The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while...Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.展开更多
A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations...A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.展开更多
Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency...Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.展开更多
Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple p...Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.展开更多
In this paper, a kind of fire new nonlinear integrator and integral action is proposed. Consequently, a conventional Proportional Nonlinear Integral (P_NI) observer and two kinds of added-order P_NI observers are deve...In this paper, a kind of fire new nonlinear integrator and integral action is proposed. Consequently, a conventional Proportional Nonlinear Integral (P_NI) observer and two kinds of added-order P_NI observers are developed to deal with the uncertain nonlinear system. The conditions on the observer gains to ensure the estimated error to be ultimate boundness, which shrinks to zero as the states and control inputs converge to the equilibrium point, are provided. This means that if the observed system is asymptotically stable, the estimated error dynamics is asymptotically stable, too. Moreover, the highlight point of this paper is that the design of nonlinear integral observer is achieved by linear system theory. Simulation results showed that under the normal and perturbed cases, the pure added-order P_NI observer can effectively deal with the uncertain nonlinearities on both the system dynamics and measured outputs.展开更多
A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring ...A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of UIES.In this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness perspective.In the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust decisions.The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power.The whole two-stage model is solved by the column-and-constraint generation(CCG)algorithm.Finally,case studies are conducted to show the performance of the proposed model and various approaches.Index Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.展开更多
Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supp...Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.展开更多
Multi-energy microgrids,such as integrated electricity-heat-gas microgrids(IEHS-MG),have been widely recognized as one of the most convenient ways to connect wind power(WP).However,the inherent intermittency and uncer...Multi-energy microgrids,such as integrated electricity-heat-gas microgrids(IEHS-MG),have been widely recognized as one of the most convenient ways to connect wind power(WP).However,the inherent intermittency and uncertainty of WP still render serious power curtailment in the operation.To this end,this paper presents an IEHS-MG model equipped with power-to-gas technology,thermal storage,electricity storage,and an electrical boiler for improving WP utilization efficiency.Moreover,a two-stage distributionally robust economic dispatch model is constructed for the IEHSMG,with the objective of minimizing total operational costs.The first stage determines the day-ahead decisions including on/off state and set-point decisions.The second stage adjusts the day-ahead decision according to real-time WP realization.Furthermore,WP uncertainty is characterized through an Imprecise Dirichlet model(IDM)based ambiguity set.Finally,Column-and-Constraints Generation method is utilized to solve the model,which provides a day-ahead economic dispatch strategy that immunizes against the worst-case WP distributions.Case studies demonstrate the presented IEHS-MG model outperforms traditional IEHS-MG model in terms of WP utilization and dispatch economics,and that distributionally robust optimization can handle uncertainty effectively.展开更多
A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter anal...A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter analysis of the system is conducted based on SOFC and ICE models.Results show that the number of cells,current density,and fuel utilization can influence SOFC and ICE.Moreover,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this paper.The results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power output.Furthermore,promising improvements in efficiency of 5.1%are achieved compared to the original engine.Finally,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.展开更多
The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, sel...The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.展开更多
Abstract： In the light of an increasing software complexity, many product variants and challenging market conditions, the automotive industry focuses two mitigation approaches： development processes and SW （softwar...Abstract： In the light of an increasing software complexity, many product variants and challenging market conditions, the automotive industry focuses two mitigation approaches： development processes and SW （software） architecture are standardized while model-driven software development technologies are progressively applied for series development. However, software architecture is subject to a continuous shift of requirements and boundary conditions. At the same time, process standards ensure necessary quality but also cause a dramatic increase of the SW development effort. There exists a methodical gap between process and market requirements on one hand and reusable standardized software functions with a high quality on the other. In this paper, an approach is presented that aims for the continuous extension of powertrain control software with increasing quality based on existing boundary conditions and a consequent methodical extension of existing technical concepts. We address this by the development of a sustainable software architecture which enables the safeguarding of consistent design principles and thus higher development efficiency. Moreover, it opens the door to a comprehensive quality assurance concept based on the agile software development principle Continuous Integration. Finally, the feasibility of this approach and software quality assessment results are shown by the application for a gasoline engine in the vehicle.展开更多
