The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial car...The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.展开更多
As the development of new power systems progresses,the inherent inertia of power systems continues to diminish.Centralized frequency regulation,which relies on rapid communication and real-time control,can enable inve...As the development of new power systems progresses,the inherent inertia of power systems continues to diminish.Centralized frequency regulation,which relies on rapid communication and real-time control,can enable inverter-based thermostatically controlled load(ITCL)clusters to provide virtual inertia support to the power grid.However,ITCL clusters exhibit significant discrete response characteristics,which precludes the direct integration of load-side inertia support into the synchronous unit side.To address this issue,this paper elaborates on the existing technical framework and analyzes the underlying causes of the problem.It proposes a timestamp allocation mechanism for ITCL cluster control instructions,ensuring that many ITCL terminals can be triggered at staggered times,thereby allowing the load cluster power to adhere to the inertia analog control law at any moment.Building on this foundation,the paper further examines the impact of the inertia response delay of ITCL clusters,which is based on centralized frequency regulation,on the stability of the power system.A design scheme for inertia analog control parameters is proposed,taking into account dual constraints,frequency stability and load cluster regulation capacity.Finally,the feasibility and applicability of the proposed mechanism and parameter design scheme are investigated through simulations conducted via MATLAB/Simulink.展开更多
The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independe...The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independent operation and multi-electrolyzer parallelization,each with distinct advantages and challenges.This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers,establishing a weakly coupled system that combines the advantages of two modes.This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection,while preserving the option for independent operation after decoupling.A specialized thermal exchange model is developed for this topology,according to the dynamics of the lye mixer.The study further details startup procedures and proposes optimized control strategies tailored to this structural design.Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system,enabling a rapid hot start to enhance the system’s ability to track renewable energy.A control strategy is established to reduce heat loss and increase startup speed,and the optimal valve openings of the diverter valve and the manifold valve are determined.Simulation results indicate a considerable enhancement in operational efficiency,marked by an 18.28%improvement in startup speed and a 6.11%reduction in startup energy consumption inmulti-electrolyzer cluster systems,particularlywhen the systems are synchronized with photovoltaic energy sources.The findings represent a significant stride toward efficient and sustainable hydrogen production,offering a promising path for large-scale integration of renewable energy.展开更多
The difficulty in capital recovery for distributed re newable energy operators(DREOs)and the high charging costs at electric vehicle charging stations(EVCSs)have long been sig nificant challenges in power systems.Coll...The difficulty in capital recovery for distributed re newable energy operators(DREOs)and the high charging costs at electric vehicle charging stations(EVCSs)have long been sig nificant challenges in power systems.Collaborative operation of DREOs and EVCSs can effectively address these challenges,yet few studies have approached incentivizing collaboration from the perspective of profit allocation.Therefore,this paper pro poses a fair and efficient profit allocation method.Incorporat ing the Gauss-Legendre quadrature formula into the AumannShapley value(GL-AS)method enables efficient calculation of the profit allocation of cooperative members.However,existing literature only discusses the profit allocation method of conven tional power generation units,limiting its applicability.This pa per addresses the problem of energy storage system(ESS)switching between charging and discharging in any time inter val and the time-varying problem of renewable energy power output,thereby ensuring the efficiency of the solution process.Furthermore,a novel profit allocation adjustment model is pro vided through the adoption of triangular fuzzy comprehensive evaluation(TFCE).Finally,the effectiveness of the proposed profit allocation method is validated through numerical simula tions in various scenarios.展开更多
Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are s...Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.展开更多
Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accura...Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.展开更多
To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of re...To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.展开更多
The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleann...The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.展开更多
