In recent years, the installed capacity and power generation of renewable energy have been increasing continuously. The integration of large amounts of renewable energy generation into the grid brings challenges to power system stability. In this paper, the impacts of converter control strategy, impedance parameter and network topology on the synchronization stability of renewable energy generation system are explored in a single-converter infinite-bus system and a multi-converter system by using simulation method. Based on the simulation results, suggestions are given to improve the synchronization stability of renewable energy generation connected to the grid through power electronic converters.

Existing methods for computing stability boundary of systems with multiple uncertain time delays are either conservative or suffer from low computational efficiency. Addressing this challenge, a value set based method is proposed to solve stability boundary for time delays with high efficiency. Firstly, the characteristic quasi-polynomial of the system is constructed in the periodic multiplication between frequency and time delays (FDM) space. Subsequently, the characteristic quasi-polynomial is transformed by the value set approximations, which is then refined through parameter space subdivision to achieve the required accuracy. Finally, the stability boundary is solved by traversing the parameter space with a bisecting step size. This method is characterized by its efficiency without introducing conservativeness, which is achieved through the decreasing step size space traversal. Numerical examples and a case study based on a microgrid with three distributed generators (DGs) that introduce communication delays are served to validate the accuracy and efficiency.Note to Practitioners-This paper proposes a value set based method to solve stability boundary of the system with multiple uncertain time delays. The method is developed based on the necessary and sufficient criteria. The method leverages the periodicity of FDM and employs a variable step traversal algorithm to decrease the computational burden and achieve high efficiency. Notably, this method can be used for systems with multiple time delays, regardless of the range or quantity of the delays. This method is effective for analyzing systems with multiple time delays, offering support for controller design and enhancing system stability.

Under the global energy transition and the advancement of the “dual carbon” goals, the intermittency and volatility of renewable energy renders traditional static power transmission inadequate. Day-ahead prediction of dynamic transmission capacity is crucial for grid stability. Based on the principles of dynamic line rating and the thermal balance equation, this study improves traditional recurrent neural networks by incorporating an attention mechanism. Utilizing historical weather data, a day-ahead prediction model for dynamic transmission capacity is developed. Through 100 days of historical data for training and validation, the proposed method can accurately output the maximum allowable hourly ampacity and transmission capacity for the following day, with prediction accuracy surpassing that of traditional models. The approach provides a scientific basis for grid dispatch, supporting renewable energy integration and grid security.

With the increasingly prominent “dual-high” characteristics in power systems, transient stability analysis faces severe challenges. This paper proposes the application of PhysicsInformed Neural Networks (PINNs) to transient stability analysis. By incorporating residual terms of differential equations into neural network training, PINNs integrate the differentialalgebraic equations of power systems into model construction. Since PINNs can compute residuals at additional virtual collocation points, this characteristic significantly enhances the fitting accuracy of transient response curves while reducing reliance on real data. The case study in this paper is based on a four-machine, two-area system, where the same three-phase shortcircuit fault is applied under different power disturbances. PINNs are utilized to simulate the system's transient response and determine its stability. By comparing the performance of RK45, NN, dtNN, and PINN across multiple dimensions, the application potential of PINNs is demonstrated.

To meet both the requirements of current limiting and ancillary services during fault ride-through (FRT) of the GFM inverters, various current limiting strategies are developed. Recently, their post-fault behaviors and the switching characteristics have been found to have a significant impact on fault recovery capability. However, the correspondence between the switching conditions and the fault recovery capability has not been fully revealed. The commonalities and uniqueness of various current limiting strategies during fault recovery have not been thoroughly investigated and summarized. To fill these gaps, this paper first graphically illustrates the switching conditions of three typical current limiting strategies. Then, on the basis of the graphical illustration, the initial current-based conditions are converted to be based on the power angle with a fixed range. In addition, the fault recovery capability of three current limiting strategies is investigated and summarized based on the proposed power angle-based switching model. The correspondence between the fault recovery capability and switching conditions is revealed. Consequently, sufficient conditions are obtained for a successful fault recovery. The correctness of the theoretical analyses is verified and validated by numerical simulations and experimental tests.

