With the widespread application of combined heat and power(CHP)units,the economic dispatch of integrated electric and district heating systems(IEHSs)has drawn increasing attention.Because the electric power system(EPS...With the widespread application of combined heat and power(CHP)units,the economic dispatch of integrated electric and district heating systems(IEHSs)has drawn increasing attention.Because the electric power system(EPS)and district heating system(DHS)are generally managed separately,the decentralized dispatch pattern is preferable for the IEHS dispatch problem.However,many common decentralized methods suffer from the drawbacks of slow and local convergence.Moreover,the uncertainties of renewable generation cannot be ignored in a decentralized pattern.Additionally,the most commonly used individual chance constraints in distributionally robust optimization cannot consider safety constraints simultaneously,so the safe operation of an IEHS cannot be guaranteed.Thus,distributionally robust joint chance constraints and robust constraints are jointly introduced into the IEHS dispatch problem in this paper to obtain a stronger safety guarantee,and a method combined with Bonferroni and conditional value at risk(CVaR)approximation is presented to transform the original model into a quadratic program.Additionally,a dynamic boundary response(DBR)-based distributed algorithm based on multiparametric programming is proposed for a fast solution.Case studies showcase the necessity of using mixed distributionally robust joint chance constraints and robust constraints,as well as the effectiveness of the DBR algorithm.展开更多
In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,a...In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.展开更多
The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in futu...The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.展开更多
基金supported by National Natural Science Foundation of China(52377107,52007105)and the Taishan Scholars Program.
文摘With the widespread application of combined heat and power(CHP)units,the economic dispatch of integrated electric and district heating systems(IEHSs)has drawn increasing attention.Because the electric power system(EPS)and district heating system(DHS)are generally managed separately,the decentralized dispatch pattern is preferable for the IEHS dispatch problem.However,many common decentralized methods suffer from the drawbacks of slow and local convergence.Moreover,the uncertainties of renewable generation cannot be ignored in a decentralized pattern.Additionally,the most commonly used individual chance constraints in distributionally robust optimization cannot consider safety constraints simultaneously,so the safe operation of an IEHS cannot be guaranteed.Thus,distributionally robust joint chance constraints and robust constraints are jointly introduced into the IEHS dispatch problem in this paper to obtain a stronger safety guarantee,and a method combined with Bonferroni and conditional value at risk(CVaR)approximation is presented to transform the original model into a quadratic program.Additionally,a dynamic boundary response(DBR)-based distributed algorithm based on multiparametric programming is proposed for a fast solution.Case studies showcase the necessity of using mixed distributionally robust joint chance constraints and robust constraints,as well as the effectiveness of the DBR algorithm.
基金Financial support from the Engineering and Physical Sciences Research Council grant EP/V034723/1(RiFTMaP)is gratefully acknowledged.
文摘In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.
基金Project(2022YFC2904502)supported by the National Key Research and Development Program of ChinaProject(62273357)supported by the National Natural Science Foundation of China。
文摘The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.