An arc is the high-temperature discharge plasma produced in the opening process of mechanical switches,which directly affects the breaking capability of a hybrid DC circuit breaker.According to the physical mechanism ...An arc is the high-temperature discharge plasma produced in the opening process of mechanical switches,which directly affects the breaking capability of a hybrid DC circuit breaker.According to the physical mechanism of an electric arc,the construction of an arc model for simulation analysis is an important technical means in the electrical field.In this study,based on the theory of magneto hydrodynamics(MHD),a gas mechanical switch model of a natural commutation DC circuit breaker with a compound gap is established.The arc motion process under different conditions is simulated and calcu-lated.The influence of different initial pressures,different opening speeds,and different striking currents on the arc voltage characteristics is analysed.The results show that the larger the gas pressure,the smaller the arc volume and the higher the arc voltage.The faster the opening speed,the longer the arc and the higher the arc voltage;with the increase of the current,the arc voltage increases rapidly at a low current,while the arc voltage increases slowly at a high current.展开更多
In-line inspection is one of the most effective technical measures to ensure the safety of oil and gas pipelines.However,the extensive application of oil and gas pipelines of high-carbon steel,large diameter,high pres...In-line inspection is one of the most effective technical measures to ensure the safety of oil and gas pipelines.However,the extensive application of oil and gas pipelines of high-carbon steel,large diameter,high pressure and high flow rate in recent years brings about new challenges to in-line inspection.In this paper,we investigated the engineering application of new technologies and equipment,including the in-line inspection technology based on electromagnetic detector array,the extremely low frequency transient weak signal detection technology and new in-line inspection equipment.Then,the new technology and equipment of electromagnetic detector array were developed by applying the active emission and receiving of electromagnetic signal to inspect the metallic defects.Finally,noisy signals were inspected on the basis of Duffing chaotic oscillator,and thus the inspection of extremely low frequency signals in the noise was realized.In addition,the new method and equipment of extremely low frequency transient weak signal detection were developed and verified in actual inspection engineering.And the following research results were obtained.First,the in-line inspection technology based on electromagnetic detector array uses the synergetic effect between DC exciting magnetic field and high-frequency exciting magnetic field,so a perturbation response can be realized even by small signal excitation.Second,with the introduction of a novel compressive sampling of acquisition information composition,the speed bottleneck of in-line detector is broken through,and a new world record of the inspection speed of the electromagnetic detector array is set up,i.e.,8 m/s.Third,under the condition of high-speed movement,the received signals of in-line detector are weak and temporary while tracking and positioning outside the pipeline.And by virtue of the extremely low frequency transient weak signal detection method based on chaos,this technical bottleneck is broken through,and the signal-to-noise ratio(SNR)of transient weak signal in the process of real-time inspection is decreased below−10 dB.In conclusion,the superior performance of these new equipment has already been verified in the inspection engineering of in-service oil and gas pipelines.These research results will provide technical and equipment support for the safe operation of domestic main oil and gas pipelines.展开更多
With the establishment of “carbon peaking and carbon neutrality” goals in China, along with the development of new power systems and ongoing electricity market reforms, pumped-storage power stations (PSPSs) will inc...With the establishment of “carbon peaking and carbon neutrality” goals in China, along with the development of new power systems and ongoing electricity market reforms, pumped-storage power stations (PSPSs) will increasingly play a significant role in power systems. Therefore, this study focuses on trading and bidding strategies for PSPSs in the electricity market. Firstly, a comprehensive framework for PSPSs participating in the electricity energy and frequency regulation (FR) ancillary service market is proposed. Subsequently, a two-layer trading model is developed to achieve joint clearing in the energy and frequency regulation markets. The upper-layer model aims to maximize the revenue of the power station by optimizing the bidding strategies using a Q-learning algorithm. The lower-layer model minimized the total electricity purchasing cost of the system. Finally, the proposed bi-level trading model is validated by studying an actual case in which data are obtained from a provincial power system in China. The results indicate that through this decision-making method, PSPSs can achieve higher economic revenue in the market, which will provide a reference for the planning and operation of PSPSs.展开更多
