During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive...During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h^(2)/(4√2),where h is the piecewise length.展开更多
Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower＇s functions are separable with respect to the follower＇s variables, and the other is that the follower＇s f...Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower＇s functions are separable with respect to the follower＇s variables, and the other is that the follower＇s functions are convex if the follower＇s variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader＇s variable x, it is proved that the optimal solution y of the follower＇s mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower＇s programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter λ, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.展开更多
Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of ...Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential complexity.Due to this reason,various MIP methods are proposed for various subclasses of PNs.This work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)PR.In particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution space.Experimental results show that the proposed MIP method has higher computational efficiency.Furthermore,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.展开更多
To properly describe and solve complex decision problems,research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important.We establish in this paper different Lipschitz-ty...To properly describe and solve complex decision problems,research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important.We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints.The obtained results extend some existing results for continuous quadratic programs,and,more importantly,lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.展开更多
In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact infor...In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.展开更多
In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onproces...In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onprocess synthesis problems. The algorithms are developed for the special case in which the nonlinearitiesarise because of logarithmic terms, with the first one being developed for the deterministic case, and thesecond for the parametric case （p-MINLP）. The key idea is to formulate and solve the square system of thefirst-order Karush-Kuhn-Tucker （KKT） conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution tech-niques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, twoprocess synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicitfunction of the uncertain parameters.展开更多
An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established ...An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediate-to-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.展开更多
With the reform of the power system further deepening,the reliance on electricity and importance attached to the reliable power supply are increasing year by year,and the establishment of a high resilient power system...With the reform of the power system further deepening,the reliance on electricity and importance attached to the reliable power supply are increasing year by year,and the establishment of a high resilient power system has considerable economic,environmental and social benefits.Reconfiguring the network is one of the well-known tactics to enhance reliability.Accordingly,this paper proposes a reconfiguration method of distribution network considering the enhancement of reliability,which reconfigures the network structure both under normal operation conditions and outage scenarios,and considers factors such as power loss,load distribution and voltage quality considered in conventional reconfiguration methods.In this paper,the reliability assessment is integrated into the process of distribution network reconfiguration by using binary variables to represent the operating state of switchable devices.Based on the concept of fictitious fault flows,the reliability indices of distribution network are linearized expressed,and the network loss is reduced by minimizing the voltage deviation.A mixed integer linear programming(MILP)model is established for distribution network reconfiguration problem,which can guarantee the global optimal solution with high solution efficiency.Finally,the applicability and effectiveness of the proposed method are verified by numerical tests on a 54-node test system.展开更多
In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon syner...In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation ofmulti-energy parks.Themethod integratesmultienergy complementary scheduling with a tiered carbon trading mechanism to balance operational security,economic efficiency,and environmental objectives.A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment,including photovoltaic systems,ground-source heat pumps,thermal storage electric boilers,combined heat and power units,and electrical energy storage systems.Furthermore,a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices.Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64%reduction in carbon emissions compared to economy-oriented operation,while limiting the increase in operational costs to 20.85%.The carbon-prioritized scenario further reduces emissions by 9.7%,underscoring the inhibitory effect of the tiered carbon pricing mechanism on highcarbon activities.Sensitivity analyses confirm the model’s robustness against fluctuations in energy load,uncertainty in renewable generation,and variations in carbon price.This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks,offering valuable insights for promoting green transformation initiatives.展开更多
Oxygen consumption is an important index of coal oxidation.In order to explore the coal-oxygen reaction,we developed an experimental system of coal spontaneous combustion and tested oxygen consumption of differently r...Oxygen consumption is an important index of coal oxidation.In order to explore the coal-oxygen reaction,we developed an experimental system of coal spontaneous combustion and tested oxygen consumption of differently ranked coals at programmed temperatures.The size of coal samples ranged from 0.18~0.42 mm and the system heat-rate was 0.8℃/min.The results show that, for high ranked coals,oxygen consumption rises with coal temperature as a piecewise non-linear process.The critical coal temperature is about 50℃.Below this temperature,oxygen consumption decreases with rising coal temperatures and reached a minimum at 50℃,approximately.Subsequently,it begins to increase and the rate of growth clearly increased with temperature.For low ranked coals,this characteristic is inconspicuous or even non-existent.The difference in oxygen consumption at the same temperatures varies for differently ranked coals.The results show the difference in oxygen consumption of the coals tested in our study reached 78.6%at 100℃.Based on the theory of coal-oxygen reaction,these phenomena were analyzed from the point of view of physical and chemical characteristics,as well as the appearance of the coal-oxygen complex.From theoretical analyses and our experiments,we conclude that the oxygen consumption at programmed temperatures reflects the oxidation ability of coals perfectly.展开更多
