飞机货舱中非集装器的配载是重要的运输环节,而如何保障非集装器的配载,是亟须研究的重要内容。其中二维矩形切割排样问题是解决非集装器运输的重要理论方法。二维矩形切割排样理论在原材料切割、装箱等问题中有着广泛应用,但尚无很好...飞机货舱中非集装器的配载是重要的运输环节,而如何保障非集装器的配载,是亟须研究的重要内容。其中二维矩形切割排样问题是解决非集装器运输的重要理论方法。二维矩形切割排样理论在原材料切割、装箱等问题中有着广泛应用,但尚无很好的求解算法。该方法会因求解速度而拖累整个实际生产作业进度。因此,本文提出了二维切割排样的混合整数线性规划(Mixed-integer linear programming,MILP)模型,模型目标是以矩形板面积利用率和切割排样价值最大为目标,模型考虑了不超边界、不重叠、可正交旋转等限制。设计了启发式分组策略的求解算法:首先基于启发式把矩形块分组为不同组别的小矩形块,降低变量和计算规模;其次,采用混合整数规划精确算法对每个小矩形块进行切割排样。以经典Benchmark实验数据为例,将Gurobi分组与Gurobi、CutLogic2D、基于遗传算法和最低水平线算法的混合算法对比。实验结果表明,CutLogic2D综合求解质量和速度较好;Gurobi分组方法是一种启发式算法,总体上要稍差于CutLogic2D;遗传算法和最低水平线算法因是启发式算法且未使用分组策略,和Gurobi分别在部分算例上求解时间相对较长,达到了7200 s,这是无法接受的。展开更多
Oil depots along products pipelines are important components of the pipeline transportation system and down-stream markets.The operating costs of oil depots account for a large proportion of the total system’s operat...Oil depots along products pipelines are important components of the pipeline transportation system and down-stream markets.The operating costs of oil depots account for a large proportion of the total system’s operating costs.Meanwhile,oil depots and pipelines form an entire system,and each operation in a single oil depot may have influence on others.It is a tough job to make a scheduling plan when considering the factors of delivering contaminated oil and batches migration.So far,studies simultaneously considering operating constraints and contaminated oil issues are rare.Aiming at making a scheduling plan with the lowest operating costs,the paper establishes a mixed-integer linear programming model,considering a sequence of operations,such as delivery, export, blending,fractionating and exchanging operations,and batch property differences of the same oil as well as influence of batch migration on contaminated volume.Moreover,the paper verifies the linear relationship between oil concentration and blending capability by mathematical deduction.Finally,the model is successfully applied to one of the product pipelines in China and proved to be practical.展开更多
This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns...This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.展开更多
Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as ...Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.展开更多
在分析典型冷热电联供(combined cooling,heat and power,CCHP)系统的基础上,提出描述其组成和结构的母线式结构,并围绕该系统结构设计了微网调度优化模型构架。在该结构中,选取电气、烟气、蒸汽、热水、空气作为基本母线,与源、负荷、...在分析典型冷热电联供(combined cooling,heat and power,CCHP)系统的基础上,提出描述其组成和结构的母线式结构,并围绕该系统结构设计了微网调度优化模型构架。在该结构中,选取电气、烟气、蒸汽、热水、空气作为基本母线,与源、负荷、储能和转换装置联接形成微网。使用该结构对各设备进行独立建模,有助于CCHP系统的灵活配置和通用建模。围绕该结构,建立联供型微网日前动态经济调度的0-1混合整数线性规划模型,最后通过测试算例证实了所提框架的合理性和有效性。展开更多
综合能源系统(integrated energy system,IES)是未来能源消费方式的重要发展方向,实现多区域IES的协同规划与调度对进一步提高其经济效益和环境效益至关重要,为此首要的问题是区域冷、热网的建模。文中基于传热学的基本原理建立了区域...综合能源系统(integrated energy system,IES)是未来能源消费方式的重要发展方向,实现多区域IES的协同规划与调度对进一步提高其经济效益和环境效益至关重要,为此首要的问题是区域冷、热网的建模。文中基于传热学的基本原理建立了区域热网能量传输通用模型,推导出热网热损方程,对其线性化得到热网能量流模型,同时推导了用于求解热网潮流(热媒流量、温度)的网络流量–温度基本方程。在冷热电联供系统运行优化模型基础上建立了含有热网的多区域IES优化混合整数线性规划模型。通过算例验证了模型的有效性。展开更多
The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manag...The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).展开更多
Although power grids have become safer with increased situational awareness,major extreme events still pose reliability and resilience challenges,primarily at the distribution level,due to increased vulnerabilities an...Although power grids have become safer with increased situational awareness,major extreme events still pose reliability and resilience challenges,primarily at the distribution level,due to increased vulnerabilities and limited recovery resources.Information and communication technologies(ICTs)have introduced new vulnerabilities that have been widely investigated in previous studies.These vulnerabilities include remote device failures,communication channel disturbances,and