The occurrence of breaking overvoltage in a DC circuit breaker(DCCB)poses a potential threat to the safe operation of a DC grid.Based on the structure of the�10 kV three-terminal DC distribution network,this paper set...The occurrence of breaking overvoltage in a DC circuit breaker(DCCB)poses a potential threat to the safe operation of a DC grid.Based on the structure of the�10 kV three-terminal DC distribution network,this paper sets up the medium-voltage DCCB and formulates a fault protection strategy.Additionally,a simulation model of the system is developed to analyse the overvoltage characteristics within the DC distribution network across different nodes and fault conditions.By examining the factors that influence breaking overvoltage,this paper unravels the fundamental mechanisms associated with DCCB-related overvoltage generation.Furthermore,the paper suggests measures to alleviate DCCB overvoltage.These insights provide a theoretical and technical basis for the design and operation of DCCBs within DC distribution networks.展开更多
This paper presents a fully customised integrated gate commutated thyristor(IGCT)gate driver monolithic integrated circuit(GDMIC),aiming to address the many shortcomings of traditional IGCT gate driver units composed ...This paper presents a fully customised integrated gate commutated thyristor(IGCT)gate driver monolithic integrated circuit(GDMIC),aiming to address the many shortcomings of traditional IGCT gate driver units composed of discrete components,such as the excessive number of components,low reliability,and complex development processes.The current-source driving characteristics of IGCTs pose significant technical challenges for developing fully customised integrated circuits(IC).The customised requirements of IGCT gate driver chips under various operating conditions are explored regarding functional module division,power sequencing,and chip parameter specifications.However,existing high-side(HS)driver methods exhibit limitations in functional monolithic integration and bipolar complementary metal-oxide-semiconductor compat-ibility.To address these challenges,a novel HS driving topology based on floating linear regulators is proposed.It can achieve synchronised control of multi-channel floating power transistors while supporting 100%duty cycle continuous conduction.The pro-posed GDMIC reduces the three independent HS power supplies to a single multiplexed topology,significantly decreasing circuit complexity.Experimental results validate the feasibility and performance of a 4-inch gate driver prototype based on IGCT current-source management IC,demonstrating significant advantages in reducing the number of components,enhancing device reliability,and simplifying development.The proposed GDMIC offers an innovative development path for future high-power IGCT drivers.展开更多
Commutation failure(CF)is an inherent problem faced by line commutated converter high voltage direct current(LCC-HVDC)technology.To completely solve the problem of CF,we have proposed a novel hybrid commutated convert...Commutation failure(CF)is an inherent problem faced by line commutated converter high voltage direct current(LCC-HVDC)technology.To completely solve the problem of CF,we have proposed a novel hybrid commutated converter(HCC)technology based on reverse blocking integrated gate commutated thyristor,which can utilise two methods for commutation:enhanced grid voltage commutation and active turn-off forced com-mutation.In this paper,the topology and operating principle of HCC are proposed.Then,the control and protection strategy is designed based on the current variation trend under AC faults.To verify the effectiveness of HCC in mitigating CF,a 120-kV/360-MW HCC-HVDC model is built in PSCAD/EMTDC,adopting LCC at the rectifier side and HCC at the inverter side.Based on this model,HCC steady-state and fault transient stresses are analysed.Various AC faults are simulated and the performance of HCC-HVDC is compared with LCC-HVDC.Finally,the results show that the HCC topol-ogy and proposed control strategy can solve CF under all fault conditions with almost the same attributes as LCC,such as large capacity,low cost,low loss and high reliability,which is meaningful for the application of HCC to the HVDC transmission system.展开更多
The high penetration of variable renewable energy raises a flexibility challenge in the power system.This raises the necessity of considering the adequacy of flexibility in power system planning.However,the flexibilit...The high penetration of variable renewable energy raises a flexibility challenge in the power system.This raises the necessity of considering the adequacy of flexibility in power system planning.However,the flexibility of the power system covers a wide range of timescales,from seconds to months.This poses difficulties in planning of multi-timescale flexible resources.This paper proposes a new perspective on the modeling and planning of multi-timescale flexible resources in power systems with high penetration of variable renewable energy.The operational boundaries of flexible resources are transformed into a characteristic domain,where flexibility at different timescales can be added and the balance of flexible supply and demand can be expressed as algebraic equations.Such modeling facilitates rigorous multi-timescale flexibility balance metrics.Furthermore,a planning method for multi-timescale flexibility is proposed based on the model in the characteristic domain.The proposed planning method is tested using data from China's Xinjiang provincial power grid.Results show the proposed method can characterize multi-timescale flexibility with high accuracy,thus making it possible to fully account for flexibility at different timescales.展开更多
