The increasing complexity of modern power systems,driven by factors such as the large-scale integration of renewable energy and the proliferation of distributed generation,has placed unprecedented demands on power dis...The increasing complexity of modern power systems,driven by factors such as the large-scale integration of renewable energy and the proliferation of distributed generation,has placed unprecedented demands on power dispatching operations.Ensuring grid stability and safety in this new environment requires real-time monitoring and swift,data-driven decision-making.Consequently,efficient and accurate data querying capabilities have become paramount.This study introduces Intelli-Dispatch-SQL,a novel agent-based Text-to-SQL framework that leverages the Large Language Model(LLM)to enhance the accuracy and reliability of generated SQL queries in the context of power dispatching.By integrating intent recognition and SQL validation modules,Intelli-Dispatch-SQL ensures that generated queries are not only syntactically correct but also semantically aligned with user intent and executable within the operational context.Through comprehensive experiments,including ablation studies and cross-model evaluations,we demonstrate that Intelli-Dispatch-SQL significantly outperforms existing Text-to-SQL models,achieving substantial improvements in both Exact Match(EM)and Execution Accuracy(EX).Notably,the incorporation of intent recognition and SQL validation modules is shown to be critical for performance enhancement.The framework’s effectiveness was further validated across various LLMs,confirming its robustness and applicability across diverse scenarios.Intelli-Dispatch-SQL offers a performance high-and generalizable solution for Text-to-SQL in power dispatching,paving the way for more efficient and intelligent power system management.展开更多
With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distributi...With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service restoration.In this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS.Additionally,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic characteristics.The objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution complexity.As a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming problem.The effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node GS.The comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.展开更多
The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-domin...The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.展开更多
This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditiona...This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.展开更多
The penetration rate of new wind and photovoltaic energy in the power system has increased significantly,and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional...The penetration rate of new wind and photovoltaic energy in the power system has increased significantly,and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional grid.This paper proposes a hierarchical optimal dispatch strategy for a high proportion of new energy power systems that considers the balanced response of grid flexibility.Firstly,various flexibility resource regulation capabilities on the source-load side are analyzed,and then flexibility demand and flexibility response are matched,and flexibility demand response assessment is proposed;then,a hierarchical optimal dispatch model of the grid taking flexibility adjustment capability into account is established,and the upper model optimizes the net load curve with the objectives of minimizing the fluctuation of the net load,maximizing the benefits of energy storage and controllable loads,and optimizing the flexibility adjustment capability.The upper layer model optimizes the net load curve by minimizing net load fluctuation,maximizing energy storage and controllable load revenue,and optimizing flexibility adjustment capability.In contrast,the lower layer model optimizes the power allocation of thermal power units and regulates the lost load of wind and solar power generation by minimizing the total system operating cost.The results show that the proposed strategy improves the flexibility of the grid by 15.2%,gives full play to the regulation capability of each flexibility resource,and reduces the fluctuation of the net load by 15.6%to achieve optimal coordination between different types of flexibility resources.展开更多
Photovoltaic(PV)power generation is undergoing significant growth and serves as a key driver of the global energy transition.However,its intermittent nature,which fluctuates with weather conditions,has raised concerns...Photovoltaic(PV)power generation is undergoing significant growth and serves as a key driver of the global energy transition.However,its intermittent nature,which fluctuates with weather conditions,has raised concerns about grid stability.Accurate PV power prediction has been demonstrated as crucial for power system operation and scheduling,enabling power slope control,fluctuation mitigation,grid stability enhancement,and reliable data support for secure grid operation.However,existing prediction models primarily target centralized PV plants,largely neglecting the spatiotemporal coupling dynamics and output uncertainties inherent to distributed PV systems.This study proposes a novel Spatio-Temporal Graph Neural Network(STGNN)architecture for distributed PV power generation prediction,designed to