The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain o...The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming solvers.In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.展开更多
Micro-phasor measurement units(μPMUs)with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network w...Micro-phasor measurement units(μPMUs)with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations.Therefore,this paper investigates the problem of how to place a limited number ofμPMUs to improve the state estimation accuracy.Combined with pseudo-measurements and supervisory control and data acquisition(SCADA)measurements,an optimalμPMU placement model is proposed based on a two-step state estimation method.The E-optimal experimental criterion is utilized to measure the state estimation accuracy.The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming(MISDP)problem,whose optimal solution can be obtained by using the improved Benders decomposition method.Simulations on several systems are carried out to evaluate the effective performance of the proposed model.展开更多
Many important integer and mixed-integer programming problems are difficult to solve.A representative example is unit commitment with combined cycle units and transmission capacity constraints.Complicated transitions ...Many important integer and mixed-integer programming problems are difficult to solve.A representative example is unit commitment with combined cycle units and transmission capacity constraints.Complicated transitions within combined cycle units are difficult to follow,and system-wide coupling transmission capacity constraints are difficult to handle.Another example is the quadratic assignment problem.The presence of cross-products in the objective function leads to nonlinearity.In this study,building upon the novel integration of surrogate Lagrangian relaxation and branch-and-cut,such problems will be solved by relaxing selected coupling constraints.Monotonicity of the relaxed problem will be assumed and exploited and nonlinear terms will be dynamically linearised.The linearity of the resulting problem will be exploited using branch-and-cut.To achieve fast convergence,guidelines for selecting stepsizing parameters will be developed.The method opens up directions for solving nonlinear mixed-integer problems,and numerical results indicate that the new method is efficient.展开更多
In order to improve the performance of time difference of arrival(TDOA)localization,a nonlinear least squares algorithm is proposed in this paper.Firstly,based on the criterion of the minimized sum of square error of ...In order to improve the performance of time difference of arrival(TDOA)localization,a nonlinear least squares algorithm is proposed in this paper.Firstly,based on the criterion of the minimized sum of square error of time difference of arrival,the location estimation is expressed as an optimal problem of a non-linear programming.Then,an initial point is obtained using the semi-definite programming.And finally,the location is extracted from the local optimal solution acquired by Newton iterations.Simulation results show that when the number of anchor nodes is large,the performance of the proposed algorithm will be significantly better than that of semi-definite programming approach with the increase of measurement noise.展开更多
In this paper,a strength-constrained unit commitment(UC)model incorporating system strength constraints based on the weighted short-circuit ratio(WSCR)is proposed.This model facilitates the comprehensive assessment of...In this paper,a strength-constrained unit commitment(UC)model incorporating system strength constraints based on the weighted short-circuit ratio(WSCR)is proposed.This model facilitates the comprehensive assessment of area-wide system strength in power systems with high inverter-based resource(IBR)penetration,thereby contributing to the mitigation of weak grid issues.Unlike traditional models,this approach considers the interactions among multiple IBRs.The UC problem is initially formulated as a mixed-integer nonlinear programming(MINLP)model,reflecting WSCR and bus impedance matrix modification constraints.To enhance computational tractability,the model is transformed into a mixed-integer linear programming(MILP)form.The effectiveness of the proposed approach is validated through simulations on the IEEE 5-bus,IEEE 39-bus,and a modified Korean power system,demonstrating the ability of the proposed UC model enhancing system strength compared to the conventional methodologies.展开更多
Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple ...Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.展开更多
This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and Herzegovina.The proposed solution is based on a n...This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and Herzegovina.The proposed solution is based on a nonlinear model predictive control algorithm which determines the optimal switching operations of the distribution system.The goal of the control algorithm is to find the optimal radial network topology which minimizes cumulative active power losses and maximizes voltages across the network while simultaneously satisfying all system constraints.The optimization results are validated through multiple simulations(using real power demand data collected for a few characteristic days during winter and summer)which demonstrate the efficiency and usefulness of the developed control algorithm in reducing the grid losses by up to 14%.展开更多