During on-site withstand voltage tests of gas-insulated switchgears(GIS),once a breakdown occurs,it is hard to locate the breakdown position due to the intricate branch structures and minimal breakdown energy.The exis...During on-site withstand voltage tests of gas-insulated switchgears(GIS),once a breakdown occurs,it is hard to locate the breakdown position due to the intricate branch structures and minimal breakdown energy.The existing methods may require the placement of a large number of sensors with unsatisfactory accuracy,which adversely affects the process of inspecting and repairing,as well as the subsequent tests.To address this issue,this paper introduces a novel GIS breakdown location method.This method is predicated on the natural frequency of the transient voltage caused by the breakdown.The relationship between the breakdown position and the natural frequency is first derived,which is referred to as the location equation.Then,the natural frequency characteristics are discussed from both mathematical and energy perspectives.Based on these characteristics,the location method is proposed that utilises the location equation and the frequencies at two measurement points.The results of the laboratory experiments demonstrate the accuracy of the method and certain advantages over the ultrasonic method.Further,the effectiveness of the method in GIS and gas-insulated transmission lines(GIL)with complex structures and high voltage levels is also confirmed by simu-lation cases and field experimental data.展开更多
The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, s...The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.展开更多
A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism ...A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism and neglecting network constraints,this paper optimizes the power and energy parameters of VB to enlarge its flexibility region.An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty.An algorithm based on the Benders decomposition is developed to solve this problem.The proposed method yields the largest VB satisfying constraints of both network and SLRs.Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.展开更多
This study conducts a comparative analysis between detached eddy simulation(DES)and Unsteady Reynolds-averaged Navier-Stokes(URANS)models for simulating pressure fluctuations in a stilling basin,aiming to assess the U...This study conducts a comparative analysis between detached eddy simulation(DES)and Unsteady Reynolds-averaged Navier-Stokes(URANS)models for simulating pressure fluctuations in a stilling basin,aiming to assess the URANS mode’s performance in modeling pressure fluctuation.The URANS model predicts accurately a smoother flow field and its time-average pressure,yet it underestimates the root mean square of pressure(RMSP)fluctuation,achieving approximately 70%of the results predicted by DES model on the bottom floor of the stilling basin.Compared with DES model’s results,which are in alignment with the Kolmogorov−5/3 law,the URANS model significantly overestimates low-frequency pulsations,particularly those below 0.1 Hz.We further propose a novel method for estimating the RMSP in the stilling basin using URANS model results,based on the establishment of a quantitative relationship between the RMSP,time-averaged pressure,and turbulent kinetic energy in the boundary layer.The proposed method closely aligns with DES results,showing a mere 15%error level.These findings offer vital insights for selecting appropriate turbulence models in hydraulic engineering and provide a valuable tool for engineers to estimate pressure fluctuation in stilling basins.展开更多
The main goal of distribution network(DN)expansion planning is essentially to achieve minimal investment con-strained by specified reliability requirements.The reliability-constrained distribution network planning(RcD...The main goal of distribution network(DN)expansion planning is essentially to achieve minimal investment con-strained by specified reliability requirements.The reliability-constrained distribution network planning(RcDNP)problem can be cast as an instance of mixed-integer linear programming(MILP)which involves ultra-heavy computation burden especially for large-scale DNs.In this paper,we propose a parallel computing based solution method for the RcDNP problem.The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination.Then,a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems.The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem.Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition.Under mild conditions,the optimality and convergence of the proposed method are verified.Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-worldproblems.展开更多
基金supported by the Scientific&Technical Project of the State Grid(5700--202490228A--1--1-ZN).
文摘The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.
基金supported by the Key Scientific and Technological Projects(2024KJGG27)of Tianfu Yongxing Laboratorythe Experimental Platform Open Innovation Funding(209042025003)of Sichuan Energy Internet Research Institute,Tsinghua University.