enhance distributed photovoltaic(PV)power generation forecasting accuracy and support regional grid scheduling.This approach models each PV power plant as a node in an undirected graph,with edges representing correlations between plants to capture spatial dependencies.The model comprises multiple Sparse Attention-based Adaptive Spatio-Temporal(SAAST)blocks.The SAAST blocks include sparse temporal attention,sparse spatial attention,an adaptive Graph Convolutional Network(GCN),and a temporal convolution network(TCN).These components eliminate weak temporal and spatial correlations,better represent dynamic spatial dependencies,and further enhance prediction accuracy.Finally,multi-dimensional comparative experiments between the STGNN and other models on the DKASC PV dataset demonstrate its superior performance in terms of accuracy and goodness-of-fit for distributed PV power generation prediction.展开更多
To enable distributed PV to adapt to variations in power grid strength and achieve stable grid connection while enhancing operational flexibility,it is essential to configure grid-connected inverters with an integrate...To enable distributed PV to adapt to variations in power grid strength and achieve stable grid connection while enhancing operational flexibility,it is essential to configure grid-connected inverters with an integrated grid-following control mode,allowing smooth switching between GFL and GFM modes.First,impedance models of GFL and GFM PV energy storage VSG systems were established,and grid stability was analyzed.Second,an online impedance identification method based on voltage fluctuation data screening was proposed to enhance the accuracy of impedance identification.Additionally,a PV energy storage GFM/GFL VSG smooth switching method based on current inner loop compensation was introduced to achieve stable grid-connected operation of distributed photovoltaics under changes in strong and weak power grids.Finally,a grid stability analysis was conducted on the multi-machine parallel PV ESS,and a simulation model of a multi-machine parallel PV ESS based on current inner loop compensation was established for testing.Results showed that,compared to using a single GFM or single GFL control for the PV VSG system,the smooth switching method of multi-machine parallel PV ESS effectively suppresses system resonance under variations in power grid strength,enabling adaptive and stable grid-connected operations of distributed PV.展开更多
The dynamics of network power response play a crucial role in system stability.However,the integration of power electronic equipment leads to amplitude and angular frequency(abbreviated as"frequency")time-va...The dynamics of network power response play a crucial role in system stability.However,the integration of power electronic equipment leads to amplitude and angular frequency(abbreviated as"frequency")time-varying characteristics of the node voltage during dynamic processes.As a result,traditional calcu-lation methods for and characteristics of the power response of the network based on phasor and impe-dance lose their validity.Therefore,this paper undertakes mathematical calculations to reveal the power response of a network under excitation by voltage with time-varying amplitude and frequency(TVAF),relying on the original mathematical relationships and superimposed step response.Then,the multi-timescale characteristics of both the active and reactive power of the network are explored physically.Additionally,this paper reveals a new phenomenon of storing and releasing the active and reactive power of the network.To meet practical engineering requirements,a simplified power expression is presented.Finally,the theoretical analysis is validated through time-domain simulations.展开更多
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr...Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.展开更多
This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and und...This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.展开更多
The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information ...The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.展开更多
With a lack of coverage in private and public power communication networks,especially for collection of information from hydropower stations in remote areas,communication coverage is a significant issue.Satellite comm...With a lack of coverage in private and public power communication networks,especially for collection of information from hydropower stations in remote areas,communication coverage is a significant issue.Satellite communication provides a large coverage area suitable for a variety of services and is less affected by geographical factors;moreover,the costs are independent of the communication distance.This study investigates information acquisition technology for small hydropower stations in remote areas using high-and low-orbit satellites.The information collection needs of small hydropower stations in remote areas are analyzed,and an information acquisition system is designed using high-and low-orbit satellites.For network security protection,network anomaly detection technology based on a support vector machine algorithm is proposed.The effectiveness of information collection was evaluated and verified for small hydropower plants in remote areas.The system provides technical support for“full coverage,full collection,and full monitoring”of the measurement automation information acquisition system.展开更多
