As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-int...As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization p...With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization problem inmulti-microgrid cooperation,a cooperative game strategy based on the Nash bargainingmodel is proposed,aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization.First,each microgrid is regarded as a game participant,and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed,targeting the minimization of total operational cost under constraints such as power balance and energy storage limits.Second,the Nash bargaining solution is introduced as the benefit allocation scheme to ensure individual rationality and coalition stability.Finally,theAlternating Direction Method of Multipliers(ADMM)is employed to decompose the centralized optimization problem into distributed subproblems for iterative solution,thereby reducing communication burden and protecting privacy.Case studies reveal that the operational costs of the threemicrogrids are reduced by 26.28%,19.00%,and 17.19%,respectively,and the overall renewable energy consumption rate is improved by approximately 66.11%.展开更多
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.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
The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage...The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.展开更多
The integration of large-scale new energy and high-capacity DC transmission leads to a reduction in system inertia.Grid-forming renewable energy sources(GF-RES)has a significant improvement effect on system inertia.Co...The integration of large-scale new energy and high-capacity DC transmission leads to a reduction in system inertia.Grid-forming renewable energy sources(GF-RES)has a significant improvement effect on system inertia.Commutation failure faults may cause a short-term reactive power surplus at the sending end and trigger transient overvoltage,threatening the safe and stable operation of the power grid.However,there is a lack of research on the calculation method of transient overvoltage caused by commutation failure in high-voltage DC transmission systems with grid-forming renewable energy sources integration.Based on the existing equivalent model of highvoltage DC transmission systems at the sending end,this paper proposes to construct a model of the high-voltage DC transmission system at the sending end with grid-forming renewable energy sources.The paper first clarifies the mechanism of overvoltage generation,then considers the reactive power droop control characteristics of GF-RES,and derives the transient voltage calculation model of theDC transmission system with GF-RES integration.It also proposes a calculation method for transient overvoltage at the sending-end converter bus with GF-RES integration.Based on the PSCAD/EMTDC simulation platform,this paper builds an experimental simulation model.By constructing three different experimental scenarios,the accuracy and effectiveness of the proposed transient overvoltage calculation method are verified,with a calculation error within 5%.At the same time,this paper quantitatively analyzes the impact of grid strength,new energy proportion,and rated transmission power on transient overvoltage from three different perspectives.展开更多
Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In respon...Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.展开更多
Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation...Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation failures in line commutated converter high voltage direct current(LCC-HVDC)systems at the receiving end leads to short-term power shortage(STPS),which differs from traditional frequency stability issues.STPS occurs during the generator’s power angle swing phase,before the governor responds,and is on a timescale that is not related to primary frequency regulation.This paper addresses these challenges by examining the impact of LVRT on voltage stability,developing a frequency response model to analyze the mechanism of frequency instability caused by STPS,deriving the impact of STPS on the maximum frequency deviation,and introducing an energy deficiency factor to assess its impact on regional frequency stability.The East China Power Grid is used as a case study,where the energy deficiency factor is calculated to validate the proposed mechanism.STPS is mainly compensated by the rotor kinetic energy of the generators in this region,with minimal impact on other regions.It is concluded that the energy deficiency factor provides an effective explanation for the spatial distribution of the impact of STPS on system frequency.展开更多
The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key feature...The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to enhance the optimization accuracy of the sparrow search algorithm. Finally, we construct an XGBoost-based model for the classification prediction of power grid meteorological faults and optimize the hyperparameters such as the optimal tree depth, optimal learning rate, and optimal number of iterations using an enhanced sparrow search algorithm. Experimental results demonstrate that our method outperforms the baseline models in predicting power grid faults accurately.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
In response to the complex working conditions of the power grid caused by the high proportion of new energy access,which leads to insufficient output accuracy of the second-order generalized integrator(SOGI)phaselocke...In response to the complex working conditions of the power grid caused by the high proportion of new energy access,which leads to insufficient output accuracy of the second-order generalized integrator(SOGI)phaselocked loop,this article proposes an improved frequency adaptive phase-locked loop structure for SOGI.Firstly,an amplitude compensation branch is introduced to compensate for the SOGI tracking fundamental frequency signal,ensuring the accuracy of the SOGI output orthogonal signal under frequency fluctuation conditions.Secondly,by cascading two adaptive SOGI modules,the suppression capability of low-order harmonics and Direct Current(DC)components has been improved.Finally,the positive and negative sequence separation method of orthogonal signals is introduced to eliminate the influence of unbalanced components on the phase-locked loop.The comparative experiment with the classic SOGI-PLL method shows that the proposed phase-locked loop structure effectively improves the accuracy of power grid synchronization detection under complex working conditions such as harmonic components,unbalanced components,and frequency fluctuations.It can complete frequency detection within 1.5 power frequency cycles,and the detected fundamental frequency positive sequence voltage has a higher sinuosity and harmonic distortion rate within 0.5%.展开更多
To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article com...To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.展开更多
With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable ener...With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.展开更多
In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and ba...In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and balancing a large number of loads nearby.However,under the background of a large-scale DPV grid-connected to the county distribution network,an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV.Therefore,a DPV orderly grid-connected method based on photovoltaics grid-connected order degree(PGOD)is proposed.This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected.Firstly,based on the voltagemagnitude sensitivity(VMS)index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node(AoGCN)index of other nodes in the network,thePGODindex is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network.Secondly,a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order.The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected,and explore the internal factors and characteristics of the impact.展开更多
The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business s...The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.展开更多
Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calcul...Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%.展开更多
Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation a...Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.展开更多
After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and de...After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.展开更多
When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicator...When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.展开更多
基金funded by the Technology Project of State Grid Jibei Electric Power Supply Co.,Ltd.(Grant Number:52018F240001).
