Driven by the global energy transition and carbon neutrality targets,alkaline water electrolysis has emerged as a key technology for coupling variable renewable generation with clean hydrogen production,offering consi...Driven by the global energy transition and carbon neutrality targets,alkaline water electrolysis has emerged as a key technology for coupling variable renewable generation with clean hydrogen production,offering considerable potential for absorbing surplus power and enhancing grid flexibility.However,conventional control architectures typically treat the power converter and electrolyzer as independent units,neglecting their dynamic interactions and thereby limiting overall system performance under practical operating conditions.This review critically examines existing control approaches,ranging from classical proportional-integral schemes to model predictive control,fuzzy-logic algorithms,and data-driven methods,evaluating their effectiveness in managing dynamic response,multivariable coupling,and operational constraints as well as their inherent limitations.Attention is then focused on the performance requirements of the hydrogen-production converter,including current ripple suppression,rapid transient response,adaptive thermal regulation,and stable power delivery.An integrated co‑control framework is proposed,aligning converter output with electrolyzer demand across steady-state operation,variable renewable input,and emergency shutdown scenarios to achieve higher efficiency,extended equipment lifetime,and enhanced operational safety.Finally,prospects for advancing unified control methodologies are outlined,with emphasis on constraint-aware predictive control,machine-learning-enhanced modeling,and real‑time co‑optimization for future alkaline electrolyzer systems.展开更多
The emergence of new resources and services in the electricity system implies that more and more agents need to obtain more accurate forecasts to optimize their operations.It is common for these agents to have differe...The emergence of new resources and services in the electricity system implies that more and more agents need to obtain more accurate forecasts to optimize their operations.It is common for these agents to have different sources of forecasts(from specialized consultants or meteorological services,among others).The proposed approach aims to obtain more accurate predictions by optimally combining a set of predictions obtained by different techniques.In this way it is possible to obtain a resulting prediction that improves the error and uncertainty associated with each of the individual forecasts.The objective is achieved by the analytical minimization of the errors obtained by each of the individual predictors.This allows to obtain dynamically the optimized weights assigned to each of the algorithms so that the combination outperforms the individual behaviour of each of them.The proposed ensemble approach has been successfully tested on a real time series of electric vehicle charging.Likewise,the results obtained have been compared exhaustively with other ensemble techniques consolidated in the literature based on different methods,including dynamic ensembles as machine learning approaches.The results obtained show an appreciable improvement of the errors obtained in the predictions using the proposed techniques.展开更多
This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers.The proposed technique is based on the unscented formulation of the Kalman filter,jointly considering the state...This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers.The proposed technique is based on the unscented formulation of the Kalman filter,jointly considering the state variables and parameters of the dynamic thermal model.A two-stage estimation technique that takes advantage of different loading conditions is developed,in order to increase the number of parameters which can be identified.Simulation results are presented,which show that the observable parameters are estimated with an error of less than 3%.The parameter estimation procedure is mainly intended for factory testing,allowing the manufacturer to enhance the thermal model of power transformers and,therefore,its customers to increase the lifetime of these assets.The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.展开更多
This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic(PV)array,using external measurements of current and voltage at the inverter level.The estimated model is of...This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic(PV)array,using external measurements of current and voltage at the inverter level.The estimated model is of interest to predict the power output of PV plants,in both planning and operation scenarios,and thus improves the efficient operation of power systems with high penetration of renewable energy.The proposed technique has been assessed in several simulated scenarios under different operating conditions.The results show that accurate estimates are provided for the model parameters,even in the presence of measurement noise and abrupt variations under the external conditions.展开更多
基金supported by Natural Science Foundation of Shanghai,under the Shanghai Action Plan for Science,Technology and Innovation(22ZR1464800).
文摘Driven by the global energy transition and carbon neutrality targets,alkaline water electrolysis has emerged as a key technology for coupling variable renewable generation with clean hydrogen production,offering considerable potential for absorbing surplus power and enhancing grid flexibility.However,conventional control architectures typically treat the power converter and electrolyzer as independent units,neglecting their dynamic interactions and thereby limiting overall system performance under practical operating conditions.This review critically examines existing control approaches,ranging from classical proportional-integral schemes to model predictive control,fuzzy-logic algorithms,and data-driven methods,evaluating their effectiveness in managing dynamic response,multivariable coupling,and operational constraints as well as their inherent limitations.Attention is then focused on the performance requirements of the hydrogen-production converter,including current ripple suppression,rapid transient response,adaptive thermal regulation,and stable power delivery.An integrated co‑control framework is proposed,aligning converter output with electrolyzer demand across steady-state operation,variable renewable input,and emergency shutdown scenarios to achieve higher efficiency,extended equipment lifetime,and enhanced operational safety.Finally,prospects for advancing unified control methodologies are outlined,with emphasis on constraint-aware predictive control,machine-learning-enhanced modeling,and real‑time co‑optimization for future alkaline electrolyzer systems.
基金the research project PID2021127550OA–I00 funded by MICIU/AEI/ 10.13039/501100011033 and by ERDF/EU.
文摘The emergence of new resources and services in the electricity system implies that more and more agents need to obtain more accurate forecasts to optimize their operations.It is common for these agents to have different sources of forecasts(from specialized consultants or meteorological services,among others).The proposed approach aims to obtain more accurate predictions by optimally combining a set of predictions obtained by different techniques.In this way it is possible to obtain a resulting prediction that improves the error and uncertainty associated with each of the individual forecasts.The objective is achieved by the analytical minimization of the errors obtained by each of the individual predictors.This allows to obtain dynamically the optimized weights assigned to each of the algorithms so that the combination outperforms the individual behaviour of each of them.The proposed ensemble approach has been successfully tested on a real time series of electric vehicle charging.Likewise,the results obtained have been compared exhaustively with other ensemble techniques consolidated in the literature based on different methods,including dynamic ensembles as machine learning approaches.The results obtained show an appreciable improvement of the errors obtained in the predictions using the proposed techniques.
基金supported by the project HySGrid+ (No.CER-20191019)the project IDENTICAL (No.TP-20210270)the project FlexOnGrid (No.PID2021-124571OB-I00)。
文摘This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers.The proposed technique is based on the unscented formulation of the Kalman filter,jointly considering the state variables and parameters of the dynamic thermal model.A two-stage estimation technique that takes advantage of different loading conditions is developed,in order to increase the number of parameters which can be identified.Simulation results are presented,which show that the observable parameters are estimated with an error of less than 3%.The parameter estimation procedure is mainly intended for factory testing,allowing the manufacturer to enhance the thermal model of power transformers and,therefore,its customers to increase the lifetime of these assets.The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.
文摘This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic(PV)array,using external measurements of current and voltage at the inverter level.The estimated model is of interest to predict the power output of PV plants,in both planning and operation scenarios,and thus improves the efficient operation of power systems with high penetration of renewable energy.The proposed technique has been assessed in several simulated scenarios under different operating conditions.The results show that accurate estimates are provided for the model parameters,even in the presence of measurement noise and abrupt variations under the external conditions.
