Abstract:Cyber threat indicators (CTIs) refer to the information necessary to describe or identify cybersecurity threats in cyberspace. Effective CTIs that represent and depict attack behaviors are the foundation for ensuring cybersecurity. Compared with general information systems, the intensity and capability level of attacks that substation monitoring and control systems need to address exhibit significant differences. Organized attacks carried out by individuals with professional knowledge can infiltrate production control areas through supply chain attacks, bypass identity and access management restrictions, and may not necessarily trigger security alerts. Therefore, using CTIs designed for general information systems is inadequate for accurately detecting highly concealed cyber attacks specifically targeted at substation monitoring and control systems. To address this, the traditional CTIs of general information systems are first summarized, and then the existing CTIs designed in conjunction with the characteristics of substation monitoring and control systems are analyzed. Based on this, in response to the challenge of detecting highly concealed security threats, the design and extraction of substation-based CTIs focusing on compliance are anticipated, considering aspects such as the execution of tasks by various business systems in the substation monitoring and control system according to established process rules, and the strong coupling between the primary system status and the communication and alerting of the secondary system. This approach is expected to accurately characterize highly concealed security threats that do not trigger alerts but violate business rules, laying the groundwork for further enhancing security protection capabilities.

Abstract:This paper takes the multi-regional interconnected power system as the research object, and studies the detection and defense of multi-region interconnected power system under hybrid attack. Firstly, a mathematical model of multi-regional interconnected power system is established, the location and type of network attacks suffered by multi-regional interconnected power system are analyzed, and false data injection attacks and denial of service attack models are established. Secondly, diagnose denial of service attacks based on whether packets are received, and the recently received packet compensates for the lost packet, the defense of denial-of-service attack is realized. Then, based on the cubature Kalman filter algorithm, false data injection attacks are detected, and exponential smoothing is used to defend against false data injection attacks. Finally, taking the two-region interconnected power system as an example, the simulation experiment shows that the designed control algorithm can effectively overcome the adverse effects of hybrid attacks on the system, and realize the power balance and frequency stability of the power system.

Abstract:Power security is a crucial guarantee for the sustainable development of modern society. With the gradual development of information technology, the increasing number and strength of cyber attack methods can cause severe damage to new power systems. Reasonable network topology and effective network defense resources are key to load recovery after a power system suffers a cyber attack. Therefore, a strategy for resilient topology optimization and defense resource allocation of the cyber-physical system (CPS) of distribution networks under FDIA-Worm hybrid attacks is proposed to enhance the resilience of distribution systems against cyber attacks. This model adopts a three-tier framework of upper, middle, and lower levels to optimize the topology and defense resources: the upper level establishes a multi-objective Pareto planning model with planning costs and load loss risks as objectives, combines it with the middle-level network attack propagation model that considers attacks and recovery, and uses the non-dominated sorting genetic algorithm II (NSGA-II) to solve the planning scheme; the lower level considers various coupling schemes between the information layer and the physical layer, and evaluates the optimal topology configuration based on resilience metrics of the CPS of distribution networks. Compared with traditional one-to-one series mode schemes, the optimized network topology and defense resource schemes under the three coupling relationships obtained through model solution can play a significant role in enhancing system resilience.

Abstract:To further improve the accuracy of transmission line icing prediction, a prediction model based on an improved Harris hawks optimization(IHHO) algorithm optimizing hybrid kernel extreme learning machine(HKELM) is proposed. The hybrid kernel function is introduced into the kernel extreme learning machine to form HKELM. The IHHO algorithm is improved by strategies such as golden sine, nonlinear decreasing inertia weight, and Gaussian random walk. The IHHO algorithm is then utilized to optimize the weight vector and kernel parameters of HKELM, establishing a transmission line icing prediction model based on IHHO-HKELM. The input variables of the icing prediction model are determined by calculating the grey relational grade between meteorological factors and icing thickness. The results of case studies show that the mean square error, maximum error, and average relative error of the IHHO-HKELM model are 0.285, 0.860 mm, and 2.83%, respectively. The prediction effect is better than other models. Applying the icing prediction model in this paper to other icing lines can achieve good application effects and verify the superiority and practicality of the model.