The BeiDou-3 navigation satellite system(BDS-3)provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation.However,BDS-3 power system positioning faces certain ch...The BeiDou-3 navigation satellite system(BDS-3)provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation.However,BDS-3 power system positioning faces certain challenges,such as complex electromagnetic interference and incomplete error elimination.Herein,a back propagation neural network-improved least mean square(BP-LMS)adaptive filtering method is proposed for the BDS-3 full-domain and high-precision power system positioning,which utilizes the loss function to update the weight of the BP hidden layer,computes the pseudo compensation range,and eliminates the impact of electromagnetic interference to enhance the accuracy of power system positioning.Simulation results confirm the superior performance of BP-LMS in positioning accuracy and error elimination.Compared with LMS and normalized least mean square(NLMS),the filtering error of the proposed BP-LMS adaptive filtering method is decreased by 57.14%and 51.38%,respectively.展开更多
Due to the variable output of renewable energy (RE) generation, difficulties of dispatching RE for power system operators could not be avoided. One of possible solutions is the energy storage technology, especially th...Due to the variable output of renewable energy (RE) generation, difficulties of dispatching RE for power system operators could not be avoided. One of possible solutions is the energy storage technology, especially the battery storage system. The large-scale energy storage system is available to support power system reliable flexibility for load following and system frequency regulation. In this paper, the bottlenecks of large-scale solar power generation dispatching and operation in Qinghai grid are discussed, and a new PV-energy storage coordinated dispatching method is proposed for reduction of PV curtailment in Qinghai. Moreover, the validation based on the time-series production simulation is provided using real data from Qinghai. The results indicate that the proposed method can effectively decrease the curtailment of solar power and future vision of large-scale solar power coordinated operation with energy storage system is also presented.展开更多
By using electric power data,observational station temperature data in Beijing,CN05.1 temperature data,ERA5 atmospheric reanalysis data,and ERSST.v3 b sea surface temperature(SST) data,it is found that summer(JulyAugu...By using electric power data,observational station temperature data in Beijing,CN05.1 temperature data,ERA5 atmospheric reanalysis data,and ERSST.v3 b sea surface temperature(SST) data,it is found that summer(JulyAugust) electric power demand in Beijing is remarkably positively correlated with the previous spring(MarchApril) tropical North Atlantic(TNA) SST anomaly(SSTA).The possible physical mechanism of the TNA SSTA affecting summer electric power in Beijing is also revealed.When a positive SSTA occurs in the TNA during spring,anomalous easterlies prevail over the tropical central Pacific,which can persist to the following summer.Trade winds are thus enhanced over the northern Pacific,which favors a strengthening of upwelling cold water in the tropical central-eastern Pacific.As a result,a negative SSTA appears in the central-eastern Pacific in summer,which means a La Nina event is triggered by the previous TNA SSTA through the Bjerknes feedback.During the La Nina event,an anomalous anticyclonic circulation occupies the northwestern Pacific.The southerly anomalies at the western edge of this anomalous anticyclone strengthen the transportation of warm and humid airflow from the low latitudes to North China,where Beijing is located,causing higher summer temperatures and increased electricity usage for air conditioning,and vice versa.The results of this study might provide a new scientific basis and dues for the seasonal prediction of summer electric power demand in Beijing.展开更多
Based on real-time digital simulations(RTDS),a laboratory environment similar to the real-time operation situation of the Three Gorges Hydropower Station is established.Then,the causes for the power fluctuation of the...Based on real-time digital simulations(RTDS),a laboratory environment similar to the real-time operation situation of the Three Gorges Hydropower Station is established.Then,the causes for the power fluctuation of the second generator by errors in the rotor rotating speed calculation are found,and the tuning method of the speed related parameters are given.The damping and reverse tuning characteristics of power system stabilizers(PSSs)in the digital automatic voltage regulator(AVR)are compared and investigated in the frequency range of 0.18-1.1 Hz.The efficiency of the proposed tuning method for ensuring power system stability is verified by RTDS.Finally,field tests show the validity of the laboratory test results.展开更多
The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial...The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.展开更多
Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reli...Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.展开更多
基金supported by the Guangdong Power Grid Com-pany(Grant Number:GDKJXM20231024)the National Natural Sci-ence Foundation of China(Grant Number:72331009,72171206 and 92270105)the Shenzhen Key Laboratory of Crowd Intelligence Em-powered Low-Carbon Energy Network(Grant number:ZDSYS20220606100601002).
