This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the i...This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the impact of wakes on turbine effective wind speed is analyzed,leading to a quantitative method for assessing wake interactions.Based on these interactions,a partitioning method divides the wind farm into smaller,computationally manageable zones.Subsequently,a heuristic control algorithm is developed for yaw optimization within each partition,reducing the overall computational burden associated with multi-turbine optimization.The algorithm’s effectiveness is evaluated through case studies on 11-turbine and 28-turbine wind farms,demonstrating power generation increases of 9.78%and 1.78%,respectively,compared to baseline operation.The primary innovation lies in coupling the higher-fidelity dynamic FLORIDyn wake model with a graph-based partitioning strategy and a computationally efficient heuristic optimization,enabling scalable and accurate yaw control for large wind farms,overcoming limitations associated with simplified models or centralized optimization approaches.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
To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control s...To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.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
A decision feedback equalization(DFE)algorithm is proposed by simplifying Volterra structure.The simplification principle and process of the proposed Volterra-based equalization algorithm are presented.With the suppor...A decision feedback equalization(DFE)algorithm is proposed by simplifying Volterra structure.The simplification principle and process of the proposed Volterra-based equalization algorithm are presented.With the support of this algorithm,the signal damage for four-level pulse amplitude modulation signal(PAM-4)is compensated,which is caused by device bandwidth limitation and dispersion during transmission in C-band intensity modulation direct detection(IM-DD)fiber system.Experiments have been carried out to demonstrate that PAM-4 signals can transmit over 2 km in standard single-mode fiber(SSMF)based on a 30 GHz Mach-Zehnder modulator(MZM).The bit error rate(BER)can reach the threshold of hard decision-forward error correction(HD-FEC)(BER=3.8×10-3)and its sensitivity is reduced by 2 d Bm compared with traditional feedforward equalization(FFE).Meanwhile,the algorithm complexity is greatly reduced by 55%,which provides an effective theoretical support for the commercial application of the algorithm.展开更多
The dispatching for monthly generation plan is to manage the congestion considering the security constrains of the power grid, where the monthly generation plan is the result of vary monthly power exchange, including ...The dispatching for monthly generation plan is to manage the congestion considering the security constrains of the power grid, where the monthly generation plan is the result of vary monthly power exchange, including long-term power contract, power exchange among provinces and generation constitution exchanges. The application of monthly security constrained dispatching is with significant meaning for the security and stability of power grid. This paper brings forward the purpose and contents of security dispatching and introduces the working procedure and mathematic models. At last, the practical example of the Anhui Province power grid is introduced to explain the models.展开更多
The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presente...The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presented. The multi-agent control structure which is put forward in this article has three layers: system agent, areal agent and local agents. System agent sends controlling execution signal to the load-local agent according to the position and the amount of load shedding upload from areal agent;The areal agent judges whether the power system is stable by monitoring and analyzing the maximum relative power angle. In the condition of instability, determines the position of load-shedding, and the optimal amount of load-shedding according to the stability margin based on the corrected transient energy function, upload control amount to system agent;local-generator agent is mainly used for real-time monitoring the power angle of generator sets and uploading it to the areal agency, local-loads agent control load by receiving the control signal from system agent. Simulations on IEEE39 system show that the proposed control strategy improves the system stability.展开更多
This paper made a research on the Intelligent Optimization Operating Modeling of Pumped Storage Power Station in Hunan Power Grid. First it introduces the characteristics of Hunan power grid and analysis the practical...This paper made a research on the Intelligent Optimization Operating Modeling of Pumped Storage Power Station in Hunan Power Grid. First it introduces the characteristics of Hunan power grid and analysis the practical requirement of dispatching. Then it brings forward the intelligent optimization model and set up running model for pumped storage power station of Hei Mi-feng. At last, it introduces the application of pumped storage power station in Hunan power grid and proves the effectiveness of the optimization models.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
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
Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to...Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.展开更多
Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addi...Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.展开更多
The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastr...The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.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
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
The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cann...The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.展开更多
Large-capacity hydropower transmission from southwestern China to load centers via ultra-high voltage direct current(UHVDC) or ultra-high voltage alternating current(UHVAC) transmission lines is an important measure o...Large-capacity hydropower transmission from southwestern China to load centers via ultra-high voltage direct current(UHVDC) or ultra-high voltage alternating current(UHVAC) transmission lines is an important measure of the accommodation of large-scale hydropower in China. The East China Grid(ECG) is the main hydropower receiver of the west–east power transmission channel in China. Moreover, it has been subject to a rapidly increasing rate of hydropower integration over the past decade. Currently, large-scale outer hydropower is one of the primary ECG power sources. However, the integration of rapidly increasing outer hydropower into the power grid is subject to a series of severe drawbacks. Therefore, this study considered the load demands and hydropower transmission characteristics for the analysis of several major problems and the determination of appropriate solutions. The power supply-demand balance problem, hydropower transmission schedule problem, and peakshaving problem were considered in this study. Correspondingly, three solutions are suggested in this paper, which include coordination between the outer hydropower and local power sources, an inter-provincial power complementary operation, and the introduction of a market mechanism. The findings of this study can serve as a basis to ensure that the ECG effectively receives an increased amount of outer hydropower in the future.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
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.under Grant No.036000KK52222044(GDKJXM20222430).