文摘As the development of new power systems progresses,the inherent inertia of power systems continues to diminish.Centralized frequency regulation,which relies on rapid communication and real-time control,can enable inverter-based thermostatically controlled load(ITCL)clusters to provide virtual inertia support to the power grid.However,ITCL clusters exhibit significant discrete response characteristics,which precludes the direct integration of load-side inertia support into the synchronous unit side.To address this issue,this paper elaborates on the existing technical framework and analyzes the underlying causes of the problem.It proposes a timestamp allocation mechanism for ITCL cluster control instructions,ensuring that many ITCL terminals can be triggered at staggered times,thereby allowing the load cluster power to adhere to the inertia analog control law at any moment.Building on this foundation,the paper further examines the impact of the inertia response delay of ITCL clusters,which is based on centralized frequency regulation,on the stability of the power system.A design scheme for inertia analog control parameters is proposed,taking into account dual constraints,frequency stability and load cluster regulation capacity.Finally,the feasibility and applicability of the proposed mechanism and parameter design scheme are investigated through simulations conducted via MATLAB/Simulink.
基金supported by the Key Technology Research and Application Demonstration Project for Large-Scale Multi-Scenario Water Electrolysis Hydrogen Production(CTGTC/2023-LQ-06).
文摘The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independent operation and multi-electrolyzer parallelization,each with distinct advantages and challenges.This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers,establishing a weakly coupled system that combines the advantages of two modes.This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection,while preserving the option for independent operation after decoupling.A specialized thermal exchange model is developed for this topology,according to the dynamics of the lye mixer.The study further details startup procedures and proposes optimized control strategies tailored to this structural design.Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system,enabling a rapid hot start to enhance the system’s ability to track renewable energy.A control strategy is established to reduce heat loss and increase startup speed,and the optimal valve openings of the diverter valve and the manifold valve are determined.Simulation results indicate a considerable enhancement in operational efficiency,marked by an 18.28%improvement in startup speed and a 6.11%reduction in startup energy consumption inmulti-electrolyzer cluster systems,particularlywhen the systems are synchronized with photovoltaic energy sources.The findings represent a significant stride toward efficient and sustainable hydrogen production,offering a promising path for large-scale integration of renewable energy.
基金supported in part by the National Natural Science Foundation of China(No.52122706)in part by the Tsinghua University Initiative Scientific Research Program.
文摘The difficulty in capital recovery for distributed re newable energy operators(DREOs)and the high charging costs at electric vehicle charging stations(EVCSs)have long been sig nificant challenges in power systems.Collaborative operation of DREOs and EVCSs can effectively address these challenges,yet few studies have approached incentivizing collaboration from the perspective of profit allocation.Therefore,this paper pro poses a fair and efficient profit allocation method.Incorporat ing the Gauss-Legendre quadrature formula into the AumannShapley value(GL-AS)method enables efficient calculation of the profit allocation of cooperative members.However,existing literature only discusses the profit allocation method of conven tional power generation units,limiting its applicability.This pa per addresses the problem of energy storage system(ESS)switching between charging and discharging in any time inter val and the time-varying problem of renewable energy power output,thereby ensuring the efficiency of the solution process.Furthermore,a novel profit allocation adjustment model is pro vided through the adoption of triangular fuzzy comprehensive evaluation(TFCE).Finally,the effectiveness of the proposed profit allocation method is validated through numerical simula tions in various scenarios.
基金The research is supported by the National Natural Science Foundation of China(62072469)the National Key R&D Program of China(2018AAA0101502)+2 种基金Shandong Natural Science Foundation(ZR2019MF049)West Coast artificial intelligence technology innovation center(2019-1-5,2019-1-6)the Opening Project of Shanghai Trusted Industrial Control Platform(TICPSH202003015-ZC).
文摘Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
基金support of national natural science foundation of China(No.52067021)natural science foundation of Xinjiang(2022D01C35)+1 种基金excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012)major science and technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2).
文摘Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.
基金supported by Manage Innovation Project of China Southern Power Grid Co.,Ltd.(No.GZHKJXM20210232).
文摘To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.
基金supported by the National Key R&D Program of China(No.2021YFB2401200).
文摘The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.
基金National Natural Science Foundation of China,Grant/Award Numbers:51922062,52241701。
文摘An arc is the high-temperature discharge plasma produced in the opening process of mechanical switches,which directly affects the breaking capability of a hybrid DC circuit breaker.According to the physical mechanism of an electric arc,the construction of an arc model for simulation analysis is an important technical means in the electrical field.In this study,based on the theory of magneto hydrodynamics(MHD),a gas mechanical switch model of a natural commutation DC circuit breaker with a compound gap is established.The arc motion process under different conditions is simulated and calcu-lated.The influence of different initial pressures,different opening speeds,and different striking currents on the arc voltage characteristics is analysed.The results show that the larger the gas pressure,the smaller the arc volume and the higher the arc voltage.The faster the opening speed,the longer the arc and the higher the arc voltage;with the increase of the current,the arc voltage increases rapidly at a low current,while the arc voltage increases slowly at a high current.