文摘The increasing complexity of modern power systems,driven by factors such as the large-scale integration of renewable energy and the proliferation of distributed generation,has placed unprecedented demands on power dispatching operations.Ensuring grid stability and safety in this new environment requires real-time monitoring and swift,data-driven decision-making.Consequently,efficient and accurate data querying capabilities have become paramount.This study introduces Intelli-Dispatch-SQL,a novel agent-based Text-to-SQL framework that leverages the Large Language Model(LLM)to enhance the accuracy and reliability of generated SQL queries in the context of power dispatching.By integrating intent recognition and SQL validation modules,Intelli-Dispatch-SQL ensures that generated queries are not only syntactically correct but also semantically aligned with user intent and executable within the operational context.Through comprehensive experiments,including ablation studies and cross-model evaluations,we demonstrate that Intelli-Dispatch-SQL significantly outperforms existing Text-to-SQL models,achieving substantial improvements in both Exact Match(EM)and Execution Accuracy(EX).Notably,the incorporation of intent recognition and SQL validation modules is shown to be critical for performance enhancement.The framework’s effectiveness was further validated across various LLMs,confirming its robustness and applicability across diverse scenarios.Intelli-Dispatch-SQL offers a performance high-and generalizable solution for Text-to-SQL in power dispatching,paving the way for more efficient and intelligent power system management.
基金funded by the Science and Technology Project of State Grid Shanxi Electric Power Company(5205E0230001).
文摘With the development of integrated power and gas distribution systems(IPGS)incorporating renewable energy sources(RESs),coordinating the restoration processes of the power distribution system(PS)and the gas distribution system(GS)by utilizing the benefits of RESs enhances service restoration.In this context,this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS.Additionally,a coordinated service restoration model is developed considering the two systems’interdependency and the GS’s dynamic characteristics.The objective is to maximize the system resilience index while adhering to operational,dynamic,restoration logic,and interdependency constraints.A method for managing uncertainties in RES output is employed,and convexification techniques are applied to address the nonlinear constraints arising from the physical laws of the IPGS,thereby reducing solution complexity.As a result,the service restoration optimization problem of the IPGS can be formulated as a computationally tractable mixed-integer second-order cone programming problem.The effectiveness and superiority of the proposed framework are demonstrated through numerical simulations conducted on the interdependent IEEE 13-bus PS and 9-node GS.The comparative results show that the proposed framework improves the system resilience index by at least 65.07%compared to traditional methods.
基金supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd under Grant 036000KC23090004(GDKJXM20231026).
文摘The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.
基金funded by Sponsorship of Science and Technology Project of State Grid Xinjiang Electric Power Co.,Ltd.,grant number SGXJ0000TKJS2400168.
文摘This study presents an emergency control method for sub-synchronous oscillations in wind power gridconnected systems based on transfer learning,addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios.By combining deep reinforcement learning with a transfer learning framework,cross-scenario knowledge transfer is achieved,significantly enhancing the adaptability of the control strategy.First,a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning.A reward evaluation system based on the active power oscillation pattern of the system is proposed,introducing penalty functions for the number of machine-shedding rounds and the number of machines shed.This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines.Furthermore,transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems.By introducing the Maximum Mean Discrepancy(MMD)algorithm to calculate the distribution differences between source data and target data,the online decision-making reliability of the emergency control model is improved.Finally,the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.
文摘The penetration rate of new wind and photovoltaic energy in the power system has increased significantly,and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional grid.This paper proposes a hierarchical optimal dispatch strategy for a high proportion of new energy power systems that considers the balanced response of grid flexibility.Firstly,various flexibility resource regulation capabilities on the source-load side are analyzed,and then flexibility demand and flexibility response are matched,and flexibility demand response assessment is proposed;then,a hierarchical optimal dispatch model of the grid taking flexibility adjustment capability into account is established,and the upper model optimizes the net load curve with the objectives of minimizing the fluctuation of the net load,maximizing the benefits of energy storage and controllable loads,and optimizing the flexibility adjustment capability.The upper layer model optimizes the net load curve by minimizing net load fluctuation,maximizing energy storage and controllable load revenue,and optimizing flexibility adjustment capability.In contrast,the lower layer model optimizes the power allocation of thermal power units and regulates the lost load of wind and solar power generation by minimizing the total system operating cost.The results show that the proposed strategy improves the flexibility of the grid by 15.2%,gives full play to the regulation capability of each flexibility resource,and reduces the fluctuation of the net load by 15.6%to achieve optimal coordination between different types of flexibility resources.
基金supported by the State Grid Corporation of China Headquarters Science and Technology Project“Research on Key Technologies for Power System Source-Load Forecasting and Regulation Capacity Assessment Oriented towards Major Weather Processes”(4000-202355381A-2-3-XG).