cyberattacks.However,only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat.This paper proposes a communication-aware restoration approach of smart distribution grids,which leverages power-telecom interdependencies to determine the optimal restoration strategies.The states of grid-energized telecom points are tracked to provide the best restoration actions,which are enabled through the resilience resources of repair,manual switching,remote reconfiguration,and distributed generators.As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies,different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations.System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming.Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources.The existing disparity between overhead and underground power line configurations is also quantified.展开更多
The increasing number of distributed energy resources(DERs),advancing communication and computation technologies,and reliability concerns of the customers have caused an intense interest in the concept of microgrid.Al...The increasing number of distributed energy resources(DERs),advancing communication and computation technologies,and reliability concerns of the customers have caused an intense interest in the concept of microgrid.Although DERs are the biggest motivation of the microgrids due to their intermittent generation characteristics,they constitute a risk for system reliability.Battery storage systems(BSSs)stand as one of the most effective solutions for this reliability problem.However,the inappropriate use of BSS creates other operational problems in power systems.In order to deal with these concerns explicitly in microgrids,an optimized microgrid central controller(MGCC)is the key factor,which controls the realtime operation of a microgrid.This work proposes a model predictive control(MPC)based MGCC that will provide optimal control of the microgrid,considering economic and operational constraints.The proposed system will minimize the energy cost of the microgrid by utilizing mixed-integer linear programming(MILP)assuming the presence of DERs and BSS as well as the bi-directional grid connection.Moreover,the aging effect of BSS will be considered in the proposed optimization problem which will provide an up-to-date system model.The proposed method is evaluated using real load and photovoltaic(PV)generation data.展开更多
With the increasing interdependence of various energy carriers,the operation of power systems is found to correlate closely with the limitations on the other energy infrastructures.This paper presents a mixed-integer ...With the increasing interdependence of various energy carriers,the operation of power systems is found to correlate closely with the limitations on the other energy infrastructures.This paper presents a mixed-integer linear programming(MILP)model for the microgrid(MG)optimal scheduling considering technical and economic ties between electricity and natural gas(NG)systems.In the proposed methodology,different energy converters and storages,including combined heat and power(CHP)units,electricity/heat storage units,and distributed energy resources(DERs)are considered.The proposed model allows the MG operator to minimize the operation cost of the MG while different operational limitations on the energy hub are satisfied.The model is developed based on AC power flow constraints so as to respect reactive power and voltage security constraints.The efficiency and robustness of the proposed MILP formulation are successfully verified using a large-scale test MG.展开更多
Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance...Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.展开更多
Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges ...Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.展开更多
