest the interdependencies. ENTSO-E system state classification is a tool that is widely used by operators to determine the current operational state of the power system. However, it does not adequately describe the impact of ICT disturbances on the operation of the power system. Despite their interconnections, the operational states of both systems have been described separately so far. This paper bridges the well-established ENTSO-E systems state classification with an ICT system state classification, forming a new model considering the state classification of the ICT-enabled grid services. The model is developed by first identifying the ICT-enabled services, remedial actions and the respective performance requirements that are required by the power system. Then the states of these services are specified based on the supporting ICT system. The resulting joint state description shows how performance degradation of ICT-enabled services (introduced by disturbances) can affect the operation of the interconnected power system. Two case studies of such ICT-enabled services, namely state estimation and on-load tap changer control, are investigated in terms of how their operational states affect the states of the power system. A third case study highlights the interdependencies that exist between the services. These case studies demonstrate the interdependencies that exist between power and ICT systems in modern cyber-physical energy systems, thus highlighting the usage of a unified system state description.

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.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial

The application of unified power flow controller (UPFC) has become an inevitable trend to deal with challenges faced by modern power system such as optimization of power supply and system operation. However, review researches on UPFC are less while the researches on specific technologies mostly focus on theoretical fields, which are difficult to guide actual engineering. To this end, this paper discusses several key issues of UPFC in its actual application. First of all, technical process of UPFC in actual application was proposed and its six key issues were pointed out. Then, for three issues in the process of “scheme-obtaining of UPFC application”, the principles of necessity to configure UPFC, the method of determining UPFC location and capacity, the method of determining UPFC control strategy are discussed. Finally, for three issues in the process of “influence analysis and optimization for UPFC”, simulation calculation method of power system with UPFC, influences of UPFC on power system operation, changes strategy of operating parameter for UPFC are discussed. The work done in this article aims to provide a reference for the actual application of UPFC.

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".

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.

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.

The electric power system is one of the indispensable infrastructures in modern society, and its stable operation is crucial for guaranteeing economic development and social security. By calculating the correlation coefficient and drawing the correlation heat map, we find that there is a correlation between the voltage at each location and the occurrence of electrical faults, and at the same time, there is also a certain correlation between various types of electrical faults and the voltage at each location. These results indicate that there are complex and close interrelationships between different locations and between different types in the power system. In order to better identify and classify different types of electrical faults, we used the random forest model for prediction. The model is used to classify whether an electrical fault occurs or not and to determine its category. The prediction results show that the Random Forest achieved 99.63% in classifying whether an electrical fault occurred or not, and 91.26% in classifying the category of electrical faults. This shows that the Random Forest algorithm model is able to detect electrical faults well, as well as judge the category of electrical faults. In summary, this paper provides insight into the operation of the power system by studying the correlation between the voltage at various locations in the power system and the different types of electrical faults and using the random forest model to make predictions. These findings are of great significance in ensuring the stable operation of power systems.

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 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.

Wide-area damping power system control based on robust self-triggered model predictive control

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

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 this paper, a fault diagnosis method of marine power system based on MANA is proposed by using the qualitative simulation analysis method of fault diagnosis. The mathematical modeling of ship power system based on modularization can make the system model clearer; The fault belongs to the transient process of the power grid. Based on the simulation analysis and power quality analysis of this transient process of the power grid, the model with built-in MANA algorithm can intuitively monitor the waveform of the input side and output side of the power grid. This paper obtains the changes of each index when the generator loses excitation fault and short circuit fault occurs in the marine power system, which can provide a theoretical reference for solving the fault in the actual marine power system and optimizing the operation of the power grid, and ensure the stable and safe operation of the marine power system.