文摘Photovoltaic(PV)power generation is undergoing significant growth and serves as a key driver of the global energy transition.However,its intermittent nature,which fluctuates with weather conditions,has raised concerns about grid stability.Accurate PV power prediction has been demonstrated as crucial for power system operation and scheduling,enabling power slope control,fluctuation mitigation,grid stability enhancement,and reliable data support for secure grid operation.However,existing prediction models primarily target centralized PV plants,largely neglecting the spatiotemporal coupling dynamics and output uncertainties inherent to distributed PV systems.This study proposes a novel Spatio-Temporal Graph Neural Network(STGNN)architecture for distributed PV power generation prediction,designed to enhance distributed photovoltaic(PV)power generation forecasting accuracy and support regional grid scheduling.This approach models each PV power plant as a node in an undirected graph,with edges representing correlations between plants to capture spatial dependencies.The model comprises multiple Sparse Attention-based Adaptive Spatio-Temporal(SAAST)blocks.The SAAST blocks include sparse temporal attention,sparse spatial attention,an adaptive Graph Convolutional Network(GCN),and a temporal convolution network(TCN).These components eliminate weak temporal and spatial correlations,better represent dynamic spatial dependencies,and further enhance prediction accuracy.Finally,multi-dimensional comparative experiments between the STGNN and other models on the DKASC PV dataset demonstrate its superior performance in terms of accuracy and goodness-of-fit for distributed PV power generation prediction.
基金supported by National Key Research and Development Technology Project program(SQ2022YFB2400136).
文摘To enable distributed PV to adapt to variations in power grid strength and achieve stable grid connection while enhancing operational flexibility,it is essential to configure grid-connected inverters with an integrated grid-following control mode,allowing smooth switching between GFL and GFM modes.First,impedance models of GFL and GFM PV energy storage VSG systems were established,and grid stability was analyzed.Second,an online impedance identification method based on voltage fluctuation data screening was proposed to enhance the accuracy of impedance identification.Additionally,a PV energy storage GFM/GFL VSG smooth switching method based on current inner loop compensation was introduced to achieve stable grid-connected operation of distributed photovoltaics under changes in strong and weak power grids.Finally,a grid stability analysis was conducted on the multi-machine parallel PV ESS,and a simulation model of a multi-machine parallel PV ESS based on current inner loop compensation was established for testing.Results showed that,compared to using a single GFM or single GFL control for the PV VSG system,the smooth switching method of multi-machine parallel PV ESS effectively suppresses system resonance under variations in power grid strength,enabling adaptive and stable grid-connected operations of distributed PV.
基金supported in part by the National Natural Science Fundation of China(52225704 and 52107096).
文摘The dynamics of network power response play a crucial role in system stability.However,the integration of power electronic equipment leads to amplitude and angular frequency(abbreviated as"frequency")time-varying characteristics of the node voltage during dynamic processes.As a result,traditional calcu-lation methods for and characteristics of the power response of the network based on phasor and impe-dance lose their validity.Therefore,this paper undertakes mathematical calculations to reveal the power response of a network under excitation by voltage with time-varying amplitude and frequency(TVAF),relying on the original mathematical relationships and superimposed step response.Then,the multi-timescale characteristics of both the active and reactive power of the network are explored physically.Additionally,this paper reveals a new phenomenon of storing and releasing the active and reactive power of the network.To meet practical engineering requirements,a simplified power expression is presented.Finally,the theoretical analysis is validated through time-domain simulations.
基金funded by the Science and Technology Project of State Grid Corporation of China under Grant No.5108-202218280A-2-299-XG.
文摘Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.
基金funded by a Project from China Southern Power Grid Company Ltd.(Nos.ZBKJXM20232481 and ZBKJXM20232482)。
文摘This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.
文摘The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.
基金funded by the Guangdong Power Grid Co.,Ltd.Technology Project(GDKJXM20180019).
文摘With a lack of coverage in private and public power communication networks,especially for collection of information from hydropower stations in remote areas,communication coverage is a significant issue.Satellite communication provides a large coverage area suitable for a variety of services and is less affected by geographical factors;moreover,the costs are independent of the communication distance.This study investigates information acquisition technology for small hydropower stations in remote areas using high-and low-orbit satellites.The information collection needs of small hydropower stations in remote areas are analyzed,and an information acquisition system is designed using high-and low-orbit satellites.For network security protection,network anomaly detection technology based on a support vector machine algorithm is proposed.The effectiveness of information collection was evaluated and verified for small hydropower plants in remote areas.The system provides technical support for“full coverage,full collection,and full monitoring”of the measurement automation information acquisition system.