文摘As the development of new power systems accelerates and the impacts of high renewable energy integration and extreme weather intensify,grid-alternative energy storage is garnering increasing attention for its grid-interaction benefits and clear business models.Consequently,assessing the value of grid-alternative energy storage in the systemtransition has become critically important.Considering the performance characteristics of storage,we propose a value assessment frame-work for grid-alternative energy storage,quantifying its non-wires-alternative effects from both cost and benefit perspectives.Building on this,we developed a collaborative planning model for energy storage and transmission grids,aimed at maximizing the economic benefits of storage systems while balancing investment and operational costs.The model considers regional grid interconnections and their interactions with system operation.By participating in system operations,grid-alternative energy storage not only maximizes its own economic benefits but also generates social welfare transfer effects.Furthermore,based on multi-regional interconnected planning,grid-alternative energy storage can reduce system costs by approximately 35%,with the most significant changes observed in generation costs.Multi-regional coordinated planning significantly enhances the sys-tem’s flexibility in regulation.However,when the load factor of interconnection lines between regions remains constant,system operational flexibility tends to decrease,leading to a roughly 28.9%increase in storage investment.Additionally,under regional coordinated planning,the greater the disparity in wind power integration across interconnected regions,the more noticeable the reduction in system costs.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金funded by StateGrid Beijing Electric PowerCompany Technology Project,grant number 520210230004.
文摘With the rapid development of renewable energy,the Microgrid Coalition(MGC)has become an important approach to improving energy utilization efficiency and economic performance.To address the operational optimization problem inmulti-microgrid cooperation,a cooperative game strategy based on the Nash bargainingmodel is proposed,aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization.First,each microgrid is regarded as a game participant,and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed,targeting the minimization of total operational cost under constraints such as power balance and energy storage limits.Second,the Nash bargaining solution is introduced as the benefit allocation scheme to ensure individual rationality and coalition stability.Finally,theAlternating Direction Method of Multipliers(ADMM)is employed to decompose the centralized optimization problem into distributed subproblems for iterative solution,thereby reducing communication burden and protecting privacy.Case studies reveal that the operational costs of the threemicrogrids are reduced by 26.28%,19.00%,and 17.19%,respectively,and the overall renewable energy consumption rate is improved by approximately 66.11%.
文摘By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
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funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China(21YJA790009)National Natural Science Foundation of China(72140001).
文摘The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy stor-age systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy gen-eration but also supply power support during peak demand periods,thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical micro-grid as a case study,an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal con-figuration of energy storage systems is determined,and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.
基金supported by Key Natural Science Research Projects of Colleges and Universities in Anhui Province(2022AH051831).
文摘The integration of large-scale new energy and high-capacity DC transmission leads to a reduction in system inertia.Grid-forming renewable energy sources(GF-RES)has a significant improvement effect on system inertia.Commutation failure faults may cause a short-term reactive power surplus at the sending end and trigger transient overvoltage,threatening the safe and stable operation of the power grid.However,there is a lack of research on the calculation method of transient overvoltage caused by commutation failure in high-voltage DC transmission systems with grid-forming renewable energy sources integration.Based on the existing equivalent model of highvoltage DC transmission systems at the sending end,this paper proposes to construct a model of the high-voltage DC transmission system at the sending end with grid-forming renewable energy sources.The paper first clarifies the mechanism of overvoltage generation,then considers the reactive power droop control characteristics of GF-RES,and derives the transient voltage calculation model of theDC transmission system with GF-RES integration.It also proposes a calculation method for transient overvoltage at the sending-end converter bus with GF-RES integration.Based on the PSCAD/EMTDC simulation platform,this paper builds an experimental simulation model.By constructing three different experimental scenarios,the accuracy and effectiveness of the proposed transient overvoltage calculation method are verified,with a calculation error within 5%.At the same time,this paper quantitatively analyzes the impact of grid strength,new energy proportion,and rated transmission power on transient overvoltage from three different perspectives.
文摘Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.
