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
Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained u...Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously.An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained(total cost can be reduced at least 2%).展开更多
According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are ...According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed. First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account. In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers. Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model. Finally, two different examples are designed to illustrate that the model and solution discussed are valid.展开更多
Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter varia...Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.展开更多
Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we p...Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work.Firstly,we established the uncertainty model of wind power and PV based on the chance constrained planning theory.Then we used the K-medoids clusteringmethod to cluster the scenarios considering the actual operation scenarios throughout the year.Secondly,we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation.Finally,by quantitative analysis of actual wind power and photovoltaic new energy base,this work verified the feasibility of the proposed method.As a result of the simulations,we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity.When the installed capacity of the solar-thermal power station is 1×106 kW,the transient voltage recovery index(TVRI)is 0.359,which has a strong voltage support capacity for the system.Based on the results of this work,the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance,transient voltage support capability,and new energy consumption while satisfying the Direct Current(DC)outgoing transmission premise.展开更多
Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete informatio...Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete information,varying thermal parameters,and stochastic user behaviors,which hin ders incorporating the public buildings into power system oper ation.To address the problem,this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation.Firstly,the DR evaluation is developed based on the equivalent thermal parameter(ETP)model,actual outdoor tem perature data,and air conditioning(AC)consumption data.To quantify the uncertainties of public buildings,the interval evalu ation is given employing the linear regression method consider ing the confidence bound.Utilizing the evaluation results,the risk dispatch model is proposed to allocate public building re serve based on the chance constrained programming(CCP).Fi nally,the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming(MISOCP)for its solution.The proposed evaluation method and the risk dis patch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.展开更多
To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a clust...To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.展开更多
Unit commitment （UC） problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the gene...Unit commitment （UC） problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the generation side because of wind-thermal co-ordination system as well as stochasticity in the load side by incorporating the randomness of the load. The most important issue that needs to be addressed is the achievement of an economic unit commitment solution after solving UC as a real time problem. This paper proposes a hybrid approach to solve the stochastic UC problem considering the volatile nature of wind and formulating the UC problem as a chance constrained problem in which the load is met with high probability over the entire time period.展开更多
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.
基金Financial support from the National Natural Science Foundation of China (22022816, 22078358)。
文摘Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously.An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained(total cost can be reduced at least 2%).
文摘According to the operational characteristics of the logistics networks for the third party logistics supplier （3PLS）, the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed. First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account. In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers. Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model. Finally, two different examples are designed to illustrate that the model and solution discussed are valid.
文摘Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.
基金funded by Major Science and Technology Projects in Gansu Province(19ZD2GA003).
文摘Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work.Firstly,we established the uncertainty model of wind power and PV based on the chance constrained planning theory.Then we used the K-medoids clusteringmethod to cluster the scenarios considering the actual operation scenarios throughout the year.Secondly,we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation.Finally,by quantitative analysis of actual wind power and photovoltaic new energy base,this work verified the feasibility of the proposed method.As a result of the simulations,we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity.When the installed capacity of the solar-thermal power station is 1×106 kW,the transient voltage recovery index(TVRI)is 0.359,which has a strong voltage support capacity for the system.Based on the results of this work,the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance,transient voltage support capability,and new energy consumption while satisfying the Direct Current(DC)outgoing transmission premise.
基金supported by the National Science Fund for Distinguished Young Scholars(No.52125702)the Key Science and Technology Project of China Southern Power Grid Corporation(No.090000KK52220020).
文摘Public buildings present substantial demand re sponse(DR)potential,which can participate in the power sys tem operation.However,most public buildings exhibit a high degree of uncertainties due to incomplete information,varying thermal parameters,and stochastic user behaviors,which hin ders incorporating the public buildings into power system oper ation.To address the problem,this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation.Firstly,the DR evaluation is developed based on the equivalent thermal parameter(ETP)model,actual outdoor tem perature data,and air conditioning(AC)consumption data.To quantify the uncertainties of public buildings,the interval evalu ation is given employing the linear regression method consider ing the confidence bound.Utilizing the evaluation results,the risk dispatch model is proposed to allocate public building re serve based on the chance constrained programming(CCP).Fi nally,the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming(MISOCP)for its solution.The proposed evaluation method and the risk dis patch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.
基金supported in part by the National Natural Science Foundation of China under Grant U2066601,51725703Southern Power Grid Technical Project GDKJXM20185069(032000KK52180069).
文摘To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.
文摘Unit commitment （UC） problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the generation side because of wind-thermal co-ordination system as well as stochasticity in the load side by incorporating the randomness of the load. The most important issue that needs to be addressed is the achievement of an economic unit commitment solution after solving UC as a real time problem. This paper proposes a hybrid approach to solve the stochastic UC problem considering the volatile nature of wind and formulating the UC problem as a chance constrained problem in which the load is met with high probability over the entire time period.
