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We present a hybrid metaheuristic optimization algorithm for solving economic dispatch problems in power systems. The proposed algorithm, based on bat algorithm, combines chaotic map and random black hole model together. Chaotic map is used to prevent premature convergence, and the random black hole model is helpful not only in avoiding premature convergence, but also in increasing the global search ability, enlarging exploitation area and accelerating convergence speed. The pseudocode and related parameters of the proposed algorithm are also given in this paper. Different from other related works, the costs of conventional thermal generators and random wind power are both included in the cost function because of the increasing penetration of wind power. The proposed algorithm has no requirement on the convexity or continuous differentiability of the cost function, although the effect on fuel cost, caused by the underestimation and overestimation of wind power, is included. This makes it feasible to take more practical nonlinear constraints into account, such as prohibited operating zones and ramp rate limits. Three test cases are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Economic dispatch can promote the economic operation of power systems, and reactive power dispatch can ensure the safe and reliable operation of power systems. Essentially different from the related literature with separately optimizing the two dispatches, this paper proposes the collaborative optimization strategy of dynamic grid dispatch to optimize economic dispatch and reactive power dispatch simultaneously. The strategy is devoted to analyzing the internal relationship of the two dispatches, and establishing a collaborative optimization model to complete the collaborative optimization of the two dispatches. Moreover, multi-objective hybrid bat algorithm is improved by an unbalanced power distribution method, which is suitable to solve dynamic grid dispatch problem. Finally, wind power predicted by neural network is integrated in the collaborative optimization of dynamic grid dispatch, and the influence of wind power integration on grid dispatch is studied. The numerical examples on the IEEE 30-bus are provided to demonstrate the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]