The optimal dispatch of energy storage systems(ESSs)in distribution networks poses significant challenges,primarily due to uncertainties of dynamic pricing,fluctuating demand,and the variability inherent in renewable ...The optimal dispatch of energy storage systems(ESSs)in distribution networks poses significant challenges,primarily due to uncertainties of dynamic pricing,fluctuating demand,and the variability inherent in renewable energy sources.By exploiting the generalization capabilities of deep neural networks(DNNs),the deep reinforcement learning(DRL)algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks.Nevertheless,the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time,which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations.This paper introduces an innovative framework,named mixed-integer programming based deep reinforcement learning(MIP-DRL),to overcome these limitations.The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution.This framework involves training a Q-function with DNNs,which is subsequently represented in a mixed-integer programming(MIP)formulation.This unique combination allows for the seamless integration of operational constraints into the decision-making process.The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations,demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.展开更多
Investors are always willing to receive more data.This has become especially true for the application of modern portfolio theory to the institutional asset allocation process,which requires quantitative estimates of r...Investors are always willing to receive more data.This has become especially true for the application of modern portfolio theory to the institutional asset allocation process,which requires quantitative estimates of risk and return.When long-term data series are unavailable for analysis,it has become common practice to use recent data only.The danger is that these data may not be representative of future performance.Although longer data series are of poorer quality,are difficult to obtain,and may reflect various political and economic regimes,they often paint a very different picture of emerging market performance.This paper presents an application of a stochastic non-linear optimization model of portfolios including transaction costs in the Brazilian financial market.In order to have that,portfolio theory and optimal control were used as theoretical basis.The first strategy tries to allocate the whole available wealth,not considering the risk associated to portfolio(deterministic result).In this case the investor obtained profits of 7.23%a month,taking into account the three risk aversion levels during the whole planning period.On the contrary,the results from the stochastic algorithm obtain profits of 1.34%a month and 18.06%a year,if the investor has low risk aversion.The profits would be 0.88%a month and 11.02%a year for a medium risk aversion investor.And with high risk aversion,the investor obtains 0.62%a month and 7.68%a year.展开更多
基金supported by the National Natural Sci‐ence Foundation of China(Grant No.62306325)。
文摘During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h^(2)/(4√2),where h is the piecewise length.
基金supported by the National Natural Science Fundation of China (60374063)
文摘Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower＇s functions are separable with respect to the follower＇s variables, and the other is that the follower＇s functions are convex if the follower＇s variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader＇s variable x, it is proved that the optimal solution y of the follower＇s mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower＇s programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter λ, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.
基金supported in part by Zhejiang Provincial Key Research and Development Program(2018C01084)Zhejiang Natural Science Foundation(LQ20F020009)Zhejiang Gongshang University,Zhejiang Provincial Key Laboratory of New Network Standards and Technologies(2013E10012)。
文摘Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential complexity.Due to this reason,various MIP methods are proposed for various subclasses of PNs.This work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)PR.In particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution space.Experimental results show that the proposed MIP method has higher computational efficiency.Furthermore,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.
基金Supported by the National Natural Science Foundation of China(10571141,70971109)the Key Projectof the National Natural Science Foundation of China(70531030)
文摘To properly describe and solve complex decision problems,research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important.We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints.The obtained results extend some existing results for continuous quadratic programs,and,more importantly,lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.
文摘In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.
基金financial support from EPSRC grants (EP/M027856/1 EP/M028240/1)
文摘In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onprocess synthesis problems. The algorithms are developed for the special case in which the nonlinearitiesarise because of logarithmic terms, with the first one being developed for the deterministic case, and thesecond for the parametric case （p-MINLP）. The key idea is to formulate and solve the square system of thefirst-order Karush-Kuhn-Tucker （KKT） conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution tech-niques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, twoprocess synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicitfunction of the uncertain parameters.
文摘An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediate-to-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20221165).
文摘With the reform of the power system further deepening,the reliance on electricity and importance attached to the reliable power supply are increasing year by year,and the establishment of a high resilient power system has considerable economic,environmental and social benefits.Reconfiguring the network is one of the well-known tactics to enhance reliability.Accordingly,this paper proposes a reconfiguration method of distribution network considering the enhancement of reliability,which reconfigures the network structure both under normal operation conditions and outage scenarios,and considers factors such as power loss,load distribution and voltage quality considered in conventional reconfiguration methods.In this paper,the reliability assessment is integrated into the process of distribution network reconfiguration by using binary variables to represent the operating state of switchable devices.Based on the concept of fictitious fault flows,the reliability indices of distribution network are linearized expressed,and the network loss is reduced by minimizing the voltage deviation.A mixed integer linear programming(MILP)model is established for distribution network reconfiguration problem,which can guarantee the global optimal solution with high solution efficiency.Finally,the applicability and effectiveness of the proposed method are verified by numerical tests on a 54-node test system.