This paper analyzes the oligopolistic equilibria of multiple price-maker agents in performance-based regulation(PBR)markets.In these markets,there are price-maker agents representing some frequency regulation(FR)provi...This paper analyzes the oligopolistic equilibria of multiple price-maker agents in performance-based regulation(PBR)markets.In these markets,there are price-maker agents representing some frequency regulation(FR)providers and a number of independent price-taker FR providers.A model of equilibrium problem with equilibrium constraints(EPECs)is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and price-taker FR providers.Due to the incorporation of the FR providers’dynamics,the proposed model is reformulated as a mixed-integer linear programming(MILP)problem over innovative mathematical techniques.An optimal equilibrium point is also selected for the market,where none of the agents is the unique deviator and the dynamic performance of power system is improved simultaneously.The effectiveness of the proposed optimal equilibrium point is evaluated by comparing the outputs with the conventional optimal dispatches of the FR providers.展开更多
文摘飞机货舱中非集装器的配载是重要的运输环节,而如何保障非集装器的配载,是亟须研究的重要内容。其中二维矩形切割排样问题是解决非集装器运输的重要理论方法。二维矩形切割排样理论在原材料切割、装箱等问题中有着广泛应用,但尚无很好的求解算法。该方法会因求解速度而拖累整个实际生产作业进度。因此,本文提出了二维切割排样的混合整数线性规划(Mixed-integer linear programming,MILP)模型,模型目标是以矩形板面积利用率和切割排样价值最大为目标,模型考虑了不超边界、不重叠、可正交旋转等限制。设计了启发式分组策略的求解算法:首先基于启发式把矩形块分组为不同组别的小矩形块,降低变量和计算规模;其次,采用混合整数规划精确算法对每个小矩形块进行切割排样。以经典Benchmark实验数据为例,将Gurobi分组与Gurobi、CutLogic2D、基于遗传算法和最低水平线算法的混合算法对比。实验结果表明,CutLogic2D综合求解质量和速度较好;Gurobi分组方法是一种启发式算法,总体上要稍差于CutLogic2D;遗传算法和最低水平线算法因是启发式算法且未使用分组策略,和Gurobi分别在部分算例上求解时间相对较长,达到了7200 s,这是无法接受的。
基金part of the Program of ‘‘Study of the mechanism of complex heat and mass transfer during batch transport process in product pipelines’’ funded under the National Natural Science Foundation of China, Grant Number 51474228
文摘Oil depots along products pipelines are important components of the pipeline transportation system and down-stream markets.The operating costs of oil depots account for a large proportion of the total system’s operating costs.Meanwhile,oil depots and pipelines form an entire system,and each operation in a single oil depot may have influence on others.It is a tough job to make a scheduling plan when considering the factors of delivering contaminated oil and batches migration.So far,studies simultaneously considering operating constraints and contaminated oil issues are rare.Aiming at making a scheduling plan with the lowest operating costs,the paper establishes a mixed-integer linear programming model,considering a sequence of operations,such as delivery, export, blending,fractionating and exchanging operations,and batch property differences of the same oil as well as influence of batch migration on contaminated volume.Moreover,the paper verifies the linear relationship between oil concentration and blending capability by mathematical deduction.Finally,the model is successfully applied to one of the product pipelines in China and proved to be practical.
文摘This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.
基金supported by the National Natural Science Foundation of China(72201229,72025103,72394360,72394362,72361137001,72071173,and 71831008).
文摘Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.
文摘在分析典型冷热电联供(combined cooling,heat and power,CCHP)系统的基础上,提出描述其组成和结构的母线式结构,并围绕该系统结构设计了微网调度优化模型构架。在该结构中,选取电气、烟气、蒸汽、热水、空气作为基本母线,与源、负荷、储能和转换装置联接形成微网。使用该结构对各设备进行独立建模,有助于CCHP系统的灵活配置和通用建模。围绕该结构,建立联供型微网日前动态经济调度的0-1混合整数线性规划模型,最后通过测试算例证实了所提框架的合理性和有效性。
文摘综合能源系统(integrated energy system,IES)是未来能源消费方式的重要发展方向,实现多区域IES的协同规划与调度对进一步提高其经济效益和环境效益至关重要,为此首要的问题是区域冷、热网的建模。文中基于传热学的基本原理建立了区域热网能量传输通用模型,推导出热网热损方程,对其线性化得到热网能量流模型,同时推导了用于求解热网潮流(热媒流量、温度)的网络流量–温度基本方程。在冷热电联供系统运行优化模型基础上建立了含有热网的多区域IES优化混合整数线性规划模型。通过算例验证了模型的有效性。
文摘The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).
