As a physical model, the cellular automata （CA） model is widely used in many areas, such as stair evacuation. However, existing CA models do not consider evacuees＇ walk preferences nor psychological status, and the...As a physical model, the cellular automata （CA） model is widely used in many areas, such as stair evacuation. However, existing CA models do not consider evacuees＇ walk preferences nor psychological status, and the structure of the basic model is unapplicable for the stair structure. This paper is to improve the stair evacuation simulation by addressing these issues, and a new cellular automata model is established. Several evacuees＇ walk preference and how evacuee＇s psychology influences their behaviors are introduced into this model. Evacuees＇ speeds will be influenced by these features. To validate this simulation, two fire drills held in two high-rise buildings are video-recorded. It is found that the simulation results are similar to the fire drill results. The structure of this model is simple, and it is easy to further develop and utilize in different buildings with various kinds of occupants.展开更多
Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind.Recently,we developed a hybrid Markovian and interval approach for the unit commitment...Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind.Recently,we developed a hybrid Markovian and interval approach for the unit commitment and economic dispatch problem where power generation of conventional units is linked to local wind states to dampen the effects of wind uncertainties.Also,to reduce complexity,extreme and expected states are considered as interval modeling.Although this approach is effective,the fact that major wind farms are often located in remote locations and not accompanied by conventional units leads to conservative results.Furthermore,weights of extreme and expected states in the objective function are difficult to tune,resulting in significant differences between optimization and simulation costs.In this paper,each remote wind farm is paired with a conventional unit to dampen the effects of wind uncertainties without using expensive utility-scaled battery storage,and extra constraints are innovatively established to model pairing.Additionally,proper weights are derived through a novel quadratic fit of cost functions.The problem is solved by using a creative integration of our recent surrogate Lagrangian relaxation and branch-and-cut.Results demonstrate modeling accuracy,computational efficiency,and significant reduction of conservativeness of the previous approach.展开更多
Federated-learning-based active fault management(AFM)is devised to achieve real-time safety assurance for microgrids and the main grid during faults.AFM was originally formulated as a distributed optimization problem....Federated-learning-based active fault management(AFM)is devised to achieve real-time safety assurance for microgrids and the main grid during faults.AFM was originally formulated as a distributed optimization problem.Here,federated learning is used to train each microgrid’s network with training data achieved from distributed optimization.The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm.The replacement transfers computation from online to offline.With this replacement,the control algorithm can meet real-time requirements for a system with dozens of microgrids.By contrast,distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids.More microgrids,however,lead to more computation time with optimization-based method.Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids.Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.展开更多
With time-based competition and rapid technology advancements, effective manufacturing scheduling and supply chain coordination are critical to quickly respond to changing market conditions. These problems, however, a...With time-based competition and rapid technology advancements, effective manufacturing scheduling and supply chain coordination are critical to quickly respond to changing market conditions. These problems, however, are difficult in view of inherent complexity and various uncertainties involved. Based on a series of results by the authors, decomposition and coordination by using Lagrangian relaxation is identified in this paper as an effective way to control complexity and uncertainty. A manufacturing scheduling problem is first formulated within the job shop context with uncertain order arrivals, processing times, due dates, and part priorities as a separable optimization problem. A solution methodology that combines Lagrangian relaxation, stochastic dynamic programming, and heuristics is developed. Method improvements to effectively solve large problems are also highlighted. To extend manufacturing scheduling within a factory to coordinate autonomic members across chains of suppliers, a decentralized supply chain model is established in the second half of this paper. By relaxing cross-member constraints, the model is decomposed into member-wise subproblems, and a nested optimization structure is developed based on the job shop scheduling results. Coordination is performed through the iterative updating of cross-member prices without accessing other members' private information or intruding their decision-making authorities, either with or without a coordinator. Two examples are presented to demonstrate the effectiveness of the method. Future prospects to overcome problem inseparability and improve computing efficiency are then discussed.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
Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major...Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major portion of the energy consumption.Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.In this paper,the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied.The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load,and the air temperature and flow rate.A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants.The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load.The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control(SPC)for measuring and analyzing their variations for fault detection and diagnosis.The gray-box model in the method,however,requires that the air temperature and flow rate be nearly constant.By introducing two new parameters and deleting data points with low air flow rate,the requirement can be satisfied and the method can then be applicable for an air-cooled chiller.The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis(FDD)performance greatly.It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.展开更多
基金Project supported by the National Basic Research Program of China(Grant No.2012CB719705)the National Natural Science Foundation of China(Grant Nos.91224008,91024032,and 71373139)
文摘As a physical model, the cellular automata （CA） model is widely used in many areas, such as stair evacuation. However, existing CA models do not consider evacuees＇ walk preferences nor psychological status, and the structure of the basic model is unapplicable for the stair structure. This paper is to improve the stair evacuation simulation by addressing these issues, and a new cellular automata model is established. Several evacuees＇ walk preference and how evacuee＇s psychology influences their behaviors are introduced into this model. Evacuees＇ speeds will be influenced by these features. To validate this simulation, two fire drills held in two high-rise buildings are video-recorded. It is found that the simulation results are similar to the fire drill results. The structure of this model is simple, and it is easy to further develop and utilize in different buildings with various kinds of occupants.
