This paper proposes a generation dispatch model based on the maximum entropy principle. The objective is to find an optimal generation dispatch strategy that minimizes the generation cost and satisfies the security co...This paper proposes a generation dispatch model based on the maximum entropy principle. The objective is to find an optimal generation dispatch strategy that minimizes the generation cost and satisfies the security constraints of power systems, while taking into account the uncertainty of wind power. Since in many situations, only partial information of the probabilistic variables can be obtained, the maximum entropy principle is introduced to find the most likely realized probability distributions of the power flow, thus providing an accurate probabilistic circumstance to solve the generation dispatch model. The proposed method is tested on the IEEE 39-bus system, and is compared with the methodologies based on Monte Carlo simulation and Gram-Charlier expansions.展开更多
Based on the observation of importance sampling and second order information about the failure surface of a structure, an importance sampling region is defined in V-space which is obtained by rotating a U-space at the...Based on the observation of importance sampling and second order information about the failure surface of a structure, an importance sampling region is defined in V-space which is obtained by rotating a U-space at the point of maximum likelihood. The sampling region is a hyper-ellipsoid that consists of the sampling ellipse on each plane of main curvature in V-space. Thus, the sampling probability density function can be constructed by the sampling region center and ellipsoid axes. Several examples have shown the efficiency and generality of this method.展开更多
Based on our recent study on probability distributions for evolution in extremal optimization (EO),we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) ...Based on our recent study on probability distributions for evolution in extremal optimization (EO),we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) problem,a generalized version of the satisfiability (SAT) problem.The basic idea behind EOSAT is to generalize the evolutionary probability distribution in the Bose-Einstein-EO (BE-EO) algorithm,competing with other popular algorithms such as simulated annealing and WALKSAT.Experimental results on the hard MAXSAT instances from SATLIB show that the modified algorithms are superior to the original BE-EO algorithm.展开更多
文摘This paper proposes a generation dispatch model based on the maximum entropy principle. The objective is to find an optimal generation dispatch strategy that minimizes the generation cost and satisfies the security constraints of power systems, while taking into account the uncertainty of wind power. Since in many situations, only partial information of the probabilistic variables can be obtained, the maximum entropy principle is introduced to find the most likely realized probability distributions of the power flow, thus providing an accurate probabilistic circumstance to solve the generation dispatch model. The proposed method is tested on the IEEE 39-bus system, and is compared with the methodologies based on Monte Carlo simulation and Gram-Charlier expansions.
文摘Based on the observation of importance sampling and second order information about the failure surface of a structure, an importance sampling region is defined in V-space which is obtained by rotating a U-space at the point of maximum likelihood. The sampling region is a hyper-ellipsoid that consists of the sampling ellipse on each plane of main curvature in V-space. Thus, the sampling probability density function can be constructed by the sampling region center and ellipsoid axes. Several examples have shown the efficiency and generality of this method.
基金supported by the National Natural Science Foundation of China (No.61074045)the National Basic Research Program (973) of China (No.2007CB714000)the National Creative Research Groups Science Foundation of China (No.60721062)
文摘Based on our recent study on probability distributions for evolution in extremal optimization (EO),we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) problem,a generalized version of the satisfiability (SAT) problem.The basic idea behind EOSAT is to generalize the evolutionary probability distribution in the Bose-Einstein-EO (BE-EO) algorithm,competing with other popular algorithms such as simulated annealing and WALKSAT.Experimental results on the hard MAXSAT instances from SATLIB show that the modified algorithms are superior to the original BE-EO algorithm.