Abstract:When an AC system operates asymmetrically, unreasonable control parameter values at the converter station can affect the stable operation of the flexible DC transmission system. Therefore, a small-signal model of the converter station with differential flatness control under asymmetric conditions is proposed. Simultaneously, combining the internal characteristics of the modular multilevel converter (MMC) with the dynamic models of various subsystems such as the circulating current suppressor and the DC line, a global small-signal model for the two-terminal flexible DC transmission system is established. Based on this, the impact of the control parameters on system stability is analyzed by the changes in the root locus of the inner and outer loops of the differential flatness controller， and determine their reasonable value ranges. Finally, a two-terminal flexible DC transmission test system with differential flatness control in the PSCAD/EMTDC is built to verify the correctness of the proposed small-signal model and conclusions, providing a theoretical basis for controller parameter tuning in practical engineering.

Abstract:As the AC grid at the receiving end of high-voltage direct current (HVDC) transmission systems becomes increasingly complex, the control characteristics of traditional voltage-dependent current order limiter (VDCOL) units configured at inverter stations have become difficult to adapt to the regulation of modern power systems. To address this issue, an optimization method for the VDCOL control segment of HVDC based on simulated annealing algorithm is proposed. Firstly, based on the steady-state model of the inverter station of the HVDC transmission system and the response characteristics of the conventional VDCOL control segment, the power interaction characteristics between the inverter station and the AC system at the receiving end are analyzed, and the relationship between the DC current command value and the reactive power consumption of the inverter station is derived. Secondly, a typical fault set for HVDC transmission systems is designed, and an optimization scheme for the multi-inflection point parameters of the U-I characteristic curve of the VDCOL control segment based on simulated annealing algorithm is proposed. Finally, the improved U-I characteristic curve of the VDCOL control segment is obtained using a joint simulation method combining MATLAB and PSCAD/EMTDC. Through comparative simulation analysis with the conventional VDCOL control segment, it can be seen that the proposed method better meets the actual demand for system transient reactive power and can effectively suppress the occurrence of continuous commutation failures in the HVDC transmission system.

Abstract:With the rapid development of electrified railways, the traction load of electrified railways has become the largest single load in China's power system, but the impact of its load characteristics on the small-signal stability of the power system is still unclear. Therefore, an equivalent mathematical model of the traction load of electrified railways is established, and based on this, a small-signal model of the power system considering the access of traction loads is constructed. Secondly, using eigenvalue analysis methods, the impacts of locomotive speed, locomotive quantity, and the access location and proportion of traction loads on the small-signal stability of the power system are analyzed. Finally, the adjustment range of excitation parameters of synchronous generators is determined through eigenvalue root locus, and the impacts of traction loads before and after access and different traction load parameters on the adjustment range of excitation parameters of the power system are analyzed. The research results indicate that after the access of traction loads, the small-signal stability of the power system decreases, the adjustment range of system excitation parameters shrinks, and the system stability margin reduces.

Abstract:Dissolved gas analysis (DGA) is significant for early warning and diagnosis of transformer faults. To enhance the accuracy and reliability of transformer fault diagnosis, a transformer fault diagnosis method is proposed based on the tree-structured parzen estimator (TPE) algorithm to optimize the light gradient boosting machine (LightGBM). Firstly, a 16-dimensional DGA feature set including gas ratios and encodings in oil is established, and the least absolute shrinkage and selection operator (LASSO) algorithm is used to select effective feature quantities for transformer fault diagnosis. Secondly, a transformer fault diagnosis method based on LightGBM is constructed, and the TPE algorithm is introduced to optimize the parameters of the LightGBM diagnosis model, forming an optimal fault diagnosis model. Finally, evaluation metrics such as accuracy, recall, and F1 score are selected to assess the performance of the proposed diagnosis model. The research results indicate that the average accuracy of TPE-LightGBM is 90.23%, and its diagnostic accuracy and robustness are superior to algorithms such as RF and XGBoost. At the same time, compared with the commonly used three-ratio method in practice, the proposed method shows significantly improved accuracy and reliability. This method can effectively enhance the level of intelligent operation and maintenance of power transformers.