基金supported in part by the Project Funded by ABB and U.S.National Science Foundation(ECCS-1509666)
文摘Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind.Recently,we developed a hybrid Markovian and interval approach for the unit commitment and economic dispatch problem where power generation of conventional units is linked to local wind states to dampen the effects of wind uncertainties.Also,to reduce complexity,extreme and expected states are considered as interval modeling.Although this approach is effective,the fact that major wind farms are often located in remote locations and not accompanied by conventional units leads to conservative results.Furthermore,weights of extreme and expected states in the objective function are difficult to tune,resulting in significant differences between optimization and simulation costs.In this paper,each remote wind farm is paired with a conventional unit to dampen the effects of wind uncertainties without using expensive utility-scaled battery storage,and extra constraints are innovatively established to model pairing.Additionally,proper weights are derived through a novel quadratic fit of cost functions.The problem is solved by using a creative integration of our recent surrogate Lagrangian relaxation and branch-and-cut.Results demonstrate modeling accuracy,computational efficiency,and significant reduction of conservativeness of the previous approach.
基金supported in part by the National Science Foundation under Grants No.OIA-2134840 and ECCS-1810108in part by Department of Energy under Grant No.DE-EE0009341Department of Navy award N00014-20-1-2858 issued by the Office of Naval Research.
文摘Federated-learning-based active fault management(AFM)is devised to achieve real-time safety assurance for microgrids and the main grid during faults.AFM was originally formulated as a distributed optimization problem.Here,federated learning is used to train each microgrid’s network with training data achieved from distributed optimization.The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm.The replacement transfers computation from online to offline.With this replacement,the control algorithm can meet real-time requirements for a system with dozens of microgrids.By contrast,distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids.More microgrids,however,lead to more computation time with optimization-based method.Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids.Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.
基金This work was supported in part by the National Science Foundation under DMI-0223443by a contract from the United Technologies Research Center,USA.
文摘With time-based competition and rapid technology advancements, effective manufacturing scheduling and supply chain coordination are critical to quickly respond to changing market conditions. These problems, however, are difficult in view of inherent complexity and various uncertainties involved. Based on a series of results by the authors, decomposition and coordination by using Lagrangian relaxation is identified in this paper as an effective way to control complexity and uncertainty. A manufacturing scheduling problem is first formulated within the job shop context with uncertain order arrivals, processing times, due dates, and part priorities as a separable optimization problem. A solution methodology that combines Lagrangian relaxation, stochastic dynamic programming, and heuristics is developed. Method improvements to effectively solve large problems are also highlighted. To extend manufacturing scheduling within a factory to coordinate autonomic members across chains of suppliers, a decentralized supply chain model is established in the second half of this paper. By relaxing cross-member constraints, the model is decomposed into member-wise subproblems, and a nested optimization structure is developed based on the job shop scheduling results. Coordination is performed through the iterative updating of cross-member prices without accessing other members' private information or intruding their decision-making authorities, either with or without a coordinator. Two examples are presented to demonstrate the effectiveness of the method. Future prospects to overcome problem inseparability and improve computing efficiency are then discussed.
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.
文摘Buildings worldwide account for nearly 40%of global energy consumption.The biggest energy consumer in buildings is the heating,ventilation and air conditioning(HVAC)systems.In HVAC systems,chillers account for a major portion of the energy consumption.Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.In this paper,the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied.The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load,and the air temperature and flow rate.A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants.The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load.The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control(SPC)for measuring and analyzing their variations for fault detection and diagnosis.The gray-box model in the method,however,requires that the air temperature and flow rate be nearly constant.By introducing two new parameters and deleting data points with low air flow rate,the requirement can be satisfied and the method can then be applicable for an air-cooled chiller.The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis(FDD)performance greatly.It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.