基金supported by EDF/Orange/SNCF in the framework of the Chair on Risk and Resilience of Complex Systems(CentraleSupelec,EDF,Orange,SNCF).
文摘Although power grids have become safer with increased situational awareness,major extreme events still pose reliability and resilience challenges,primarily at the distribution level,due to increased vulnerabilities and limited recovery resources.Information and communication technologies(ICTs)have introduced new vulnerabilities that have been widely investigated in previous studies.These vulnerabilities include remote device failures,communication channel disturbances,and cyberattacks.However,only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat.This paper proposes a communication-aware restoration approach of smart distribution grids,which leverages power-telecom interdependencies to determine the optimal restoration strategies.The states of grid-energized telecom points are tracked to provide the best restoration actions,which are enabled through the resilience resources of repair,manual switching,remote reconfiguration,and distributed generators.As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies,different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations.System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming.Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources.The existing disparity between overhead and underground power line configurations is also quantified.
文摘The increasing number of distributed energy resources(DERs),advancing communication and computation technologies,and reliability concerns of the customers have caused an intense interest in the concept of microgrid.Although DERs are the biggest motivation of the microgrids due to their intermittent generation characteristics,they constitute a risk for system reliability.Battery storage systems(BSSs)stand as one of the most effective solutions for this reliability problem.However,the inappropriate use of BSS creates other operational problems in power systems.In order to deal with these concerns explicitly in microgrids,an optimized microgrid central controller(MGCC)is the key factor,which controls the realtime operation of a microgrid.This work proposes a model predictive control(MPC)based MGCC that will provide optimal control of the microgrid,considering economic and operational constraints.The proposed system will minimize the energy cost of the microgrid by utilizing mixed-integer linear programming(MILP)assuming the presence of DERs and BSS as well as the bi-directional grid connection.Moreover,the aging effect of BSS will be considered in the proposed optimization problem which will provide an up-to-date system model.The proposed method is evaluated using real load and photovoltaic(PV)generation data.
文摘With the increasing interdependence of various energy carriers,the operation of power systems is found to correlate closely with the limitations on the other energy infrastructures.This paper presents a mixed-integer linear programming(MILP)model for the microgrid(MG)optimal scheduling considering technical and economic ties between electricity and natural gas(NG)systems.In the proposed methodology,different energy converters and storages,including combined heat and power(CHP)units,electricity/heat storage units,and distributed energy resources(DERs)are considered.The proposed model allows the MG operator to minimize the operation cost of the MG while different operational limitations on the energy hub are satisfied.The model is developed based on AC power flow constraints so as to respect reactive power and voltage security constraints.The efficiency and robustness of the proposed MILP formulation are successfully verified using a large-scale test MG.
基金supported in part by the Department of Energy(No.DE-AR-0001001,No.DE-EE0009355)the National Science Foundation(NSF)(No.ECCS-2145063)。
文摘Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input measurements.It has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)problems.The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted.The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.
基金Project supported by the National Natural Science Foundation of China(No.U21A20451)。
文摘Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.
文摘This paper analyzes the oligopolistic equilibria of multiple price-maker agents in performance-based regulation(PBR)markets.In these markets,there are price-maker agents representing some frequency regulation(FR)providers and a number of independent price-taker FR providers.A model of equilibrium problem with equilibrium constraints(EPECs)is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and price-taker FR providers.Due to the incorporation of the FR providers’dynamics,the proposed model is reformulated as a mixed-integer linear programming(MILP)problem over innovative mathematical techniques.An optimal equilibrium point is also selected for the market,where none of the agents is the unique deviator and the dynamic performance of power system is improved simultaneously.The effectiveness of the proposed optimal equilibrium point is evaluated by comparing the outputs with the conventional optimal dispatches of the FR providers.