Abstract:Protection relay system is one of the main defense lines to ensure the stable operation of high-voltage networks. However, within the scenarios with the more complex network topology, the line architecture and the distribution, it is difficult to eliminate the potential operating anomalies or even failures. Also, the diversification of the protection equipment types, functions and locations poses new challenges to the defect management and equipment maintenance. Therefore, the automatic early warning technology of equipment abnormal state risk which considers both the timeliness and comprehensiveness should be studied. To this end, a real-time detection model of abnormal state risk based on data mining is proposed in this paper. Firstly, the independent component analysis is used for mass heterogeneous monitoring data to implement noise reduction. This can effectively improve the computational efficiency under high-dimensional data conditions. Secondly, the feed-forward neural network deep learning method which deploys the end-to-end training process to achieve time series anomaly detections is utilized. This can effectively alleviate the multi-category timing conditions of computational complexity. Finally, the protection system equipment in one area is exploited as empirical study, the results verify the abnormal detection performance of the designed model, which can promote the automatic identification and timely response of the protection relay system.

Abstract:Influenced by climate change, heavy rainfall and waterlogging have shown an increasing trend in recent years. The distribution system with a small power supply radius and insufficient flexibility in location selection is prone to large-scale power outages caused by waterlogging. Analysis points out that due to the lack of detailed records of waterlogging levels, there is a benchmark missing problem in the planning and construction of distribution systems in terms of waterlogging protection. Therefore, referring to disaster prevention methods such as ice disasters in power systems, and based on the analysis of storm inundation using a two-dimensional hydrodynamic model, this study proposes a method for delineating and differentially planning power outage risk areas in distribution systems due to rainstorm and waterlogging, considering the impact of micro-topography. Firstly, extreme rainfall for different return periods is estimated based on the extreme value distribution model, and then combined with geographic information for two-dimensional hydrodynamic simulation to obtain inundation maps, including inundation scope and depth, for extreme rainfall with a 50-year return period. Secondly, considering the construction standards of distribution terminals such as distribution transformers and switch stations, power outage risks corresponding to different inundation depths are set, and then a risk level map of power outages in the distribution system due to rainstorm and waterlogging is drawn. This provides a basis for setting different foundation heights for distribution terminals in different risk areas, enhancing their resilience to rainstorm and flood disasters. Finally, the effectiveness of the proposed method is verified through simulation analysis based on data from Nanning.

Abstract:To improve the accuracy of fault location in the case of single-phase grounding faults in distribution networks, a method for fault line selection and location based on the voltage zero-mode traveling-wave S-transform time-frequency matrix is proposed. First, the traveling-wave signals measured by the traveling-wave detection devices at the ends of each feeder are decoupled to obtain the fault voltage zero-mode traveling wave. Then, faults are set at special locations such as the main feeder, primary and secondary branch lines to obtain the S-transform time-frequency matrix corresponding to the fault location, and a comparison library for determining the fault branch is established. Finally, the S-transform time-frequency matrix of the fault zero-mode voltage traveling wave is compared with the data in the comparison library for similarity to determine the feeder and branch where the single-phase grounding fault occurs. The optimal location method is selected for different types of branches to achieve precise location of single-phase grounding faults in distribution networks. Simulation results show that this method has high accuracy in fault location and can achieve precise location of single-phase grounding faults in distribution networks without the need for traveling-wave detection devices across the entire distribution network.

Abstract:Aiming at the problems of complex process and insufficient refinement of artificial feature selection in traditional power quality disturbances (PQDs) classifier, a new PQD recognition method based on Markov transition field visualization and improved DenseNet is proposed. Firstly, the one-dimensional PQD signal is mapped into a two-dimensional image by MTF. Then, the image is input into an improved DenseNet with a new channel attention mechanism. Finally, the network is trained to extract features from a large number of samples by itself, so as to realize the correct recognition of PQD signals. The example results show that: in the case of no noise and signal-to-noise ratio of 20dB and 30dB, the proposed improved DenseNet can effectively overcome the shortcomings of traditional methods, such as strong subjectivity of feature selection and poor anti-noise performance. It can better extract the feature information of complex PQD, and has a high recognition rate for complex PQD.

Abstract:As a new type of regional energy management system, the virtual power plant (VPP) can efficiently participate in the secondary frequency regulation auxiliary services of the power grid through the coordinated optimal scheduling of "source-load-storage". This paper introduces the internal structure of the VPP, and models and analyzes the characteristics of new energy units and controllable loads. A two-stage scheduling model for the VPP participating in secondary frequency regulation is established, which can balance the net profit and frequency regulation effect of secondary frequency regulation. An improved quantum particle swarm optimization (QPSO) algorithm with adaptive weights is studied. By introducing an adaptive weighting mechanism, the weight parameters are dynamically adjusted during the quantum particle update process to improve the search ability and convergence speed of the algorithm. The improved algorithm is applied to the two-stage optimization process, enabling the VPP to achieve higher net profits from secondary frequency regulation and better frequency regulation effects. Simulation results demonstrate that the proposed improved algorithm has a faster convergence speed and stronger global optimization ability.

