Synchronous machines form the principal source of electrical power in power systems. Modeling of the synchronous machines for transient analysis has always been an active topic of research. In this paper, a novel wound-field three-phase synchronous machine model is developed for the accurate and efficient simulation of multi-scale transients. The machine stator equations are expressed with analytic signals in the phase domain, thus providing direct interface between machine and external network models. Frequency shifting is applied to stator quantities to eliminate the ac carrier in the stator windings which enables the use of large time-step size. An artificial damper winding is introduced to eliminate the numerical saliency. To provide accurate and stable solutions with multiple time-step sizes, the artificial winding parameters setting algorithm is established. The proposed machine model is expressed in terms of a Norton equivalent with constant admittance matrix without introduction of prediction of any electrical quantity. The update of the admittance matrix at each time-step is avoided. The analysis of test cases demonstrates the effectiveness of the proposed multiscale synchronous machine model and the artificial winding parameters setting algorithm.

The Electromagnetic Transients Program (EMTP) is widely used for simulating electromagnetic transients in power systems. In recent years, applications of electrolyzers in electric power systems have attracted considerable attention because electrolyzers are among the most promising energy-conversion devices for realizing hydrogen production from renewable energy sources while enhancing system flexibility by functioning as controllable loads. With the growing interest in electrolyzers, it would be advantageous to broaden the application of EMTP to include multi-physics transients such as those observed in electrolyzers. This article details the implementation of such an extension for a proton exchange membrane electrolyzer (PEMEL). Analogies between electric, mass transfer, and thermal quantities are adopted to develop an electric circuit model that describes currents, voltages, mass transfer, pressure, heat transfer, and temperature. The PEMEL model considering the interactions of electric, mass transfer, and thermal transients can be readily implemented in EMTP-type programs using existing components from standard libraries. The proposed electric circuit model was validated through comparison with experimental data under both steady-state and transient conditions. The value of the proposed model was illustrated via application to a PEMELbased integrated electrical and gas system.

With the increasing penetration of renewable energy, ensuring reliability and security of power supply has become a significant challenge. In this paper, a novel photovoltaic (PV) intraday power supply guarantee capability forecasting method is proposed. Different from the conventional PV power forecasting methods, it can provide diverse forecasting information, including guarantee power, low output power period, and power supply guarantee probability. PV guarantee power which represents a conservative prediction of PV power is forecasted based on convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) model with a compound loss function. PV low output power period represents the period when the power gap between the theoretical maximum PV power and the actual PV power is higher than a specific threshold. It is forecasted using hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model based on an improved spatiotemporal feature encoding method. The power supply guarantee probability which represents the risk of PV power shortage is obtained using quantile regression model based on gradient boosted regression tree (GBRT) considering different PV power supply demands. Furthermore, new indexes, including guarantee rate, guarantee energy ratio, success index, forecasting gain index, and probability forecasting accuracy are proposed to evaluate the forecasting performance. The effectiveness of the proposed method is verified based on actual operation data in a province in Northwest China.

In response to the difficulties faced by existing leakage fault protection devices in accurately identifying leakage faults in low-voltage distribution networks, which often lead to false tripping or failure to trip, A leakage fault protection method based on first-order difference feature dynamic threshold detection and SWT-MCNN-BIGRU is proposed.First, the first-order differential features of the leakage signal are extracted and compared with a threshold to determine whether an electric shock fault has occurred. Next, the one-dimensional leakage signal is converted into a two-dimensional time-frequency image with high-frequency expression using Synchronous Wavelet Transform (SWT). A multi-scale convolutional neural network (MCNN) is then used to perform multi-scale feature extraction on the time-frequency image data, achieving further refinement of features at the spatial level. Finally, a bidirectional gated recurrent unit (BiGRU) is employed to capture the temporal relationship from both forward and backward direction, enabling more accurate identification of leakage signals in different states. The results show that the proposed model achieves a recognition accuracy of 100

PV power forecasting is essential for the stable and efficient operation of power systems. However, forecasting accuracy is compromised by two main factors: the quality of solar irradiance predictions and the limited flexibility of forecasting methods. In this paper, an ultra-short-term PV power forecasting method for the SAT PV system is proposed based on irradiance transformation and the CNN-BiGRU model. First, a SAT-based irradiance transformation is adopted to convert the horizontal-plane NWP irradiance data into the effective irradiance received by the SAT PV panels. Then, the FCM clustering algorithm is used to classify weather types, improving the adaptability of forecasting models to diverse meteorological conditions. Finally, a hybrid CNN-BiGRU network is developed to capture spatiotemporal features for accurate power prediction. The proposed method is validated using two-year operational data from a 50 MW PV plant in Northwest China and a 60 MW plant in Southeast China. A high degree of forecasting accuracy is observed. For the Northwest plant, the MSE values are 2.46, 4.53 and 3.64 MW2 under sunny, cloudy and rainy conditions, respectively; corresponding results for the Southeast plant are 2.62, 4.84 and 3.80 MW2. Consistent improvements are also observed in MAE, RMSE, R2 and FS metrics. Comparative tests confirm the superiority of the proposed model over state-of-the-art models, demonstrating that the integration of irradiance transformation and CNN-BiGRU model significantly enhances forecasting performance for SAT PV systems.

