Dispatching function optimization method based on multi-objective genetic algorithm

Abstract

The invention relates to a dispatching function optimization method based on a multi-objective genetic algorithm. The method comprises the following steps of: setting the form of a dispatching function; setting decision variables; and calculating dispatching rules by multi-objective particle swarm optimization (MOPSO), wherein the calculation process by the multi-objective particle swarm optimization (MOPSO) comprises the steps of performing initialization, creating an external Archive and creating a copy in the Archive according to position information of non-dominated particles in popVar, and iterating. According to the method disclosed by the invention, the particle swarm optimization is adopted for replacing linear regression. The algorithm has the advantages of simple flow process, easiness in implementation, few algorithm parameters, fast convergence speed and the like and is further widely adopted. By applying the method to the generation of a reservoir dispatching function, the established dispatching function can be utilized for optimizing optimal parameters and the reservoir optimal dispatching function can be further obtained.

Description

technical field [0001] The invention relates to a scheduling function optimization method based on a multi-objective genetic algorithm, which is a cascade reservoir group optimization scheduling method, and a cascade reservoir group scheduling function optimization model construction and solution method considering multi-objective optimization. Background technique [0002] At present, most of the optimal dispatching of cascade reservoirs studied at home and abroad is to establish an optimization model with a single objective, which can reduce the difficulty of cascade optimal dispatching, but at the same time ignores the comprehensive benefits that can be developed by hydropower stations, resulting in extreme and single research results. In the optimal dispatching of reservoirs, only considering improving the power generation efficiency of hydropower stations includes research goals such as maximum power generation, minimum water consumption, and maximum energy storage. Thes...

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Problems solved by technology

Because this method decomposes the unified optimization problem into two independent processes to optimize separately, the calculation results often deviate from the real optimal solution of the original problem; at the same time, because it is difficult to consider constraints such as guaranteed output in the regression process, the method When the formulated dispatching function guides the operation of the hydropower station reservoir, there will be unfavorable situations in which the normal operation of the reservoir is more damaged

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