基金supported by Technology Project of State Grid Tianjin Electric Power Company(2024-06)“Research on hierarchical partition dynamic calculation and panoramic monitoring technology of electric power carbon emission and its application”.
文摘In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation ofmulti-energy parks.Themethod integratesmultienergy complementary scheduling with a tiered carbon trading mechanism to balance operational security,economic efficiency,and environmental objectives.A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment,including photovoltaic systems,ground-source heat pumps,thermal storage electric boilers,combined heat and power units,and electrical energy storage systems.Furthermore,a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices.Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64%reduction in carbon emissions compared to economy-oriented operation,while limiting the increase in operational costs to 20.85%.The carbon-prioritized scenario further reduces emissions by 9.7%,underscoring the inhibitory effect of the tiered carbon pricing mechanism on highcarbon activities.Sensitivity analyses confirm the model’s robustness against fluctuations in energy load,uncertainty in renewable generation,and variations in carbon price.This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks,offering valuable insights for promoting green transformation initiatives.
基金Financial support for this research from the National Natural Science Foundation of China(Nos. 50674088 and 50927403)
文摘Oxygen consumption is an important index of coal oxidation.In order to explore the coal-oxygen reaction,we developed an experimental system of coal spontaneous combustion and tested oxygen consumption of differently ranked coals at programmed temperatures.The size of coal samples ranged from 0.18~0.42 mm and the system heat-rate was 0.8℃/min.The results show that, for high ranked coals,oxygen consumption rises with coal temperature as a piecewise non-linear process.The critical coal temperature is about 50℃.Below this temperature,oxygen consumption decreases with rising coal temperatures and reached a minimum at 50℃,approximately.Subsequently,it begins to increase and the rate of growth clearly increased with temperature.For low ranked coals,this characteristic is inconspicuous or even non-existent.The difference in oxygen consumption at the same temperatures varies for differently ranked coals.The results show the difference in oxygen consumption of the coals tested in our study reached 78.6%at 100℃.Based on the theory of coal-oxygen reaction,these phenomena were analyzed from the point of view of physical and chemical characteristics,as well as the appearance of the coal-oxygen complex.From theoretical analyses and our experiments,we conclude that the oxygen consumption at programmed temperatures reflects the oxidation ability of coals perfectly.
文摘The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming solvers.In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.
基金supported by the Science and Technology Project of State Grid Corporation of China (No.5204JY20000B)。
文摘Micro-phasor measurement units(μPMUs)with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations.Therefore,this paper investigates the problem of how to place a limited number ofμPMUs to improve the state estimation accuracy.Combined with pseudo-measurements and supervisory control and data acquisition(SCADA)measurements,an optimalμPMU placement model is proposed based on a two-step state estimation method.The E-optimal experimental criterion is utilized to measure the state estimation accuracy.The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming(MISDP)problem,whose optimal solution can be obtained by using the improved Benders decomposition method.Simulations on several systems are carried out to evaluate the effective performance of the proposed model.
基金supported by the United States National Science Foundation[grant numbers ECCS-1028870 and ECCS-1509666]and Southern California Edison.
文摘Many important integer and mixed-integer programming problems are difficult to solve.A representative example is unit commitment with combined cycle units and transmission capacity constraints.Complicated transitions within combined cycle units are difficult to follow,and system-wide coupling transmission capacity constraints are difficult to handle.Another example is the quadratic assignment problem.The presence of cross-products in the objective function leads to nonlinearity.In this study,building upon the novel integration of surrogate Lagrangian relaxation and branch-and-cut,such problems will be solved by relaxing selected coupling constraints.Monotonicity of the relaxed problem will be assumed and exploited and nonlinear terms will be dynamically linearised.The linearity of the resulting problem will be exploited using branch-and-cut.To achieve fast convergence,guidelines for selecting stepsizing parameters will be developed.The method opens up directions for solving nonlinear mixed-integer problems,and numerical results indicate that the new method is efficient.
基金This study was supported by the“High level research and training project for professional leaders of teachers in Higher Vocational Colleges in Jiangsu Province”.
