This paper proposes an innovative supervision method that can provide project supervisors with realtime supervision of engineering projects and contractor activity. To obtain real-time and comprehensive state of proje...This paper proposes an innovative supervision method that can provide project supervisors with realtime supervision of engineering projects and contractor activity. To obtain real-time and comprehensive state of project, we use grid management to divide the project supervision grid in three levels: stage, objective, and milestone. Then, a detailed supervision mechanism is designed to help supervisors measure the project situation in real time. This mechanism checks that if the project objectives(such as schedule, cost, quality, and safety) in every supervision grid cell are under the healthy limits, any project deviation can be identified as soon as possible.A schedule objective is selected as an example to illustrate the method used to calculate the healthy limit.展开更多
Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment fr...Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.展开更多
基金the National Natural Science Foundation of China(No.71271085)the Beijing "12th Five-Year Plan" Project of Philosophy and Social Sciences(No.12JGB044)
文摘This paper proposes an innovative supervision method that can provide project supervisors with realtime supervision of engineering projects and contractor activity. To obtain real-time and comprehensive state of project, we use grid management to divide the project supervision grid in three levels: stage, objective, and milestone. Then, a detailed supervision mechanism is designed to help supervisors measure the project situation in real time. This mechanism checks that if the project objectives(such as schedule, cost, quality, and safety) in every supervision grid cell are under the healthy limits, any project deviation can be identified as soon as possible.A schedule objective is selected as an example to illustrate the method used to calculate the healthy limit.
基金The authors would like to thank Dr.-Ing.Nils Bornhorst for the fruitful discussion.The publication and development of this work was funded by the Hessian Ministry of Higher Education,Research,Science and the Arts,Germany through the K-ES project under reference number:511/17.001.
文摘Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.