基金supported by the Major Project of Scientific Instrument and Equipment under National Key R&D Program of China“Development and Pilot Application of Internal and External Detection and Fault Diagnosis Equipment for Deep sea Oil and Gas Pipelines”(No.:2017YFF0108800)the Major Project of Risk Prevention and Control and Emergency Response Technologies and Equipment for Public Security under National Key R&D Program of China“Inspection,Examination and Security Technologies for Long-distance Oil and Gas Pipelines and Storage&Transportation Facilities”(No.:2016YFC0802100).
文摘In-line inspection is one of the most effective technical measures to ensure the safety of oil and gas pipelines.However,the extensive application of oil and gas pipelines of high-carbon steel,large diameter,high pressure and high flow rate in recent years brings about new challenges to in-line inspection.In this paper,we investigated the engineering application of new technologies and equipment,including the in-line inspection technology based on electromagnetic detector array,the extremely low frequency transient weak signal detection technology and new in-line inspection equipment.Then,the new technology and equipment of electromagnetic detector array were developed by applying the active emission and receiving of electromagnetic signal to inspect the metallic defects.Finally,noisy signals were inspected on the basis of Duffing chaotic oscillator,and thus the inspection of extremely low frequency signals in the noise was realized.In addition,the new method and equipment of extremely low frequency transient weak signal detection were developed and verified in actual inspection engineering.And the following research results were obtained.First,the in-line inspection technology based on electromagnetic detector array uses the synergetic effect between DC exciting magnetic field and high-frequency exciting magnetic field,so a perturbation response can be realized even by small signal excitation.Second,with the introduction of a novel compressive sampling of acquisition information composition,the speed bottleneck of in-line detector is broken through,and a new world record of the inspection speed of the electromagnetic detector array is set up,i.e.,8 m/s.Third,under the condition of high-speed movement,the received signals of in-line detector are weak and temporary while tracking and positioning outside the pipeline.And by virtue of the extremely low frequency transient weak signal detection method based on chaos,this technical bottleneck is broken through,and the signal-to-noise ratio(SNR)of transient weak signal in the process of real-time inspection is decreased below−10 dB.In conclusion,the superior performance of these new equipment has already been verified in the inspection engineering of in-service oil and gas pipelines.These research results will provide technical and equipment support for the safe operation of domestic main oil and gas pipelines.
基金Supported by the Innovation Project of the China Southern Power Grid Co.,Ltd.(020000KK52210005).
文摘With the establishment of “carbon peaking and carbon neutrality” goals in China, along with the development of new power systems and ongoing electricity market reforms, pumped-storage power stations (PSPSs) will increasingly play a significant role in power systems. Therefore, this study focuses on trading and bidding strategies for PSPSs in the electricity market. Firstly, a comprehensive framework for PSPSs participating in the electricity energy and frequency regulation (FR) ancillary service market is proposed. Subsequently, a two-layer trading model is developed to achieve joint clearing in the energy and frequency regulation markets. The upper-layer model aims to maximize the revenue of the power station by optimizing the bidding strategies using a Q-learning algorithm. The lower-layer model minimized the total electricity purchasing cost of the system. Finally, the proposed bi-level trading model is validated by studying an actual case in which data are obtained from a provincial power system in China. The results indicate that through this decision-making method, PSPSs can achieve higher economic revenue in the market, which will provide a reference for the planning and operation of PSPSs.