文摘In order to improve the performance of time difference of arrival(TDOA)localization,a nonlinear least squares algorithm is proposed in this paper.Firstly,based on the criterion of the minimized sum of square error of time difference of arrival,the location estimation is expressed as an optimal problem of a non-linear programming.Then,an initial point is obtained using the semi-definite programming.And finally,the location is extracted from the local optimal solution acquired by Newton iterations.Simulation results show that when the number of anchor nodes is large,the performance of the proposed algorithm will be significantly better than that of semi-definite programming approach with the increase of measurement noise.
基金partially supported by Korea Electrotechnology Research Institute(KERI)Primary research program through the National Research Council of Science&Technology(NST)funded by the Ministry of Science and ICT(MSIT)(No.25A01038)partially supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00218377).
文摘In this paper,a strength-constrained unit commitment(UC)model incorporating system strength constraints based on the weighted short-circuit ratio(WSCR)is proposed.This model facilitates the comprehensive assessment of area-wide system strength in power systems with high inverter-based resource(IBR)penetration,thereby contributing to the mitigation of weak grid issues.Unlike traditional models,this approach considers the interactions among multiple IBRs.The UC problem is initially formulated as a mixed-integer nonlinear programming(MINLP)model,reflecting WSCR and bus impedance matrix modification constraints.To enhance computational tractability,the model is transformed into a mixed-integer linear programming(MILP)form.The effectiveness of the proposed approach is validated through simulations on the IEEE 5-bus,IEEE 39-bus,and a modified Korean power system,demonstrating the ability of the proposed UC model enhancing system strength compared to the conventional methodologies.
基金Financial support from the National Natural Science Foundation of China under Grant(22393954 and 22078358)is gratefully acknowledged.
文摘Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.
基金supported in part by the European Regional Development Fund under Grant KK.01.1.1.01.0009(DATACROSS).
文摘This paper deals with reduction of losses in electric power distribution system through a dynamic reconfiguration case study of a grid in the city of Mostar,Bosnia and Herzegovina.The proposed solution is based on a nonlinear model predictive control algorithm which determines the optimal switching operations of the distribution system.The goal of the control algorithm is to find the optimal radial network topology which minimizes cumulative active power losses and maximizes voltages across the network while simultaneously satisfying all system constraints.The optimization results are validated through multiple simulations(using real power demand data collected for a few characteristic days during winter and summer)which demonstrate the efficiency and usefulness of the developed control algorithm in reducing the grid losses by up to 14%.
基金supported by the DATALESs project(No.482.20.602)jointly financed by the Netherlands Organization for Scientific Research(NWO)and the National Natural Science Foundation of China.
文摘The optimal dispatch of energy storage systems(ESSs)in distribution networks poses significant challenges,primarily due to uncertainties of dynamic pricing,fluctuating demand,and the variability inherent in renewable energy sources.By exploiting the generalization capabilities of deep neural networks(DNNs),the deep reinforcement learning(DRL)algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks.Nevertheless,the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time,which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations.This paper introduces an innovative framework,named mixed-integer programming based deep reinforcement learning(MIP-DRL),to overcome these limitations.The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution.This framework involves training a Q-function with DNNs,which is subsequently represented in a mixed-integer programming(MIP)formulation.This unique combination allows for the seamless integration of operational constraints into the decision-making process.The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations,demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.
文摘Investors are always willing to receive more data.This has become especially true for the application of modern portfolio theory to the institutional asset allocation process,which requires quantitative estimates of risk and return.When long-term data series are unavailable for analysis,it has become common practice to use recent data only.The danger is that these data may not be representative of future performance.Although longer data series are of poorer quality,are difficult to obtain,and may reflect various political and economic regimes,they often paint a very different picture of emerging market performance.This paper presents an application of a stochastic non-linear optimization model of portfolios including transaction costs in the Brazilian financial market.In order to have that,portfolio theory and optimal control were used as theoretical basis.The first strategy tries to allocate the whole available wealth,not considering the risk associated to portfolio(deterministic result).In this case the investor obtained profits of 7.23%a month,taking into account the three risk aversion levels during the whole planning period.On the contrary,the results from the stochastic algorithm obtain profits of 1.34%a month and 18.06%a year,if the investor has low risk aversion.The profits would be 0.88%a month and 11.02%a year for a medium risk aversion investor.And with high risk aversion,the investor obtains 0.62%a month and 7.68%a year.