基金supported by the Science and Technology Project of the China Southern Power Grid Company Limited under Grant Number(GDKJXM20202001).
文摘The BeiDou-3 navigation satellite system(BDS-3)provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation.However,BDS-3 power system positioning faces certain challenges,such as complex electromagnetic interference and incomplete error elimination.Herein,a back propagation neural network-improved least mean square(BP-LMS)adaptive filtering method is proposed for the BDS-3 full-domain and high-precision power system positioning,which utilizes the loss function to update the weight of the BP hidden layer,computes the pseudo compensation range,and eliminates the impact of electromagnetic interference to enhance the accuracy of power system positioning.Simulation results confirm the superior performance of BP-LMS in positioning accuracy and error elimination.Compared with LMS and normalized least mean square(NLMS),the filtering error of the proposed BP-LMS adaptive filtering method is decreased by 57.14%and 51.38%,respectively.
文摘Due to the variable output of renewable energy (RE) generation, difficulties of dispatching RE for power system operators could not be avoided. One of possible solutions is the energy storage technology, especially the battery storage system. The large-scale energy storage system is available to support power system reliable flexibility for load following and system frequency regulation. In this paper, the bottlenecks of large-scale solar power generation dispatching and operation in Qinghai grid are discussed, and a new PV-energy storage coordinated dispatching method is proposed for reduction of PV curtailment in Qinghai. Moreover, the validation based on the time-series production simulation is provided using real data from Qinghai. The results indicate that the proposed method can effectively decrease the curtailment of solar power and future vision of large-scale solar power coordinated operation with energy storage system is also presented.
基金supported by the National Natural Science Foundation of China [grant number 42088101]the National Key R&D Program of China [grant number 2018YFC1505604]the National Natural Science Foundation of China [grant numbers 42005016 and 41905061]。
文摘By using electric power data,observational station temperature data in Beijing,CN05.1 temperature data,ERA5 atmospheric reanalysis data,and ERSST.v3 b sea surface temperature(SST) data,it is found that summer(JulyAugust) electric power demand in Beijing is remarkably positively correlated with the previous spring(MarchApril) tropical North Atlantic(TNA) SST anomaly(SSTA).The possible physical mechanism of the TNA SSTA affecting summer electric power in Beijing is also revealed.When a positive SSTA occurs in the TNA during spring,anomalous easterlies prevail over the tropical central Pacific,which can persist to the following summer.Trade winds are thus enhanced over the northern Pacific,which favors a strengthening of upwelling cold water in the tropical central-eastern Pacific.As a result,a negative SSTA appears in the central-eastern Pacific in summer,which means a La Nina event is triggered by the previous TNA SSTA through the Bjerknes feedback.During the La Nina event,an anomalous anticyclonic circulation occupies the northwestern Pacific.The southerly anomalies at the western edge of this anomalous anticyclone strengthen the transportation of warm and humid airflow from the low latitudes to North China,where Beijing is located,causing higher summer temperatures and increased electricity usage for air conditioning,and vice versa.The results of this study might provide a new scientific basis and dues for the seasonal prediction of summer electric power demand in Beijing.
文摘Based on real-time digital simulations(RTDS),a laboratory environment similar to the real-time operation situation of the Three Gorges Hydropower Station is established.Then,the causes for the power fluctuation of the second generator by errors in the rotor rotating speed calculation are found,and the tuning method of the speed related parameters are given.The damping and reverse tuning characteristics of power system stabilizers(PSSs)in the digital automatic voltage regulator(AVR)are compared and investigated in the frequency range of 0.18-1.1 Hz.The efficiency of the proposed tuning method for ensuring power system stability is verified by RTDS.Finally,field tests show the validity of the laboratory test results.
基金supported by China Southern Power Grid Technology Project under Grant 03600KK52220019(GDKJXM20220253).
文摘The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.
基金supported by Science and Technology Project of China Southern Power Grid Company Limited under Grant Number 036000KK52200058(GDKJXM20202001).
文摘Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.