The coordinated operation of microgrid units is crucial for the society. Since the system output power is full of uncertainties, it is crucial to propose reliable optimal scheduling strategies for microgrids. In this paper, a multi-objective scheduling structure is established based on the output power allocation of each operating unit in the grid-connected mode of the microgrid and solved by the improved multi-objective particle swarm algorithm (MOPSO). Through the simulation model data verification, the proposed method can not only deal with the complex multi-objective microgrid system but also can effectively give the real-time active output of different operating units in one day. Therefore, the optimal scheduling strategy proposed in this paper has high practical value and helps to improve the power supply reliability and economy of renewable energy microgrids.

Grid-forming (GFM) inverters are DC-AC converters that regulate their AC terminal frequency and voltage in response to real-time measurements. Virtual oscillator control (VOC) is an emerging strategy of grid-forming controllers. Unlike conventional phasor-based control methods such as droop control and virtual synchronous machine control, VOC is a time-domain controller that global convergence can be almost guaranteed by adding a nonlinear element. In this article, the state space model of a VOC-based inverter system connected to an infinite bus is established to access the steady-state values. In order to study the effect of nonlinear terms on the system stability, a detailed impedance model of the VOC controller is proposed. Numerical simulations validate the impedance model. Through the impedance-based method, how the nonlinear term can influence system stability is analyzed.

Induction-heating graphitization furnaces are widely used to produce high-purity graphite products due to their high heating rate, high-limit temperatures, safety, cleanliness, and precise control. However, the existing induction-heating systems based on copper coils have limited energy efficiency. This paper proposes a new induction-heating graphitization furnace based on graphene coils. Due to the excellent high-temperature resistance of the macroscopic graphene material, the coil can be placed closer to the graphite heater, which improves the electromagnetic efficiency; the coil itself does not need to pass cooling water, which reduces the heat loss of the furnace and ultimately results in a higher energy efficiency of the induction furnace. In this paper, a numerical model of the induction-heating process is established and verified, the temperature-field and electromagnetic-field distributions of the heating process are analyzed by using the model, and the energy balance calculations are performed for the original furnace and the new furnace. Through a comparison, it was found that the new furnace possesses an electromagnetic efficiency of 84.87% and a thermal efficiency of 20.82%, and it can reduce the energy consumption by 33.34%, compared with the original furnace. In addition, the influence of the coil parameters on the performance of the induction furnace is discussed. By changing the coil conductivity, the induction furnace can achieve an energy efficiency of 17.76%–18.11%. This study provides new ideas for the application of macroscopic graphene materials in high-temperature induction heating.

Multi-band oscillations in renewable plants threaten system stability. The installation of Phasor Measurement Units (PMUs) in power systems provides high-precision synchronized data. Filtering further improves data quality. Utilizing methods such as Fast Fourier Transform (FFT), Prony analysis, and Dynamic Mode Decomposition (DMD) allow for precise analysis of power system oscillations, thereby improving system stability and reliability. This paper employs the described methods to simulate a renewable energy station model. The results are analyzed and compared, aiming to offer insights for practical applications in real-world stations.

The stability of the power system is crucial to the operation of the whole society, and the oscillation of the power system will threaten its stability. Because of the limitations of traditional methods, dynamic mode decomposition (DMD) is introduced in this paper to improve the accuracy and efficiency of power system oscillation identification. Through the data verification of the simulation model, the accuracy of DMD and Prony method is almost the same, and it has higher computational efficiency and real-time performance, which also shows that dynamic mode decomposition method has great potential in power system oscillation analysis.

Abstract This paper establishes the impedance model of a grid-following converter without grabbing control delay and frequency coupling effects into attention, and uses the sinusoidal linearization to operate method to create both sides of the sequence impedance model in order to study the oscillation problem of power systems that contain renewable energy. The grid-connected system’s stability will be evaluated in light of this. MATLAB/Simulink simulations were completed to inspect the veracity of the created resistance model and the reliability analysis results.