基金National Natural Science Foundation of China,Grant/Award Numbers:51922062,52241701。
文摘The occurrence of breaking overvoltage in a DC circuit breaker(DCCB)poses a potential threat to the safe operation of a DC grid.Based on the structure of the�10 kV three-terminal DC distribution network,this paper sets up the medium-voltage DCCB and formulates a fault protection strategy.Additionally,a simulation model of the system is developed to analyse the overvoltage characteristics within the DC distribution network across different nodes and fault conditions.By examining the factors that influence breaking overvoltage,this paper unravels the fundamental mechanisms associated with DCCB-related overvoltage generation.Furthermore,the paper suggests measures to alleviate DCCB overvoltage.These insights provide a theoretical and technical basis for the design and operation of DCCBs within DC distribution networks.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFB2401604The Integration Projects of National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid,Grant/Award Number:U2166602National Natural Science Foundation of China,Grant/Award Number:52241701。
文摘This paper presents a fully customised integrated gate commutated thyristor(IGCT)gate driver monolithic integrated circuit(GDMIC),aiming to address the many shortcomings of traditional IGCT gate driver units composed of discrete components,such as the excessive number of components,low reliability,and complex development processes.The current-source driving characteristics of IGCTs pose significant technical challenges for developing fully customised integrated circuits(IC).The customised requirements of IGCT gate driver chips under various operating conditions are explored regarding functional module division,power sequencing,and chip parameter specifications.However,existing high-side(HS)driver methods exhibit limitations in functional monolithic integration and bipolar complementary metal-oxide-semiconductor compat-ibility.To address these challenges,a novel HS driving topology based on floating linear regulators is proposed.It can achieve synchronised control of multi-channel floating power transistors while supporting 100%duty cycle continuous conduction.The pro-posed GDMIC reduces the three independent HS power supplies to a single multiplexed topology,significantly decreasing circuit complexity.Experimental results validate the feasibility and performance of a 4-inch gate driver prototype based on IGCT current-source management IC,demonstrating significant advantages in reducing the number of components,enhancing device reliability,and simplifying development.The proposed GDMIC offers an innovative development path for future high-power IGCT drivers.
基金National Natural Science Foundation of China-State Grid Corporation Joint Fund for Smart Grid,Grant/Award Number:U2166602。
文摘Commutation failure(CF)is an inherent problem faced by line commutated converter high voltage direct current(LCC-HVDC)technology.To completely solve the problem of CF,we have proposed a novel hybrid commutated converter(HCC)technology based on reverse blocking integrated gate commutated thyristor,which can utilise two methods for commutation:enhanced grid voltage commutation and active turn-off forced com-mutation.In this paper,the topology and operating principle of HCC are proposed.Then,the control and protection strategy is designed based on the current variation trend under AC faults.To verify the effectiveness of HCC in mitigating CF,a 120-kV/360-MW HCC-HVDC model is built in PSCAD/EMTDC,adopting LCC at the rectifier side and HCC at the inverter side.Based on this model,HCC steady-state and fault transient stresses are analysed.Various AC faults are simulated and the performance of HCC-HVDC is compared with LCC-HVDC.Finally,the results show that the HCC topol-ogy and proposed control strategy can solve CF under all fault conditions with almost the same attributes as LCC,such as large capacity,low cost,low loss and high reliability,which is meaningful for the application of HCC to the HVDC transmission system.
基金supported by the Science and Technology Project of State Grid Corporation of China"The technology and application of model refinement and aggregation to support multi-level,multiagent and multiperiod dispatch"(5100-202099497A-0-0-00).
文摘The high penetration of variable renewable energy raises a flexibility challenge in the power system.This raises the necessity of considering the adequacy of flexibility in power system planning.However,the flexibility of the power system covers a wide range of timescales,from seconds to months.This poses difficulties in planning of multi-timescale flexible resources.This paper proposes a new perspective on the modeling and planning of multi-timescale flexible resources in power systems with high penetration of variable renewable energy.The operational boundaries of flexible resources are transformed into a characteristic domain,where flexibility at different timescales can be added and the balance of flexible supply and demand can be expressed as algebraic equations.Such modeling facilitates rigorous multi-timescale flexibility balance metrics.Furthermore,a planning method for multi-timescale flexibility is proposed based on the model in the characteristic domain.The proposed planning method is tested using data from China's Xinjiang provincial power grid.Results show the proposed method can characterize multi-timescale flexibility with high accuracy,thus making it possible to fully account for flexibility at different timescales.