基金funded by the Technology Project of State Grid Corporation of China(Research on Safety and Stability Evaluation and Optimization Enhancement Technology of Flexible Ultra High Voltage Multiterminal DC System Adapting to the Background of“Sand and Gobi Deserts”),grant number J2024003。
文摘Countries worldwide are advocating for energy transition initiatives to promote the construction of low-carbon energy systems.The low voltage ride through(LVRT)characteristics of renewable energy units and commutation failures in line commutated converter high voltage direct current(LCC-HVDC)systems at the receiving end leads to short-term power shortage(STPS),which differs from traditional frequency stability issues.STPS occurs during the generator’s power angle swing phase,before the governor responds,and is on a timescale that is not related to primary frequency regulation.This paper addresses these challenges by examining the impact of LVRT on voltage stability,developing a frequency response model to analyze the mechanism of frequency instability caused by STPS,deriving the impact of STPS on the maximum frequency deviation,and introducing an energy deficiency factor to assess its impact on regional frequency stability.The East China Power Grid is used as a case study,where the energy deficiency factor is calculated to validate the proposed mechanism.STPS is mainly compensated by the rotor kinetic energy of the generators in this region,with minimal impact on other regions.It is concluded that the energy deficiency factor provides an effective explanation for the spatial distribution of the impact of STPS on system frequency.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(Research on Power Meteorology Digitalization Application for Future Climate Scenarios and New Energy Operation Risks,J2023076).
文摘The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to enhance the optimization accuracy of the sparrow search algorithm. Finally, we construct an XGBoost-based model for the classification prediction of power grid meteorological faults and optimize the hyperparameters such as the optimal tree depth, optimal learning rate, and optimal number of iterations using an enhanced sparrow search algorithm. Experimental results demonstrate that our method outperforms the baseline models in predicting power grid faults accurately.
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 Science and Technology Project of China Southern Power Grid Co.,Ltd.(Grant No.ZBKJXM20232471)the National Key R&D Program of China(Grant No.2021YFB1600200)the Innovation Capability Support Program of Shaanxi(Grant No.2022KXJ-144)。
文摘In response to the complex working conditions of the power grid caused by the high proportion of new energy access,which leads to insufficient output accuracy of the second-order generalized integrator(SOGI)phaselocked loop,this article proposes an improved frequency adaptive phase-locked loop structure for SOGI.Firstly,an amplitude compensation branch is introduced to compensate for the SOGI tracking fundamental frequency signal,ensuring the accuracy of the SOGI output orthogonal signal under frequency fluctuation conditions.Secondly,by cascading two adaptive SOGI modules,the suppression capability of low-order harmonics and Direct Current(DC)components has been improved.Finally,the positive and negative sequence separation method of orthogonal signals is introduced to eliminate the influence of unbalanced components on the phase-locked loop.The comparative experiment with the classic SOGI-PLL method shows that the proposed phase-locked loop structure effectively improves the accuracy of power grid synchronization detection under complex working conditions such as harmonic components,unbalanced components,and frequency fluctuations.It can complete frequency detection within 1.5 power frequency cycles,and the detected fundamental frequency positive sequence voltage has a higher sinuosity and harmonic distortion rate within 0.5%.
基金funded by National Key Research and Development Program of China (2021YFB2601400)。
文摘To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.
基金supported by State Grid Corporation of China Project“Research and Application of Key Technologies for Active Power Control in Regional Power Grid with High Penetration of Distributed Renewable Generation”(5108-202316044A-1-1-ZN).
文摘With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.
基金supported by North China Electric Power Research Institute’s Self-Funded Science and Technology Project“Research on Distributed Energy Storage Optimal Configuration and Operation Control Technology for Photovoltaic Promotion in the Entire County”(KJZ2022049).
文摘In recent years,distributed photovoltaics(DPV)has ushered in a good development situation due to the advantages of pollution-free power generation,full utilization of the ground or roof of the installation site,and balancing a large number of loads nearby.However,under the background of a large-scale DPV grid-connected to the county distribution network,an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV.Therefore,a DPV orderly grid-connected method based on photovoltaics grid-connected order degree(PGOD)is proposed.This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected.Firstly,based on the voltagemagnitude sensitivity(VMS)index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node(AoGCN)index of other nodes in the network,thePGODindex is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network.Secondly,a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order.The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected,and explore the internal factors and characteristics of the impact.
基金National Natural Science Foundation of China,Grant/Award Number:52177085DGRI‐CSG Innovative Project,Grant/Award Number:210000KK52220036。
文摘The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.
基金This work is supposed by the Science and Technology Projects of China Southern Power Grid(YNKJXM20222402).
文摘Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%.
基金supported by the National Key Research and Development Program of China(Program Number 2021YFB4000100)the Beijing Postdoctoral Research Foundation(Grant Number 2023-ZZ-63).
文摘Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.
基金supported by the State Grid Henan Economic Research Institute Science and Technology Project“Calculation and Demonstration of Distributed Photovoltaic Open Capacity Based on Multi-Source Heterogeneous Data”(5217L0230013).
文摘After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.
基金the Incubation Project of State Grid Jiangsu Corporation of China“Construction and Application of Intelligent Load Transferring Platform for Active Distribution Networks”(JF2023031).
文摘When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.