The modern power system exhibits a double high characteristic, where the physical structure and control strategies of power electronic devices have altered the stability characteristics of the modern power system. This paper investigates the transient instability issue faced by virtual synchronous generators (VSG) equipped with a current limiter during grid faults. Firstly, the transient stability model of the VSG is established. Then, the equal-area criterion (EAC) is employed to reveal the mechanism of transient instability in VSG and elucidate the influence of the current limiter on the transient stability of VSG. Based on this analysis, current angle configuration methods are further proposed to enhance the transient stability of VSG. Finally, the correctness of the mechanism analysis and proposed methods is validated through simulation.

Induction-heating graphitization furnaces are widely used to produce high-purity graphite products due to their high heating rate, high-limit temperatures, safety, cleanliness, and precise control. However, the existing induction-heating systems based on copper coils have limited energy efficiency. This paper proposes a new induction-heating graphitization furnace based on graphene coils. Due to the excellent high-temperature resistance of the macroscopic graphene material, the coil can be placed closer to the graphite heater, which improves the electromagnetic efficiency; the coil itself does not need to pass cooling water, which reduces the heat loss of the furnace and ultimately results in a higher energy efficiency of the induction furnace. In this paper, a numerical model of the induction-heating process is established and verified, the temperature-field and electromagnetic-field distributions of the heating process are analyzed by using the model, and the energy balance calculations are performed for the original furnace and the new furnace. Through a comparison, it was found that the new furnace possesses an electromagnetic efficiency of 84.87% and a thermal efficiency of 20.82%, and it can reduce the energy consumption by 33.34%, compared with the original furnace. In addition, the influence of the coil parameters on the performance of the induction furnace is discussed. By changing the coil conductivity, the induction furnace can achieve an energy efficiency of 17.76%-18.11%. This study provides new ideas for the application of macroscopic graphene materials in high-temperature induction heating.

Induction-heating graphitization furnaces are widely used to produce high-purity graphite products due to their high heating rate, high-limit temperatures, safety, cleanliness, and precise control. However, the existing induction-heating systems based on copper coils have limited energy efficiency. This paper proposes a new induction-heating graphitization furnace based on graphene coils. Due to the excellent high-temperature resistance of the macroscopic graphene material, the coil can be placed closer to the graphite heater, which improves the electromagnetic efficiency; the coil itself does not need to pass cooling water, which reduces the heat loss of the furnace and ultimately results in a higher energy efficiency of the induction furnace. In this paper, a numerical model of the induction-heating process is established and verified, the temperature-field and electromagnetic-field distributions of the heating process are analyzed by using the model, and the energy balance calculations are performed for the original furnace and the new furnace. Through a comparison, it was found that the new furnace possesses an electromagnetic efficiency of 84.87% and a thermal efficiency of 20.82%, and it can reduce the energy consumption by 33.34%, compared with the original furnace. In addition, the influence of the coil parameters on the performance of the induction furnace is discussed. By changing the coil conductivity, the induction furnace can achieve an energy efficiency of 17.76%-18.11%. This study provides new ideas for the application of macroscopic graphene materials in high-temperature induction heating.

Obtaining the real-time state of the distribution system is the basis for intelligent operation of the power system. With the surge in new energy generation and the volatility in load demands, traditional state estimation methods face significant challenges. The distribution system requires frequent topology reconfigurations to maintain stable operation. However, current data-driven methods typically cater only to specific topologies. To address this issue, a complete distribution system state estimation (DSSE) framework is proposed to adapt to frequent topology reconfigurations. To ensure data quality, a measurement device configuration algorithm using node importance was designed. Then, a convolutional neural network-based topology identification model is utilized to provide real-time topology data to the DSSE, which uses measured data pre-processed by the Gramian corner field. Finally, we use graph attention network to model the DSSE as a node-level regression prediction problem on a graph abstracted from the distribution system. Simulation results on IEEE 33-bus and IEEE 118-bus distribution systems illustrate the feasibility and efficiency of the proposed framework. Further experiments show that the proposed framework has good robustness.