Abstract:Focusing on enabling smart energy consumption for residential users, a smart energy strategy with incentives for residential users to participate in grid peak shaving is proposed. According to the load characteristics of household appliances, residential loads are classified into four categories, and corresponding mathematical models are established. Considering time constraints, electricity satisfaction constraints, and electricity bill satisfaction constraints, an optimization model for residential electricity consumption cost is established. Given that traditional time-of-use electricity pricing is inadequate in motivating residents to participate in grid peak shaving and valley filling, a peak shaving effect evaluation model is established based on cosine similarity, and a market incentive mechanism is proposed based on residents' contribution to peak shaving. Finally, the peak shaving effect is introduced into the Shapley value allocation to establish a cooperative game model between residential users and grid companies. Simulation results demonstrate that the proposed strategy can effectively reduce household electricity consumption costs while promoting active participation of residential users in grid peak shaving.

Abstract:In the context of demand response at electric vehicle charging stations, user participation has a significant impact on the economic benefits of charging stations. Based on prospect theory, an optimization method for user participation at charging stations is proposed, aiming to improve user participation and economic benefits by altering the composition and format of electricity prices at charging stations. Initially, a user price impact model is established to analyze the influence of charging station prices on electric vehicle users, yielding preliminary user quantity change rates. Subsequently, the value function in prospect theory is used to quantify the decision uncertainty of users when faced with different electricity prices, and adjustments are made to the preliminary user change rates, considering the impact of charging station distance, to obtain the final user quantity changes. Finally, based on the aforementioned models and typical load data from charging stations, with the maximum load during demand response periods as a constraint, the non-dominated sorting genetic algorithm-II (NSGA-II) optimization algorithm is employed to conduct a multi-objective optimization aiming to maximize daily revenue and user participation at the charging station. This determines the composition and format of electricity prices at the charging station, further identifying the optimal user participation and charging station revenue. Simulation results verify the effectiveness of the proposed method.

Abstract:To incentivize electric vehicles (EVs) to participate in demand-side response to reduce the peak-to-valley difference in grid load and enhance the economic viability of EV electricity usage, the Monte Carlo method is used to simulate the unordered charging load of EVs. EVs are then categorized into three types based on whether they are regulated by the grid or guided by price signals. Subsequently, a dynamic time-of-use (TOU) pricing demand response model is established on the basis of the electricity price demand elasticity matrix, with the objectives of minimizing the mean square value of the grid's peak-to-valley difference and minimizing user charging and discharging costs. Using historical load data from a region in Hunan and segmenting the electricity prices for a day, simulation analysis is conducted to verify that dynamic TOU pricing considering real-time load feedback can effectively manage load fluctuations, and it has a more pronounced effect on reducing the peak-to-valley difference and lowering user electricity costs.

Abstract:In response to the "dual carbon" strategy, a new type of power system with a high proportion of renewable energy access has become the next development goal. As one of the main forms of current energy generation, photovoltaic (PV) power generation has characteristics such as multi-source, heterogeneous, and high-dimensional data distribution, which makes the mechanisms and effects of different features relatively complex and subsequently increases the difficulty of predicting the output of distributed PV systems. To address this, multiple categories of data mining models are integrated to construct an K-I-ELM prediction method for short-term PV output prediction in complex data environments. First, a kernel principal component analysis (KPCA) model is constructed to extract principal components based on the effective information contained in different features in the feature space through a kernel function. An information entropy (IE) model is employed to measure the weighting coefficients based on the information load of each principal component and comprehensively solve the corresponding effect weights. Finally, based on the feature evaluation results, the network parameters of the extreme learning machine (ELM) are set specifically to reduce prediction uncertainty. A case study based on actual PV power generation data from a certain substation demonstrates the adaptability and high prediction accuracy of the proposed method in different data environments.