基金National Key R&D Program of China,Grant/Award Numbers:2022YFB2403700,2022YFB2403705。
文摘During on-site withstand voltage tests of gas-insulated switchgears(GIS),once a breakdown occurs,it is hard to locate the breakdown position due to the intricate branch structures and minimal breakdown energy.The existing methods may require the placement of a large number of sensors with unsatisfactory accuracy,which adversely affects the process of inspecting and repairing,as well as the subsequent tests.To address this issue,this paper introduces a novel GIS breakdown location method.This method is predicated on the natural frequency of the transient voltage caused by the breakdown.The relationship between the breakdown position and the natural frequency is first derived,which is referred to as the location equation.Then,the natural frequency characteristics are discussed from both mathematical and energy perspectives.Based on these characteristics,the location method is proposed that utilises the location equation and the frequencies at two measurement points.The results of the laboratory experiments demonstrate the accuracy of the method and certain advantages over the ultrasonic method.Further,the effectiveness of the method in GIS and gas-insulated transmission lines(GIL)with complex structures and high voltage levels is also confirmed by simu-lation cases and field experimental data.
基金supported in part by the founding of state key laboratory of industrial control technology,Zhejiang University(ICT2021B19)the Technological Innovation and Application Demonstration in Chongqing(Major Themes of Industry:cstc2019jscx-zdztzxX0033,cstc2019jscxfxyd0158)the National Natural Science Foundation of China(NO.22005026,21908142).
文摘The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.
基金supported by the Science and Technology Institute of China Three Gorges Corporation under Grant 202103386.
文摘A virtual battery(VB)provides a succinct interface for aggregating distributed storage-like resources(SLR)to interact with a utility-level system.To overcome the drawbacks of existing VB models,including conservatism and neglecting network constraints,this paper optimizes the power and energy parameters of VB to enlarge its flexibility region.An optimal VB is identified by a robust optimization problem with decision-dependent uncertainty.An algorithm based on the Benders decomposition is developed to solve this problem.The proposed method yields the largest VB satisfying constraints of both network and SLRs.Case studies verify the superiority of the optimal VB in terms of security guarantee and less conservatism.
基金Project supported by the Key Research and Development Plan Project of China(Grant No.2022YFC3204602)the National Natural Science Foundation of China(Grant No.U21A20157).
文摘This study conducts a comparative analysis between detached eddy simulation(DES)and Unsteady Reynolds-averaged Navier-Stokes(URANS)models for simulating pressure fluctuations in a stilling basin,aiming to assess the URANS mode’s performance in modeling pressure fluctuation.The URANS model predicts accurately a smoother flow field and its time-average pressure,yet it underestimates the root mean square of pressure(RMSP)fluctuation,achieving approximately 70%of the results predicted by DES model on the bottom floor of the stilling basin.Compared with DES model’s results,which are in alignment with the Kolmogorov−5/3 law,the URANS model significantly overestimates low-frequency pulsations,particularly those below 0.1 Hz.We further propose a novel method for estimating the RMSP in the stilling basin using URANS model results,based on the establishment of a quantitative relationship between the RMSP,time-averaged pressure,and turbulent kinetic energy in the boundary layer.The proposed method closely aligns with DES results,showing a mere 15%error level.These findings offer vital insights for selecting appropriate turbulence models in hydraulic engineering and provide a valuable tool for engineers to estimate pressure fluctuation in stilling basins.
基金supported in part by the State Grid Science and Technology Program of China(No.5100-202121561A-0-5-SF).
文摘The main goal of distribution network(DN)expansion planning is essentially to achieve minimal investment con-strained by specified reliability requirements.The reliability-constrained distribution network planning(RcDNP)problem can be cast as an instance of mixed-integer linear programming(MILP)which involves ultra-heavy computation burden especially for large-scale DNs.In this paper,we propose a parallel computing based solution method for the RcDNP problem.The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination.Then,a parallelizable augmented Lagrangian algorithm with acceleration method is developed to solve the coordination planning problems.The lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem.Gauss-Seidel iteration is adopted on the subset of the convex hull of the feasible region constructed by decomposition.Under mild conditions,the optimality and convergence of the proposed method are verified.Numerical tests show that the proposed method can significantly reduce the solution time and make the RcDNP applicable for real-worldproblems.