Conventional fossil-fueled power generators are one of flexibility options to accommodate the ever increasing penetration of fluctuating renewable energy whereas their capability constraints are not negligible. A multi-criteria approach is proposed in this paper for evaluating voltage regulation characteristics of fossil-fueled power generators to facilitate their flexible operations. First, an evaluation index system is established accounting for three vital criteria including steady-state performance, dynamic performance, and transient performance of the excitation system. Second, Bayesian BWM and CRITIC methods are used to determine respectively each index’s subjective and objective weights in which the game theory is leveraged to obtain the combined weights. Finally, PROMETHEE II is exploited to rank candidate generators based on the preference functions. The proposed multi-criteria approach is tested on an example system to verify its feasibility and effectiveness. The generator with balanced performance in all aspects prevails in the evaluation of the voltage regulation characteristics.

Concentrated Solar Power (CSP) has gradually become an emerging development direction in the clean energy area because of its high energy efficiency, renewable and abundant nature. Thermal storage devices are used to regulate the power generation characteristics of CSP plants, and a rational configuration of thermal storage capacity can effectively reduce the system's carbon footprint. Considering the generation cost of thermal power generators, operation and maintenance cost of CSP plants, and system spinning reserve cost, a low-carbon economic dispatch scheme for power systems including CSP plants that takes carbon trading into account is proposed in this paper. On this basis, a thermal storage capacity allocation method for CSP plants is proposed, taking into account the thermal storage cost and dispatching cost. Additionally, simulation verification is performed on IEEE 30 network, and the findings demonstrate that the designed thermal storage capacity configuration strategy effectively reduces carbon emissions and overall system costs.

In a grid with a high proportion of renewable energy, the coordinated control between grid-forming (GFM) and grid-following (GFL) inverters becomes crucial for maintaining aspects such as frequency and voltage regulation. This paper presents a decentralized competitive power coordination scheme, designed for a large-scale networked inverter system based on the Mean Field Games (MFG) concept. This scheme effectively harmonizes GFM and GFL inverters to achieve both frequency recovery and active power distribution within the distribution network. Each inverter devises its optimal active power control strategy by maximizing a frequency-constrained revenue function. Through the market price, each inverter's control strategy influences others' strategies. Importantly, this scheme simplifies the interaction among inverters by averaging the impacts generated by each inverter's control strategies. For the MFG control scheme, a decentralized policy iteration algorithm is proposed, eliminating the need for information exchange between inverters and significantly decreasing the communication burden. The effectiveness of the proposed control framework is corroborated using the IEEE 33 test system.

A power balance control method is proposed for renewable energy source (RES) integrated power systems based on deep reinforcement learning (DRL), with the reasonable utilization rate of renewable energy optimized. This method considers the dispatching problems of a high-proportion renewable energy grid from a new perspective. It is proposed that the dispatching of a high-proportion renewable energy grid must consider the reasonable utilization rate of renewable energy and conduct reasonable abandonment of wind and light. And in the offline training of DRL scheduling, the reasonable utilization rate is used as the element of the state vector to train the final power grid DRL control strategy. The control strategy has verified its effectiveness in the IEEE 14-bus system with the supporting datasets.

With the global warming and climate change, the low-carbon power system has attracted increasing attention. A low-carbon optimal dispatch model incorporating carbon capture and storage technology and the uncertainty of wind power is proposed. An accurate calculation of carbon dioxide emission is established, and the carbon tax mechanism is incorporated into the traditional economic dispatch taking into account the economy and low-carbon of power production. The effectiveness of the proposed method is verified by simulation results of modified IEEE 24-bus system. The case study shows that the proposed model can significantly reduce carbon emissions of coal-fired units and encourage coal-fired units to improve their emission performance.