In this paper,we propose a class of robust independence tests for two random vectors based on weighted integrals of empirical characteristic functions.By letting weight functions be probability density functions of a ...In this paper,we propose a class of robust independence tests for two random vectors based on weighted integrals of empirical characteristic functions.By letting weight functions be probability density functions of a class of special distributions,the proposed test statistics have simple closed forms and do not require moment conditions on the random vectors.Moreover,we derive the asymptotic distributions of the test statistics under the null hypothesis.The proposed testing method is computationally feasible and easy to implement.Based on a data-driven bandwidth selection method,Monte Carlo simulation studies indicate that our tests have a relatively good performance compared with the competitors.A real data example is also presented to illustrate the application of our tests.展开更多
The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demand...The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.展开更多
The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells(PEMFCs)is sig-nificant for the advancement of this technology.Here,to solve this scientific issue,a surrogate modelling me...The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells(PEMFCs)is sig-nificant for the advancement of this technology.Here,to solve this scientific issue,a surrogate modelling method that combines a state-of-the-art three-dimensional PEMFC physical model and data-driven model is proposed.The surrogate modelling prediction results demonstrate that the test-set relative root mean square errors(rRMSEs)of the multi-physics fields range from 3.88%to 24.80%and can mirror the multi-physics field distribution charac-teristics well.In summary,for multi-physics field prediction,the data-driven surrogate model has a comparable accuracy to the comprehensive 3D physical model;however,it considerably reduces the cost of computation and time and achieves the efficient multi-physics-resolved digital-twin.Two model-based designs based on the as-developed digital twin framework,i.e.the PEMFC healthy operation envelope and the PEMFC state map,are demonstrated.This study highlights the potential of combining data-driven approaches and comprehensive physical models to develop the digital twin of complex systems,such as PEMFCs.展开更多
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20242194)the National Natural Science Foundation of China(Grant Nos.52175251 and 52205268)+1 种基金the Industry Key Technology Research Fund Project of Northwestern Polytechnical University(Grant No.HYGJXM202318)the National Basic Scientific Research Program(Grant No.JCKY2021206B005).
文摘Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.
文摘Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.
基金supported by the National Natural Science Foun-dation of China(52325001,52170009,and 52400114)the National Key Research and Development Program of China(2021YFC3200700 and 2021YFC3200702)+1 种基金the Program of Shanghai Academic Research Leader,China(21XD1424000)the Fundamental Research Funds for the Central Universities.
文摘Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.
文摘In this paper, a kind of fire new nonlinear integrator and integral action is proposed. Consequently, a conventional Proportional Nonlinear Integral (P_NI) observer and two kinds of added-order P_NI observers are developed to deal with the uncertain nonlinear system. The conditions on the observer gains to ensure the estimated error to be ultimate boundness, which shrinks to zero as the states and control inputs converge to the equilibrium point, are provided. This means that if the observed system is asymptotically stable, the estimated error dynamics is asymptotically stable, too. Moreover, the highlight point of this paper is that the design of nonlinear integral observer is achieved by linear system theory. Simulation results showed that under the normal and perturbed cases, the pure added-order P_NI observer can effectively deal with the uncertain nonlinearities on both the system dynamics and measured outputs.
基金supported by National Key Research and Development Program of China under Grant No.2019YFE0111500Science and Technology Department of Sichuan Province under Grant No.2020YFH0040National Natural Science Foundation of China under Grant No.51807125.
文摘A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of UIES.In this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness perspective.In the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust decisions.The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power.The whole two-stage model is solved by the column-and-constraint generation(CCG)algorithm.Finally,case studies are conducted to show the performance of the proposed model and various approaches.Index Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.
基金supported by National Key R&D Program of China(Grant No.2023YFE0108600)National Natural Science Foundation of China(Grant No.51806190)+1 种基金National Key R&D Program of China(Grant No.2022YFB3304502)Self-directed project,State Key Laboratory of Clean Energy Utilization.
文摘Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.
文摘Multi-energy microgrids,such as integrated electricity-heat-gas microgrids(IEHS-MG),have been widely recognized as one of the most convenient ways to connect wind power(WP).However,the inherent intermittency and uncertainty of WP still render serious power curtailment in the operation.To this end,this paper presents an IEHS-MG model equipped with power-to-gas technology,thermal storage,electricity storage,and an electrical boiler for improving WP utilization efficiency.Moreover,a two-stage distributionally robust economic dispatch model is constructed for the IEHSMG,with the objective of minimizing total operational costs.The first stage determines the day-ahead decisions including on/off state and set-point decisions.The second stage adjusts the day-ahead decision according to real-time WP realization.Furthermore,WP uncertainty is characterized through an Imprecise Dirichlet model(IDM)based ambiguity set.Finally,Column-and-Constraints Generation method is utilized to solve the model,which provides a day-ahead economic dispatch strategy that immunizes against the worst-case WP distributions.Case studies demonstrate the presented IEHS-MG model outperforms traditional IEHS-MG model in terms of WP utilization and dispatch economics,and that distributionally robust optimization can handle uncertainty effectively.