Abstract:In response to China's "dual carbon" goals, the proportion of new energy sources, represented by wind power, in the power output for power grids continues to increase. Effective wind turbine output prediction is particularly important for formulating grid scheduling and power generation plans ahead of time. Due to the strong irregularity and seasonality of wind power data, a single model prediction method cannot solve the problem of wind power intermittency while ensuring prediction accuracy. To address this, a combined model using the autoregressive integrated moving average (ARIMA) time series, long- and short-term memory (LSTM) network, and radial basis function (RBF) neural network is proposed for short-term prediction of wind turbine output in a certain region. First, data preprocessing and sequence stationarity analysis are performed to obtain a stationary sequence and predict it through ARIMA. Secondly, irregular data that do not meet the criteria of residual white noise analysis are predicted through LSTM. Then, the RBF neural network is used to learn and simulate the predicted values to improve accuracy. Finally, simulations are conducted based on data from a wind power station. Compared with other single model prediction methods, the results show that the proposed combined model prediction method can predict wind power data with strong seasonality and irregularity and has better prediction accuracy, providing a reference for the operation and scheduling of corresponding equipment and enhancing power supply reliability.

Abstract:Ice formation on wind turbine blades poses dual challenges to the operational safety and power generation efficiency of wind farms, making it urgent to de-ice wind turbines with severe icing. Air thermal deicing is an active anti-icing technology for blades, where hot air transfers heat from the inner surface to the outer surface of the blade through a combination of conduction and convection, melting the overlying ice layer. From the perspective of the heat transfer process alone, the processes of convective and conductive heat transfer in air thermal deicing are not particularly complex and can be studied through two methods: systematic experimentation and numerical simulation, to investigate their flow and heat transfer characteristics. However, the conditions required for experimentation are quite demanding, and the experimental costs are relatively high. To address this issue, a coupled flow and heat transfer model for both the inner and outer sides of the turbine blade is established based on technologies such as the k-ε turbulence model, velocity-pressure coupling algorithm, and wall function. This model analyzes the effectiveness of air thermal deicing under the combined effects of conduction and convection, avoiding the separated defects of traditional numerical models that only consider unilateral flow and heat transfer. It can accurately obtain the velocity field, temperature field, pressure field inside the blade cavity, as well as the temperature distribution on the outer wall of the blade under specific operating conditions, providing technical guidance for the design and operational control of a reasonable deicing system. The research results indicate that under different air supply velocities, the temperature distribution on the blade surface shows a trend of being higher at both ends and lower in the middle, and as the air velocity increases, the temperature imbalance phenomenon is significantly improved. When the air supply velocity is less than 15 m/s, the surface temperature of most areas of the blade is below 0 ℃, but when the air supply velocity increases to 20 m/s, the area with a surface temperature below 0 ℃ is significantly reduced.

Abstract:To support the rapid switching of unit control modes in grid-following/grid-forming hybrid new energy stations and achieve safe and stable operation of these stations that can adapt to changes in grid strength, a rapid assessment method for small-signal stability of grid-connected systems in grid-following/grid-forming hybrid new energy stations based on temporal convolutional network (TCN) is proposed. Specifically, an aggregated impedance model for grid-following/grid-forming hybrid new energy stations is constructed, and the small-signal stability margin of the grid-connected system is obtained through eigenvalue calculations. Furthermore, using the short-circuit ratio of the grid-connected system and the information on the grid-following/grid-forming control mode of the new energy station as input features, and the small-signal stability margin and damping ratio of the grid-connected system as output features, a TCN is trained to obtain a rapid assessment model for small-signal stability of grid-connected systems in hybrid new energy stations. The trained model can quickly output the corresponding small-signal stability margin and damping ratio based on the short-circuit ratio and the control mode of each unit in the grid-following/grid-forming hybrid new energy station. A case study is conducted using a new energy station with 10 wind turbines, and the results show that compared to the long short-term memory neural network method, the proposed method reduces the mean absolute percentage error of small-signal stability margin prediction and damping ratio prediction by 16.76% and 14.75% respectively. Additionally, the computation time of the proposed method is reduced by 98.54% compared to the eigenvalue calculation method, verifying the accuracy and efficiency of the proposed rapid assessment method for small-signal stability.