文摘This paper addresses the complexity of wake control in large-scale wind farms by proposing a partitioning control algorithm utilizing the FLORIDyn(FLOW Redirection and Induction Dynamics)dynamic wake model.First,the impact of wakes on turbine effective wind speed is analyzed,leading to a quantitative method for assessing wake interactions.Based on these interactions,a partitioning method divides the wind farm into smaller,computationally manageable zones.Subsequently,a heuristic control algorithm is developed for yaw optimization within each partition,reducing the overall computational burden associated with multi-turbine optimization.The algorithm’s effectiveness is evaluated through case studies on 11-turbine and 28-turbine wind farms,demonstrating power generation increases of 9.78%and 1.78%,respectively,compared to baseline operation.The primary innovation lies in coupling the higher-fidelity dynamic FLORIDyn wake model with a graph-based partitioning strategy and a computationally efficient heuristic optimization,enabling scalable and accurate yaw control for large wind farms,overcoming limitations associated with simplified models or centralized optimization approaches.
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.
基金supported by the National Natural Science Foundation of China(52077078)China Southern Power Grid Company Limited 036000KK52220004(GDKJXM20220147).
文摘To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
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.
基金supported by the China Southern Power Grid Science and Technology Project,Research on 230 MHz Iot Access Architecture for Deep Coverage of Power Edge Services and R&D of Key Communication Devices(No.036000KK52180036).
文摘A decision feedback equalization(DFE)algorithm is proposed by simplifying Volterra structure.The simplification principle and process of the proposed Volterra-based equalization algorithm are presented.With the support of this algorithm,the signal damage for four-level pulse amplitude modulation signal(PAM-4)is compensated,which is caused by device bandwidth limitation and dispersion during transmission in C-band intensity modulation direct detection(IM-DD)fiber system.Experiments have been carried out to demonstrate that PAM-4 signals can transmit over 2 km in standard single-mode fiber(SSMF)based on a 30 GHz Mach-Zehnder modulator(MZM).The bit error rate(BER)can reach the threshold of hard decision-forward error correction(HD-FEC)(BER=3.8×10-3)and its sensitivity is reduced by 2 d Bm compared with traditional feedforward equalization(FFE).Meanwhile,the algorithm complexity is greatly reduced by 55%,which provides an effective theoretical support for the commercial application of the algorithm.
文摘The dispatching for monthly generation plan is to manage the congestion considering the security constrains of the power grid, where the monthly generation plan is the result of vary monthly power exchange, including long-term power contract, power exchange among provinces and generation constitution exchanges. The application of monthly security constrained dispatching is with significant meaning for the security and stability of power grid. This paper brings forward the purpose and contents of security dispatching and introduces the working procedure and mathematic models. At last, the practical example of the Anhui Province power grid is introduced to explain the models.
文摘The multi-agent theory is introduced and applied in the way to strike the control amount of emergency control according to stability margin, based on which an emergency control strategy of the power system is presented. The multi-agent control structure which is put forward in this article has three layers: system agent, areal agent and local agents. System agent sends controlling execution signal to the load-local agent according to the position and the amount of load shedding upload from areal agent;The areal agent judges whether the power system is stable by monitoring and analyzing the maximum relative power angle. In the condition of instability, determines the position of load-shedding, and the optimal amount of load-shedding according to the stability margin based on the corrected transient energy function, upload control amount to system agent;local-generator agent is mainly used for real-time monitoring the power angle of generator sets and uploading it to the areal agency, local-loads agent control load by receiving the control signal from system agent. Simulations on IEEE39 system show that the proposed control strategy improves the system stability.
文摘This paper made a research on the Intelligent Optimization Operating Modeling of Pumped Storage Power Station in Hunan Power Grid. First it introduces the characteristics of Hunan power grid and analysis the practical requirement of dispatching. Then it brings forward the intelligent optimization model and set up running model for pumped storage power station of Hei Mi-feng. At last, it introduces the application of pumped storage power station in Hunan power grid and proves the effectiveness of the optimization models.
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 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the Science and Technology Project of China South Power Grid Co., Ltd. under Grant No. 036000KK52222044 (GDKJXM20222430).
文摘Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.
基金the Science and Technology Project of State Grid Fujian Electric Power Co.,Ltd.(Project No.B31300240001)with the project title“Research on Key Technologies for Load Forecasting and Regulation Capability Evaluation of Regional Power Grid Taking into AccountWide Area Distributed New Energy Access”.
文摘Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.
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 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supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010 (5400-202199534A-05-ZN)。
文摘The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.
基金supported by the National Natural Science Foundation of China [No.51579029]Fundamental Research Funds for the Central Universities (No. DUT19JC43)
文摘Large-capacity hydropower transmission from southwestern China to load centers via ultra-high voltage direct current(UHVDC) or ultra-high voltage alternating current(UHVAC) transmission lines is an important measure of the accommodation of large-scale hydropower in China. The East China Grid(ECG) is the main hydropower receiver of the west–east power transmission channel in China. Moreover, it has been subject to a rapidly increasing rate of hydropower integration over the past decade. Currently, large-scale outer hydropower is one of the primary ECG power sources. However, the integration of rapidly increasing outer hydropower into the power grid is subject to a series of severe drawbacks. Therefore, this study considered the load demands and hydropower transmission characteristics for the analysis of several major problems and the determination of appropriate solutions. The power supply-demand balance problem, hydropower transmission schedule problem, and peakshaving problem were considered in this study. Correspondingly, three solutions are suggested in this paper, which include coordination between the outer hydropower and local power sources, an inter-provincial power complementary operation, and the introduction of a market mechanism. The findings of this study can serve as a basis to ensure that the ECG effectively receives an increased amount of outer hydropower in the future.
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.
