With the development of Intemet and Web service technology, Web service composition has been an effective way to construct software applications; service selection is the crucial element in the composition process. Ho...With the development of Intemet and Web service technology, Web service composition has been an effective way to construct software applications; service selection is the crucial element in the composition process. However, the existing selection methods mostly generate static plans since they neglect the inherent stochastic and dynamic nature of Web services. As a result, Web service composition often inevitably terminates with failure. An indeterminacy-aware service selection algorithm based on an improved Markov decision process （IMDP） has been designed for reliable service composition, but it suffers from higher computation complexity. Therefore, an efficient method is proposed, which can reduce the computation cost by converting the service selection problem based on IMDP into solving a nonhomogeneous linear equation set. Experimental results demonstrate the success rate of service composition has been improved greatly, whilst also reducing computation cost.展开更多
This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system.A two-level model that solves the allocation...This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system.A two-level model that solves the allocation problem is presented.The upper model allocates operation reserve among subsystems from the economic point of view.In the upper model,transmission constraints of tielines are formulated to represent limited reserve support from the neighboring system due to wind power fluctuation.The lower model evaluates the system on the reserve schedule from the reliability point of view.In the lower model,the reliability evaluation of composite power system is performed by using Monte Carlo simulation in a multi-area system.Wind power prediction errors and tieline constraints are incorporated.The reserve requirements in the upper model are iteratively adjusted by the resulting reliability indices from the lowermodel.Thus,the reserve allocation is gradually optimized until the system achieves the balance between reliability and economy.A modified two-area reliability test system (RTS) is analyzed to demonstrate the validity of the method.展开更多
文摘With the development of Intemet and Web service technology, Web service composition has been an effective way to construct software applications; service selection is the crucial element in the composition process. However, the existing selection methods mostly generate static plans since they neglect the inherent stochastic and dynamic nature of Web services. As a result, Web service composition often inevitably terminates with failure. An indeterminacy-aware service selection algorithm based on an improved Markov decision process （IMDP） has been designed for reliable service composition, but it suffers from higher computation complexity. Therefore, an efficient method is proposed, which can reduce the computation cost by converting the service selection problem based on IMDP into solving a nonhomogeneous linear equation set. Experimental results demonstrate the success rate of service composition has been improved greatly, whilst also reducing computation cost.
基金supported by National Natural Science Foundation of China(No.51277141)National High Technology Research and Development Program of China(863 Program)(No.2011AA05A103)
文摘This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system.A two-level model that solves the allocation problem is presented.The upper model allocates operation reserve among subsystems from the economic point of view.In the upper model,transmission constraints of tielines are formulated to represent limited reserve support from the neighboring system due to wind power fluctuation.The lower model evaluates the system on the reserve schedule from the reliability point of view.In the lower model,the reliability evaluation of composite power system is performed by using Monte Carlo simulation in a multi-area system.Wind power prediction errors and tieline constraints are incorporated.The reserve requirements in the upper model are iteratively adjusted by the resulting reliability indices from the lowermodel.Thus,the reserve allocation is gradually optimized until the system achieves the balance between reliability and economy.A modified two-area reliability test system (RTS) is analyzed to demonstrate the validity of the method.
