Hydropower station group power generation optimization method based on reservoir regulation coefficient sorting

Abstract

The invention provides a hydropower station group power generation optimization method based on reservoir regulation coefficient sorting. The hydropower station group power generation optimization method comprises the following steps: collecting basic hydrological data of each hydropower station in a to-be-optimized hydropower station group; calculating a reservoir adjustment coefficient of each hydropower station of the hydropower station group according to the basic hydrological data; numbering each hydropower station in the hydropower station group; initializing original output data of each hydropower station in the hydropower station group; generating a corresponding optimization mark variable according to the serial number G of each hydropower station; performing optimization processing on the generated output of each hydropower station in sequence through the objective function to obtain a generated output optimization result; correcting the original output data of the current hydropower station to obtain optimized output data; and outputting the optimized output data of each hydropower station. And the regulation capability of the hydropower station is quantitatively analyzed more finely. The problems that an existing hydropower station group joint optimization scheduling problem is complex in constraint boundary, discretization segmentation of a search space is very difficult, and the solving difficulty of a hydropower station group optimization model is large are solved.

Description

technical field [0001] The present application relates to the technical field of power systems, in particular to a method for optimizing power generation of hydropower station groups based on the ranking of reservoir regulation coefficients. Background technique [0002] In large-scale hydropower systems, due to the differences in river basins and dispatching units, hydropower station clusters are usually managed in different regions or at different levels, but there are often large differences in the adjustment capabilities of the various hydropower station subregions. Roughly dividing the regulation capacity of hydropower stations by "daily regulation, seasonal regulation, and annual regulation" leads to the fact that the optimization results cannot better utilize the multi-time scale regulation capabilities of hydropower station groups. For example, the dimension of decision variables increases linearly with the increase of the number of power stations, and the number of ...

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Problems solved by technology

It solves the problem that the constraint boundary of the joint optimization scheduling problem of the existing hydropower station group is complex, the discretization and segmentation of the search space is very difficult, and the solution of the optimization model of the hydropower station group is difficult

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