Abstract:Accurate estimation of the state of charge (SOC) of lithium-ion batteries relies on precise model parameters. When using the forgetting factor recursive least square (FFRLS) algorithm for parameter identification of the equivalent circuit model of lithium-ion batteries, improper selection of initial iterative values can lead to low identification accuracy and slow convergence speed. To address this issue, circuit analysis is combined with the FFRLS algorithm, and then an improved initial value-FFRLS (IIV-FFRLS) algorithm is proposed. Firstly, offline identification is performed to obtain the equivalent circuit model parameters corresponding to various SOC points, which are then fitted using a polynomial function. Secondly, the initial SOC is obtained using the initial open circuit voltage (OCV) and the OCV-SOC curve, which is then substituted into the parameter fitting function to obtain the initial parameters. Finally, these initial parameters are used in the recursive formula to obtain the initial iterative values for the IIV-FFRLS algorithm. Parameter identification is performed for four operating conditions of lithium-ion batteries, and the results show that compared with traditional methods, the IIV-FFRLS algorithm reduces the average relative error by more than 58% and the convergence time by more than 23%. The IIV-FFRLS algorithm exhibits higher identification accuracy and faster convergence speed.

Abstract:Addressing the uncertainties of renewable energy and load within isolated microgrids, a robust economic optimization approach for microgrids is proposed based on scenario probability distribution uncertainty and probabilistic combined scenario performance. The K-means clustering method is employed to preprocess extensive historical data, constructing a fuzzy set of data-driven scenario probability distributions. In the day-ahead planning phase, the binary expansion concept is introduced to discretize the probabilistic combination coefficients in continuous variable form, simplifying and effectively parameterizing the intensity and search interval of the worst-case scenario search. This extends the search range of the worst-case scenario effectively from the boundary of the uncertainty set to its interior, enabling the search for the worst probabilistic combined scenario. By optimizing the performance of the worst probabilistic combined scenario, the day-ahead optimal solution for microgrid operation is calculated. Subsequently, in the real-time scheduling phase, real-time measurement data of renewable energy and load are utilized to perform secondary optimization adjustments on part of the day-ahead planning optimization solutions, enhancing the economic efficiency and robustness of the microgrid control scheme. Simulation examples demonstrate the effectiveness of the proposed method.

Abstract:As an important part of energy transformation, integrated energy system (IES) has attracted widespread attention from more and more countries. Establishing an evaluation system and method for IES that matches China's national conditions can not only lay a foundation for post-evaluation of IES planning and rank the planning schemes accordingly, but also improve the management level of IES projects and provide a reference for formulating a unified and complete IES evaluation criterion. To this end, combining the basic characteristics and operational features of the park IES, a comprehensive evaluation index system including four aspects of economy, reliability, environmental protection, and intelligence and friendliness is constructed. Then, to address the uncertainty of IES operation, the comprehensive evaluation system based on the traditional cloud matter-element model proposes cloud entropy optimization, considering the varying degrees of acceptability of fuzziness among different evaluators. To solve the problem that the evaluation results caused by a single weighting method may be too subjective or too objective, a comprehensive weighting method combining the decision making trial and evaluation laboratory method with the entropy weight method based on the principle of minimum discrimination information is selected, and the variable weight method is used to further improve the value of comprehensive evaluation index. Finally, the scientific correctness of the proposed comprehensive evaluation system is verified through case analysis.

Abstract:A regional integrated energy system (RIES) incorporates various energy sources such as wind and solar power, and cooling, heating and electrical loads, as well as batteries, offering advantages like enhancing the utilization of renewable energy. Firstly, considering the uncertainty of wind and solar power output, a robust optimization model with a polytopic uncertainty set is constructed to handle this uncertainty. Secondly, a multi-objective optimization model is established to minimize carbon emissions and operating costs, and a carbon emission penalty factor is introduced to convert the multi-objective into a single-objective for solution. Finally, simulations are conducted using an actual RIES, and the results demonstrate the accuracy and effectiveness of the proposed method. The model effectively balances the environmental protection and economic aspects of the system, better handles uncertainties, and achieves economic optimization of system operation.

Abstract:The rise of electric vehicles has increased the electrical load in airport service areas. Therefore, by utilizing the development of photovoltaic and energy storage around the airport, combined with the charging characteristics of electric vehicles during parking, a two-layer optimization model for electric vehicles to participate in the price-based demand response and the capacity configuration of photovoltaic and energy storage in service areas is established. The upper model is optimized for the capacity configuration of photovoltaic and energy storage equipment, with the goal of minimizing the configuration cost of photovoltaic and energy storage; the lower model proposes a power optimization management strategy for service areas that considers time-of-use tariff and different charging demands during electric vehicle parking. Simultaneously, a load stochastic model of electric vehicles and a price-based demand response model are established, with the goal of maximizing the benefits of charging, photovoltaic and energy storage , a typical daily optimization control model is established, and the load curve of electric vehicles and the control of energy storage in service areas are optimized. In the simulation, the randomness of photovoltaic output and charging load is considered, and the Monte Carlo method is used to eliminate its impact on the results, and the impact of demand response uncertainty on the optimization results is analyzed. The results show that optimizing the configuration of photovoltaic and energy storage systems considering time-of-use tariff and the charging benefits of electric vehicles can save one-time investment costs, and utilizing the time-of-use electricity pricing policies for charging and the optimization control of photovoltaic and energy storage can achieve better economic benefits. Therefore, reasonable system configuration, site utilization, charging management, and photovoltaic and energy storage control are effective ways to improve energy utilization and economic benefits.

