A method and device for unit combination optimization based on vertical and horizontal cross algorithm

Abstract

Set assembly optimization method and set assembly optimization device based on a crisscross algorithm The method comprises the following steps of establishing an optimization model of an electric power system set assembly based on a target function with lowest electric power system operation cost and an electric power system set restriction; initializing a group according to a latent solution of the optimization model, and obtaining an initial set state matrix and an initial set output matrix; based on the initial set state matrix, iteratively performing a horizontal crossing operation and a vertical crossing operation by means of a binary crisscross algorithm BCSO and the crisscross algorithm CSO, and making the generated filial generation compete with the parent, thereby obtaining an optimized solution of the electric power system set assembly. The set assembly optimization method provided by an embodiment of the invention can realize quick propagation of particle individual information in the group, great improvement of global convergence capability and effective convergence speed keeping. The invention further discloses a set assembly optimization device based on the crisscross algorithm, wherein the device has a corresponding technical effect.

Description

technical field [0001] The invention relates to the field of electric power technology, in particular to a method and device for unit combination optimization based on a vertical-horizontal algorithm. Background technique [0002] In power system, unit combination (UC) problem is one of the important problems. The unit combination problem is a combination optimization problem. The main task is to determine the start-up and shutdown time of the units and the economic distribution of load, so that the total operating cost is the lowest under the premise of meeting the forecasted load demand. [0003] Due to a series of unit and system constraints such as unit capacity constraints, ramp constraints, spinning reserve constraints, and minimum start and stop time constraints, the unit combination problem becomes a complex, large-scale, dynamic, mixed integer nonlinear problem. [0004] In recent years, with the rapid development of intelligent algorithms, heuristic search technol...

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Problems solved by technology

[0005] The above methods can solve the problem of a small number of unit combinations to a certain extent, but for the large-scale unit combination problem in the power system, these optimization methods are prone to fall into the curse of dimensionality, and are prone to the problem of sacrificing the convergence speed while improving the global convergence ability

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