Object-oriented logic configuration platform design for large power grid security and stability simulation

Virtual synchronous generator (VSG) control algorithm can simulate the characteristics of the traditional synchronous generator, inverter with control can provide the voltage and frequency support for large power grid, and the corresponding inertia and damping characteristics, improving the stability of large power grid. Aiming at the problem of multi inverter parallel circulation suppression, the island micro grid under the virtual synchronous generator (VSG) based on double closed-loop control strategy of virtual impedance, VSG parallel system simulation model built two units of the same capacity, the virtual impedance to suppress the circulation, the simulation results show that the proposed control strategy of circulation.

Existing state-of-the-art realizable RC reduction methods may not be suitable for scalable power grid reductions due to the fast growing computational complexity and the large number of ports. In this work, we present a scalable power grid reduction method for reducing large-scale flip-chip power grids based on recent spectral graph sparsification techniques. The first step of the proposed approach aggressively reduces the large power grid blocks into much smaller power grid blocks by properly matching the effective resistances of the original power grid networks. Next, an efficient spectral graph sparsification scheme is introduced to dramatically sparsify the relatively dense power grid blocks that are generated during the previous step. In the last, an effective grid compensation scheme is proposed to further improve the model accuracy of the reduced and sparsified power grid. Since reduction of each power grid block can be performed independently, our method can be easily accelerated on parallel computers, and therefore expected to be capable of handling large power grid designs as well as incremental designs. Extensive experimental results show that our method can scale linearly with power grid sizes and efficiently reduce industrial power grids sizes by 20X without loss of much accuracy in both DC and transient analysis.

The distance for AC and DC dense transmission channels is very long, the transmission capacity is large, and the number of crossing points for 220kV and above are large. Multiple faults are prone to occur in extreme weather, natural disasters and so on, which seriously endangers the security of the power grid. The transmission sections are generally the weak links of power grid, and the rapid search of key transmission sections considering the influence of multiple faults is of great significance to the protection and control of power grid. The current research focuses on the influence of natural disasters on transmission equipment and transmission section search methods such as grid cut search and network contraction. But the impact of multiple faults on security and stability characteristics of power grid and the weak transmission sections haven’t been analyzed. In this paper, a key transmission section identification method considering multiple preconceived faults is proposed. Multiple faults are divided into three types, which are multiple AC line faults, multiple DC line faults and AC / DC line mixed faults. And the impact of the multiple faults on power grid security is analyzed. Then, the key section identification method considering multiple fault disturbances and the weak section identification method after topology changes due to multiple faults are studied. The validity of the proposed method is verified in scenarios with multiple expected faults by using AC/DC hybrid power grid data. Identification of critical sections under multiple faults is an important basis for on-line risk early warning and decision-making of multiple faults in large power grid, which has important practical value.

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.

This topical issue collects contributions to the interdisciplinary study of power grid stability in face of increasing volatility of energy production and consumption due to increasing renewable energy infeed and changing climatic conditions. The individual papers focus on different aspects of this field and bring together modern achievements from various disciplines, in particular complex systems science, nonlinear data analysis, control theory, electrical engineering, and climatology. Main topics considered here are prediction and volatility of renewable infeed, modelling and theoretical analysis of power grid topology, dynamics and stability, relationships between stability and complex network topology, and improvements via topological changes or control. Impacts for the design of smart power grids are discussed in detail.

Short-term frequency instability is one of the major concerns in power systems with high percentage of converter-interfaced renewable energy sources. Energy storage system (ESS) has proven to be a viable solution for the problem of short-term frequency instability by fast frequency response (FFR). However, the appropriate location, size, and operating strategy of ESS are the main challenges for FFR. Power injection at some buses in large grids may lead to angular separation and instability. In addition, oversizing ESS could lead to huge investments without appropriate returns and under sizing may jeopardizes grid stability. Capacity estimation of ESS for FFR considering the suitable location and overall deployment strategies are missing in the current literature for large power grids. Hence, this research proposes a technique to place and size ESS for better FFR in power grids. The proposed technique consists of two steps. In the first step, a methodology based on frequency dynamic signature (FDS) is developed to identify the most suitable location. In the second step, the required capacity of the ESS is estimated based on a step reduction iterative algorithm (SRIA). SRIA and FDS consider the complete dynamics of power system components that affect the frequency dynamics of the system. The proposed methodology is thoroughly verified for various operating conditions in IEEE 39-bus using DIgSILENT PowerFactory.

In the modern new power grid, it is no longer a single voltage level, but a complex large power grid composed of AC and DC and multiple voltage levels, in which 800kV DC transmission project is the main artery of the large power grid, in the operation and management of the new large power grid, green safety vertical is its core pillar, If there is a forest fire in the transmission line corridor, it is likely to cause a tripping fault, especially for the ultra-high voltage DC transmission lines. The conductor insulation on the line will be damaged by the fire at a height of tens of meters above the ground, causing the power grid to trip. What causes this? The research group has concluded through big data analysis and multi-dimensional analysis that the “hot air column” of the forest fire is the main reason for destroying the external insulation of the ultra- high voltage DC transmission lines. The theory of the hot air column (i.e. the column theory) is extremely easy to destroy the external insulation characteristics of the power grid.