Abstract:The phase-locked loop (PLL) is a core component for grid synchronization of power converters. Existing technologies commonly employ the PLL based on the synchronous reference frame (SRF-PLL). The introduction of SRF-PLL complicates the model of grid-connected converters and makes stability analysis challenging. Typically, numerical verification analysis is conducted using Bode plots, making it difficult to obtain analytical solutions and effectively reveal the underlying mechanisms of stability. Therefore, an algebraic operation-based PLL (AO-PLL) control technology is proposed, which offers faster synchronization and reduces the order of the converter model under this PLL control mode. Based on this, a closed-form analytical solution for system stability is presented, and necessary and sufficient conditions for system stability are obtained. On this basis, the impact of active and reactive power control coupling on system stability is analyzed; the different mechanisms of the proportional controller and integral controller of the current loop on system stability are revealed; and the connotation of using short-circuit ratio to describe the system stability margin is explained. Furthermore, the intrinsic relationship between the proposed AO-PLL and the conventional SRF-PLL is elaborated, as well as the applicability of stability conclusions under conventional SRF-PLL control. Finally, numerical examples are provided to verify the correctness of the aforementioned theoretical analysis.

Abstract:Power amplifiers based on power electronic converters have become one of the ideal physical interfaces for grid voltage simulation due to their flexibility and ease of control. Addressing the drawbacks of existing power amplifier control methods, which can amplify frequency deviations, cause filter circuit parameter drifts, and make it difficult to simulate high-frequency harmonics, an ultra-wideband control strategy for harmonic power amplifiers with mixed feed-in adaptive compensation is proposed. This strategy aims to accurately reproduce wideband voltage signals while balancing dynamic and steady-state performance. Next, a multivariable mixed feed-in loop is designed to compensate for the transient characteristics of the system. In the time domain, an adaptive method considering parameter drifts is derived using the Lyapunov function to compensate for uncertainties in filter parameters. The limitations of digital implementation in mixed adaptive compensation are thoroughly analyzed and overcome. The performance enhancement of the proposed method as a repeated embedded control is discussed. Finally, a prototype is built, and simulations and experiments are conducted to verify the dynamic performance and steady-state accuracy of the proposed strategy. The results indicate that compared to existing controls, the proposed method offers faster dynamic response, higher steady-state accuracy, and a wider range of harmonic frequency tracking. It can achieve rapid and error-free tracking of ultra-wideband reference voltages.

Abstract:The deformation of windings caused by short circuits is one of the key factors leading to transformer failures. To explore the elastic-plastic deformation law of transformer windings under multiple short-circuit impacts, a SFZ7-31500/110 power transformer is taken as the research object to construct a three-dimensional finite element simulation model of the transformer. Through magnetic field-structure field coupling calculation, the leakage magnetic field distribution of the transformer and the magnitude of the electromotive force on the windings during short circuits are obtained. The deformation characteristics and cumulative effects of windings under multiple short-circuit impacts are systematically analyzed. The results show that under short-circuit impacts, the deformation directions of high- and low-voltage windings are opposite, and the deformation of low-voltage windings is significantly greater than that of high-voltage windings. In addition, due to the influence of the iron core structure, the force and deformation of the same winding located inside and outside the iron core window show significant inhomogeneity, and the deformation of the winding inside the window is larger. As the number of short circuits increased, the plastic strain on the low-voltage windings gradually accumulate. It grows rapidly in the early stage, but the deformation accumulation speed gradually slows down and tends to stabilize due to the material hardening effect. On the contrary, the high-voltage windings maintain elastic deformation throughout the process and does not show plastic deformation. The research results have certain guiding significance for the analysis of the dynamic stability of transformer windings.