The emergence of multi-energy networks, comprising electricity and natural gas (NG), presents novel and complex challenges to the comprehensive analysis of energy systems. Energy systems based on electricity and gas adhere to distinct physical laws and mathematical representations. As attention and interest in integrated electricity and gas systems (IEGS) grow, expanding the scope of applying electrical analogies to pneumatic quantities is advantageous. The objectives of this paper are to implement the extension of this analogy and to conduct a multi-rate simulation of IEGS. It shows how the NG pipeline and gas compressor station (GCS) can be modeled using basic electric elements for the simulation of pneumatic transients. The primary objective of devising the multi-rate algorithm is to attain greater efficiency during the computational procedure. The target system is partitioned into an electrical network subsystem (ENS) and a gas network subsystem (GNS). Different time steps are adopted in the simulation of these subsystems. A novel interface model based on gas turbines is proposed to represent the interactions between ENS and GNS. A comparatively large time-step size is used in the GNS for accelerated computations. The multi-rate simulation algorithm is accompanied by validation and application to demonstrate its effectiveness in enabling efficient simulation of IEGS.

To enhance the flexibility and economic performance of integrated thermoelectric energy systems, this study develops an optimal operation strategy for supplementary electric heating in heat-exchange stations by explicitly exploiting the virtual thermal energy storage (VTS) potential of district heating networks (DHNs) and buildings. The VTS of the pipe network is modeled based on adjustable supply water temperature, while that of buildings is modeled based on allowable indoor temperature variation, enabling unified scheduling within the heat–electric coupling framework. A coordinated operation model is established for the combined heat and power (CHP) system with supplementary electric heating under time-of-use electricity pricing, aiming to minimize the total operating cost while satisfying thermal comfort and operational constraints. The model is expressed as a mixed-integer linear programming (MILP) problem and solved with the CPLEX optimizer. Case studies demonstrate that the proposed strategy achieves significant cost and carbon-emission reductions: compared with the scenario without VTS, the energy purchase cost decreases by ∼14%, and carbon-emission-related costs are also reduced. Further analysis reveals that the DHN provides a more substantial storage effect (reducing electricity purchases by 1.1%) than buildings (0.1%), highlighting the need to prioritize network heat storage in practical applications. These results confirm the effectiveness, economic advantages, and low-carbon potential of the proposed approach, providing a viable pathway for clean heating systems to reduce dependence on fossil fuels.

Accurate modeling of wind turbine units is key to analyzing the impact of large-scale wind power integration on grid stability, safety, and reliability. However, traditional mechanistic models often suffer from issues such as simplified assumptions and parameter characteristic deviations, leading to low model accuracy and inefficiency in electromagnetic transient simulation models. To address these issues, this paper proposes a mechanism-data-driven dynamic simulation model for wind power generation systems. Based on the transfer function mechanistic model, actual operational data from wind turbines are utilized to correct the parameters of the mechanistic model through a data-driven approach, thereby improving model accuracy. The effectiveness of the proposed model is ultimately validated through case analysis.

Electromagnetic Transients Programs (EMTP) have been widely employed for power system electromagnetic transient simulation. With deepening interdependencies between electrical systems and natural gas (NG) systems, cascading failure risks escalate significantly. It is beneficial to extend the application of the EMTP-type to multi-physical transients in integrated electricity and gas systems (IEGS). This study proposes a dynamic modeling method for NG systems based on circuit analogy. Through the analogy between electrical and pneumatic quantities, an equivalent circuit model of NG pipelines is established, which accounts for time-varying fluid resistance and has constant impedance characteristics. The modeling method is extended to fault scenarios, and equivalent circuit models for leakage and blockage faults are constructed to achieve accurate description of key parameters such as pressure and mass flow under fault conditions. The key technologies of energy conversion device integration in IEGS are systematically summarized, including the detailed dynamic models of the power-to-gas (P2G) and the gas turbine. A novel energy conversion device-based interface model is proposed to characterize the interactions between electrical and NG networks. Case studies are conducted to investigate the dynamic characteristics of IEGS under multiple scenarios and the effects of NG network failures on IEGS performance.

