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In solving the electrical power systems dynamic economic dispatch problem, the goal is to find the optimal allocation of output power among the various generators available to serve the system load. However, new challenges about dynamic economic dispatch arise with large amounts of wind power integrated into the system. In this article, a dynamic economic dispatch model with wind power is formulated first, and then an improved particle swarm optimization approach is developed for solving the dynamic economic dispatch problem. In the optimization model, the constraints of up-spinning reserve and down-spinning reserve are introduced to deal with the influence of wind power on dynamic economic dispatch, and valve point effect is taken into account in the objective function. The proposed method combines a solutionsharing strategy with an elitist learning strategy based on the basic particle swarm optimization. The effectiveness of the proposed approach is demonstrated by comparing its performance with other approaches, including basic particle swarm optimization and the genetic algorithm. The simulation results show that the proposed method has good convergence and great economic effect. All simulations are conducted based on the 6-unit system and the 15-unit system.

In this paper, we propose a novel algorithm to learn a Büchi automaton from a teacher who knows an ω-regular language. The algorithm is based on learning a formalism named family of DFAs (FDFAs) recently proposed by An-gluin and Fisman [10]. The main catch is that we use a classification tree structure instead of the standard observation table structure. The worst case storage space required by our algorithm is quadratically better than the table-based algorithm proposed in [10]. We implement the first publicly available library ROLL (Reg-ular Omega Language Learning), which consists of all ω-regular learning algorithms available in the literature and the new algorithms proposed in this paper. Experimental results show that our tree-based algorithms have the best performance among others regarding the number of solved learning tasks.

This paper considers the stochastic point location (SPL) problem as a learning mechanism trying to locate a point on a real line via interacting with a random environment. Compared to the stochastic environment in the literatures that confines the learning mechanism to moving in two directions, i.e., left or right, this paper introduces a general triple level stochastic environment which not only tells the learning mechanism to go left or right, but also informs it to stay unmoved. It is easy to understand, as we will prove in this paper, that the environment reported in the previous literatures is just a special case of the triple level environment. And a new learning algorithm, named as random walk-based triple level learning algorithm, is proposed to locate an unknown point under this new type of environment. In order to examine the performance of this algorithm, we divided the triple level SPL problems into four distinguished scenarios by the properties of the unknown point and the stochastic environment, and proved that even under the triple level nonstationary environment and the convergence condition having not being satisfied for some time, which are rarely considered in existing SPL problems, the proposed learning algorithm is still working properly whenever the unknown point is static or evolving with time. Extensive experiments validate our theoretical analyses and demonstrate that the proposed learning algorithms are quite effective and efficient.

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