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

The following achievements have been made: in the research of the operation image model of the large power grid, a real-time operation image model of the large power grid has been proposed. Corresponding solutions or technologies such as serviceability calculation have been proposed for typical difficult problems in dynamic measurement data and analysis calculation. Combined with the production needs of demonstration projects, the overall architecture design of the large power grid image model has been proposed, and the interface with simulation data and measurement data has been established to establish the large power grid image model. We have designed and implemented intelligent analysis and decision-making functions for the static safety and dynamic response characteristics of the power grid, as well as a laboratory validation plan for the effectiveness of response driven stability discrimination and control measures. In the analysis of the evolution of transient safety and stability characteristics in electromagnetically systems, a technical route for power grid image model “prediction-planning-correction” state deduction has been proposed, providing basic materials for system state analysis under different operating conditions. We have established a provincial-level grid 10000 node point mixed simulation verification platform, which can be used to verify the effectiveness testing of prototypes in operating scenarios, and conducted research on demonstration project implementation plans.

We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.

This paper deals with the sliding mode estimation for microgrid with time delays. Delay has a great impact on large power grids’ management for microgrid, which terribly reduces the stability and quality of microgrid. Random delay caused by load dependent congestion, constant transmission delay and constant delay in microgrid are considered in this paper. To eliminate the adverse effects of delays, a novel sliding mode estimation-based controller is designed to predict time delays and microgrid states, and to reject the disturbance of estimation errors. The mathematical inverter model containing electrical characteristics is regarded as the model of practical microgrid system. Delay estimation with learning parameter and state estimation are derived according to the inverter model. By regarding estimation errors as disturbance of sliding mode control (SMC), the control signal of SMC is adaptively changed in the sliding mode estimation-based control loop to ensure the stability of system and accuracy of estimation. Exponential reaching law (ERL) is implemented to improve the chattering issues and reaching performance of SMC. Lyapunov approach is exploited to analyze the stability of sliding motion. Finally, the proposed SMC strategy is validated by simulation experiments of a microgrid with time delays.

The problem of power stability in electrical systems has been well analyzed. There exist number of approaches to handle the issue which consider the factors like residual energy in various power grids of the system. However, they suffer to achieve higher performance in power stability. To handle this issue, an efficient deep learning based voltage switching model (DLVSM) is presented in this article. The proposed model adapts deep neural network towards the selection of electrical circuit from the list of serially connected grids. As the grids have their own residual energy and varying voltage production, the proposed model trains the network with different features like average voltage generation, average output voltage and residual energy. Using all these factors, the neurons are designed to measure the Optimized Power Stability Factor (OPSF) for various patterns of grid circuit. The network is designed with number of intermediate layers where each layer has set of neurons which estimates the Power stability factor (PSF) for specific grid unit. The output layer neurons estimates OPSF value for various sequences of grids. Based on the value of OPSF, the proposed model identifies a specific sequence and performs voltage switching to maintain the power stability in the electrical systems. The proposed model improves the performance of power stability and reduces voltage loss.

The increasing electrical load penetration on power systems requires the development of researching on short-circuit current level for large scale power grid, and the problem of short circuit currents has become significant for the planning and operation of the power system.This paper analyses short-circuit current situation as well as the development tendency in China in detail, and proposes a new algorithm to forecast three-phase short circuit current on the basis of genetic algorithm and improved BP artificial neural network algorithm. Taking a wide range electrical network as an example, three-phase short- circuit current computation based on load flow is carried out. Two algorithms are employed to carry out short-circuit current level forecast so that weak points of short circuit current level could be found. This method can be used in basically unchanged circumstances of the grid structure to calculate short-circuit current level. The feasibility and validity of the proposed method is shown by the simulation and computation.

Power grid stability has become an increasingly intricate challenge with the rising integration of renewable energy sources like wind and solar power. These energy sources, while essential for achieving sustainability goals, introduce significant variability and reduce the system’s inertia due to the displacement of conventional synchronous generators. This paper explores the combined role of High-Voltage Direct Current (HVDC) systems and the Kuramoto model in addressing these challenges. The Kuramoto model, traditionally used to study synchronization phenomena, is adapted to represent power system dynamics, enabling simulation of grid stability under diverse conditions. HVDC systems, with their ability to regulate power flows dynamically and reduce instability, are evaluated for their potential to mitigate fluctuations caused by renewable energy. Key topics include the theoretical underpinnings of grid stability, detailed simulation methodologies, experimental scenarios, and results showcasing HVDC’s stabilizing effects. Conclusions emphasize the synergy between advanced control systems and theoretical modelling, providing a pathway toward resilient and adaptable future energy networks. Key words. HVDC technology, Kuramoto-model, stability analysis of power systems

High shares of power from renewable resources in large power supply grids do not imply insurmountable technical problem anymore. A question is rather whether, besides the technical feasibility, it is also economically viable to go for a high share of power from sun, wind, water, geothermal resources and biomass. This paper scrutinizes the costs of a renewable-energy based power supply in the Java-Bali grid, by far the largest grid in Indonesia. The study refers to a challenging power supply scenario in which 100% of the power in the Java-Bali grid comes from renewable resources. This scenario is expressed in a one-year modeling of the load and the respective power supply in the grid. The modeling identifies possible sets of installed capacities of the different power plant types and storage systems that allow the supply of the required electricity. A cost scenario is applied to these sets, which renders the system costs, and finally the power generation costs. All scenarios refer to the year 2050. The results show that, assuming moderate financing costs, the cost of the electricity generation in the possible supply systems would not be higher than that in currently existing systems in developed countries; and it would even not be far away from the power generation cost in the current Java-Bali grid. A secondary result of the study is that the usage of special long-term storage systems for the balancing of seasonal power supply fluctuations, additionally to pumped storages and batteries, is not economically necessary in the considered grid.