With the continuous advancement of demand response (DR) research, significant progress has been made in its application in conventional electricity usage scenarios. However, studies focusing on special areas, such as offshore fishing farms, are relatively scarce. This paper focuses on the electricity consumption characteristics of offshore fishing farms and proposes a method for calculating DR potential. A load classification framework tailored to the specific features of fishing farms is established, and a DR model is constructed to maximize the benefits of fishing farm participation in DR while maintaining electricity comfort. A method for calculating DR potential is designed for different load characteristics. The case study analysis results show that DR can achieve a total load reduction of 34.6% for offshore fishing farms, with electric boats (EBs), feed mills, and lighting equipment contributing 73.8%, 19.2%, and 7%, respectively, to the overall DR potential. The findings provide theoretical support for offshore fishing farms' participation in DR and have significant implications for improving their power supply reliability.

This paper formulates a multi-scale grid-connected model customized for simulating various transients in battery energy storage systems (BESSs). The proposed model is composed of three fundamental components: a primary system, a control system, and an interface model. Specifically, the electrical performance of pivotal components in the primary system, including the multi-scale voltage source converter (VSC) and filter, is characterized via analytic signals, whose Fourier spectra are capable of being shifted. Conversely, the battery model and control system are established on real signals. To achieve effective integration between the analytic signals of the primary system and the real signals of the control system, a high-fidelity interface is constructed. The model incorporates shift frequency as a newly defined parameter to enlarge the simulation time-step. Setting the shift frequency to zero allows the model to directly process instantaneous signals that represent natural waveforms; when assigned a value matching the primary system's characteristic frequency, the analytic signals are transformed into envelope form, enabling accurate envelope tracking. The shift frequency is adaptively determined according to the transient characteristics detected during simulation. Test cases verify the proposed multi-scale grid-connected model, showing its capacity to enable accurate and efficient simulation of both electromagnetic and electromechanical transients in a single simulation iteration.

The saturation model of the transformer is one of the core tools of multi-physics simulation. By combining it with multi-physics simulation, researchers can more comprehensively evaluate the performance of a transformer in actual applications. Geomagnetically induced currents (GIC) induce DC bias in transformers, leading to core saturation and a host of adverse effects. Traditional transformer models often struggle to accurately capture the behavior of the core under nonlinear saturation conditions. To address these challenges, a saturable transformer unified magnetic-equivalent (UMEC) model that directly takes the B-H magnetization curve to represent a transformer’s core nonlinear characteristics is proposed. The saturable transformer model is based on the model of a magnetic circuit of the transformer core. An estimation method to obtain a transformer’s essential parameters for saturation simulation is presented. GIC effects on transformer saturation are also studied through the proposed saturable transformer and estimation method.

The rural integrated energy systems are geographically dispersed, and different regions have varying natural resource endowments. To leverage the differences between the energy resources and energy demands of rural integrated energy systems in different regions and achieve complementary advantages, a strategy based on mixed-game theory for multi-region rural integrated energy systems' mutual support and operation is proposed. The strategy involves establishing a cluster alliance consisting of multiple rural integrated energy systems and a game model involving rural renewable energy operators. By employing mixed-game theory, the strategy strives to strike a balance between the interests of different entities. The operator aims to maximize profits, while the alliance aims to minimize costs. The approach models the problem using a combination of hierarchical and cooperative game modes. The operator provides the alliance with purchase and sale electricity prices, and the alliance optimizes transaction prices and exports transaction volume to the operator. The process iterates until a balance is achieved among the interests of all parties. The model is solved using differential evolution and the alternating direction method of multipliers (ADMM). To validate the model's rationality, reasonable multi-agent game models are established using three different types of rural regions. The case study results demonstrate that the proposed strategy can achieve energy complementarity among different rural regions, thereby enhancing the economic and environmental performance of the system.

In this paper, a novel synchronous machine model is developed for the accurate and efficient simulation of multi-scale transients. The machine stator equations are expressed with analytic signals in the phase domain, thus providing direct interface between machine model and external network model. Frequency shifting is applied to stator quantities to eliminate the ac carrier in the stator windings which enables the use of large time-step size. An artificial damper winding is introduced to eliminate the numerical saliency based on a pioneering technique. The proposed machine model is represented as a Norton equivalent with constant conductance matrix. The analysis of test cases demonstrates the effectiveness of the proposed synchronous machine model in terms of accuracy and efficiency.

