The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either el...The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems.Therefore,it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration.In this paper,a novel reliability evaluation method for integrated electricity-gas systems(IEGSs)is proposed.First,reliability network equivalents are utilized to represent reliability models of gas-fired generating units,gas sources(GSs),power-to-gas facilities,and other conventional generating units in IEGS.A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique.Finally,the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents.The proposed method is capable to evaluate customers’reliabilities in IEGS,which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.展开更多
With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affec...With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.展开更多
We present the general results determining confidence limits for the mean of exponential distribution in any time-sequential samples, which are obtained in any sequential life tests with replacement or without replace...We present the general results determining confidence limits for the mean of exponential distribution in any time-sequential samples, which are obtained in any sequential life tests with replacement or without replacement. Especially, we give the best lower confidence limits in the case of no failure data.展开更多
Insulation is one of the most important parts in a high voltage equipment.There are gaseous,liquid and solid insulations which are commonly used.In a high voltage transformer for example the insulating materials are a...Insulation is one of the most important parts in a high voltage equipment.There are gaseous,liquid and solid insulations which are commonly used.In a high voltage transformer for example the insulating materials are all used.During operation of a high voltage equipment high electric stress may occur.Under extreme condition failure of the insulation may take place.Excessive electric field in air may cause corona discharges while in liquid insulation discharges may take place in the form of streamer.This paper reports experimental results on the corona and streamer discharges in air and silicone oil.The discharges were artificially generated around a needle tip in a needle-plane electrode system with gap length of 4 mm under sinusoidal and triangular voltages.The needle was made of steel with tip radius of 3 μm and curvature angle of 30°.The needle was made by Ogura Jewelry.The discharge pulses were measured using personal-computer based partial discharge(PD)measurement system with sensitivity of better than 0.5 pC.The system is able to measure discharge in time sequential.Phase-resolved analysis of the discharges was done to interpret the physical processes behind the discharges.The experimental results showed that corona discharges took place at negative half cycles.The discharges were concentrated around 270° of phase angle of applied voltage.The discharge magnitude and discharge number of corona clearly dependent on the instantaneous of applied voltage.These were strongly supported by the application of triangular voltage.Streamer discharges occurred at both positive and negative half cycles.The discharges pulses concentrated around the peak of applied voltage at phase angle of 90° and 270°.Experimental results under sinusoidal and triangular voltages revealed that streamer discharge magnitude as well as probability of occurrence was strongly dependent on the instantaneous applied voltage.展开更多
基金supported by National Natural Science Foundation of China(No.71871200).
文摘The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems.The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems.Therefore,it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration.In this paper,a novel reliability evaluation method for integrated electricity-gas systems(IEGSs)is proposed.First,reliability network equivalents are utilized to represent reliability models of gas-fired generating units,gas sources(GSs),power-to-gas facilities,and other conventional generating units in IEGS.A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique.Finally,the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents.The proposed method is capable to evaluate customers’reliabilities in IEGS,which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.
基金supported by the National Natural Science Foundation of China(No.52177110)and the Shenzhen Science and Technology Program(No.JCYJ20210324131409026)。
文摘With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.
基金the National Natural Science Foundation of China(Grant No.10471007)and MCSEC grant.
文摘We present the general results determining confidence limits for the mean of exponential distribution in any time-sequential samples, which are obtained in any sequential life tests with replacement or without replacement. Especially, we give the best lower confidence limits in the case of no failure data.
文摘Insulation is one of the most important parts in a high voltage equipment.There are gaseous,liquid and solid insulations which are commonly used.In a high voltage transformer for example the insulating materials are all used.During operation of a high voltage equipment high electric stress may occur.Under extreme condition failure of the insulation may take place.Excessive electric field in air may cause corona discharges while in liquid insulation discharges may take place in the form of streamer.This paper reports experimental results on the corona and streamer discharges in air and silicone oil.The discharges were artificially generated around a needle tip in a needle-plane electrode system with gap length of 4 mm under sinusoidal and triangular voltages.The needle was made of steel with tip radius of 3 μm and curvature angle of 30°.The needle was made by Ogura Jewelry.The discharge pulses were measured using personal-computer based partial discharge(PD)measurement system with sensitivity of better than 0.5 pC.The system is able to measure discharge in time sequential.Phase-resolved analysis of the discharges was done to interpret the physical processes behind the discharges.The experimental results showed that corona discharges took place at negative half cycles.The discharges were concentrated around 270° of phase angle of applied voltage.The discharge magnitude and discharge number of corona clearly dependent on the instantaneous of applied voltage.These were strongly supported by the application of triangular voltage.Streamer discharges occurred at both positive and negative half cycles.The discharges pulses concentrated around the peak of applied voltage at phase angle of 90° and 270°.Experimental results under sinusoidal and triangular voltages revealed that streamer discharge magnitude as well as probability of occurrence was strongly dependent on the instantaneous applied voltage.