文摘A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter analysis of the system is conducted based on SOFC and ICE models.Results show that the number of cells,current density,and fuel utilization can influence SOFC and ICE.Moreover,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this paper.The results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power output.Furthermore,promising improvements in efficiency of 5.1%are achieved compared to the original engine.Finally,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.
文摘The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.
文摘Abstract： In the light of an increasing software complexity, many product variants and challenging market conditions, the automotive industry focuses two mitigation approaches： development processes and SW （software） architecture are standardized while model-driven software development technologies are progressively applied for series development. However, software architecture is subject to a continuous shift of requirements and boundary conditions. At the same time, process standards ensure necessary quality but also cause a dramatic increase of the SW development effort. There exists a methodical gap between process and market requirements on one hand and reusable standardized software functions with a high quality on the other. In this paper, an approach is presented that aims for the continuous extension of powertrain control software with increasing quality based on existing boundary conditions and a consequent methodical extension of existing technical concepts. We address this by the development of a sustainable software architecture which enables the safeguarding of consistent design principles and thus higher development efficiency. Moreover, it opens the door to a comprehensive quality assurance concept based on the agile software development principle Continuous Integration. Finally, the feasibility of this approach and software quality assessment results are shown by the application for a gasoline engine in the vehicle.
基金supported by National Natural Science Foundation of China(NNSFC)(Grant No.12201317)China Postdoctoral Science Foundation(Grant No.2022M721716),Changliang Zou’s research was supported by the National Key R&D Program of China(Grant Nos.2022YFA1003703,2022YFA1003800)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925106,12231011,11931001,12226007,12326325)Cui’s research was supported by NNSFC(Grant Nos.12031016 and 11971324)。
文摘In this paper,we propose a class of robust independence tests for two random vectors based on weighted integrals of empirical characteristic functions.By letting weight functions be probability density functions of a class of special distributions,the proposed test statistics have simple closed forms and do not require moment conditions on the random vectors.Moreover,we derive the asymptotic distributions of the test statistics under the null hypothesis.The proposed testing method is computationally feasible and easy to implement.Based on a data-driven bandwidth selection method,Monte Carlo simulation studies indicate that our tests have a relatively good performance compared with the competitors.A real data example is also presented to illustrate the application of our tests.
基金National Natural Sciences Foundation of China,Grant/Award Numbers:62125302,62203087Sci-Tech Talent Innovation Support Program of Dalian,Grant/Award Number:2022RG03+1 种基金Liaoning Revitalization Talents Program,Grant/Award Number:XLYC2002087Young Elite Scientist Sponsorship Program by CAST,Grant/Award Number:YESS20220018。
文摘The electricity and steam integrated energy systems,which can capture waste heat and improve the overall energy efficiency,have been widely utilised in industrial parks.However,intensive and frequent changes in demands would lead to model parameters with strong time-varying characteristics.This paper proposes a hybrid physics and data-driven framework for online joint state and parameter estimation of steam and electricity integrated energy system.Based on the physical non-linear state space models for the electricity network(EN)and steam heating network(SHN),relevance vector machine is developed to learn parameters'dynamic characteristics with respect to model states,which is embedded with physical models.Then,the online joint state and parameter estimation based on unscented Kalman filter is proposed,which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs.The IEEE 39-bus EN and the 29-nodes SHN are employed to verify the effectiveness of the proposed method.The experimental results validate that the pro-posed method can provide a higher estimation accuracy than the state-of-the-art approaches.
基金This research is supported by the China-UK International Coopera-tion and Exchange Project(Newton Advanced Fellowship)jointly sup-ported by the National Natural Science Foundation of China(grant No.51861130359)the UK Royal Society(grant No.NAF\R1\180146)the Natural Science Foundation of Tianjin(China)for Distinguished Young Scholars(Grant No.18JCJQJC46700).
文摘The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells(PEMFCs)is sig-nificant for the advancement of this technology.Here,to solve this scientific issue,a surrogate modelling method that combines a state-of-the-art three-dimensional PEMFC physical model and data-driven model is proposed.The surrogate modelling prediction results demonstrate that the test-set relative root mean square errors(rRMSEs)of the multi-physics fields range from 3.88%to 24.80%and can mirror the multi-physics field distribution charac-teristics well.In summary,for multi-physics field prediction,the data-driven surrogate model has a comparable accuracy to the comprehensive 3D physical model;however,it considerably reduces the cost of computation and time and achieves the efficient multi-physics-resolved digital-twin.Two model-based designs based on the as-developed digital twin framework,i.e.the PEMFC healthy operation envelope and the PEMFC state map,are demonstrated.This study highlights the potential of combining data-driven approaches and comprehensive physical models to develop the digital twin of complex systems,such as PEMFCs.