The increasing uncertainties in power systems have brought non-negligible influences on the dynamic behaviors. An accurate and efficient simulation method considering the effects of stochastic disturbances is of critical importance for the analysis of the dynamic performance of power systems. In this paper, a methodology for the simulation of stochastic transients in power systems is developed based on frequency shifting theory. The stochastic differential equations describing the stochastic process of parameter migration are represented through analytic signals. The frequency shifting operation is introduced. The Fourier spectra of analytic signals are shifted to reduce their maximum frequency contents, thereby permitting a larger time-step in time-domain simulation in accordance with Shannon's sampling theory. Through the trapezoidal Milstein scheme, the stochastic differential equations with shifted analytic signals are discretized. Branch companion models that process analytic signals for the simulation of stochastic transients are formulated. The numerical properties of branch models are further examined. The analysis of test cases demonstrates that the proposed method can be used to represent stochastic transients caused by parameter migrations accurately and efficiently.

The use of the shift frequency as a simulation parameter has been widely acknowledged as an enabler for multi-scale modeling of electrical power systems. So far, the frequency shifting concept has been applied to modeling electrical components considering one shift frequency. This letter aims to complement the previous work and present a multiple-frequency shifting modeling methodology. The methodology is applied to the high-slip induction machine. Two major achievements are introduced: 1) Application of frequency shifting concept for components considering multi-carriers. The Fourier spectra of machine stator and rotor quantities are shifted by different shift frequencies. 2) Modeling of components with time-varying shift frequencies. The frequency shifting modeling methodology is so extended to a wider application area. Case studies are included to demonstrate the effectiveness of the proposed method and the developed machine model

Electromagnetic transients program (EMTP) is widely used to analyse transients in power systems. With the increasing interest in integrated energy systems (IESs), it would be beneficial to extend the application of EMTP to multi-physics transients in integrated electrical and heating networks. In this paper, an accurate and efficient lumped-element circuit model of the heating district pipe is developed in EMTP. The pipe is split into segments using spatial discretisation. The application of the numerical discretisation to the energy conservation equation gives the discretised pipe equation which is expressed in the form of a companion model of EMTP. In order to reduce the computational effort, the time-varying terms in the admittance matrix of the companion model of pipe are eliminated. The modification of the admittance matrix is avoided. Furthermore, the internal nodes resulting from spatial discretisation are eliminated, and the pipe model appears as a lumped-element circuit with two external nodes. Case studies are carried out to verify the accuracy and efficiency of the proposed lumped-element circuit model of pipe. The implementation of the proposed pipe model in the EMTP-type simulator enables the analysis of multi-physics transients in a multi-carrier energy system.

Accurate photovoltaic (PV) power forecasting is critical for the reliable and stable operation of the power grid. Most existing studies directly use weather conditions as data-driven inputs without incorporating PV physics models to improve the quality of input weather condition features. Therefore, a novel PV power forecasting method that combines physical models and data-driven approaches is proposed. First, a physical model of the PV system is established to convert solar irradiance data into the instantaneous observed irradiance received by the PV system. Then, this newly obtained irradiance data, along with other weather features, is used to reconstruct the data-driven inputs, achieving short-term power forecasting by combining physical models and data-driven approaches. Finally, simulation analysis is conducted using a real PV system dataset. The results show a significant improvement in forecasting accuracy, validating the effectiveness of the proposed method. The results indicate that this article combines physical mechanisms with data-driven approaches, fully considering the advantages and disadvantages of two commonly used models, greatly improving prediction accuracy.

One of the most remarkable features of power and energy systems pertains to their extremely far-reaching scales, which are unique in the field of engineering. In continental Europe, for example, the synchronous ac power system integrates capitals from Lisbon to Warsaw and from Athens to Copenhagen. Reaching farther out to the north, high-voltage dc (HVdc) links connect to Sweden and the United Kingdom. And as in other regions of the world, those grids are being further developed to integrate more renewables. This only adds to the diversity of technologies in the power grids and contributes to the wide range of timescales involved. Those range from electrothermal interactions in the range of minutes over electromechanical transients within seconds, down to microseconds or even faster for the electromagnetic transients of traveling waves.

Loss Allocation is a crucial task in power transmission and distribution services, as it essential for reconciliation of generator-user settlement results. With the availability of distributed generation in recent years, power can flow in both directions along transformers and mainlines in the traditional distribution networks. This makes it difficult to calculate power losses in active distribution networks and correct transactions between generators and users. In this paper, we propose the concept of Virtual Contribution Theory and construct a virtual contribution matrix to illustrating how generators and loads use and occupy distribution networks. By combining Virtual Contribution Theory with Power Flow Tracing, a bidirectional loss allocation method based on the Virtual Contribution Theory is proposed to account for power losses in active distribution network. One key advantage of this method is that it eliminates many shortcomings of traditional loss allocation method, including cumbersome calculation processes, low solution efficiency, and limitations in bidirectional power flow calculations. This method can also fairly allocate power loss between generators and loads in the retail electricity market. We conducted tests on IEEE 33 and IEEE 69 systems to evaluate the feasibility and effectiveness of the proposed method.