Source-load-storage scheduling method based on improved multi-objective particle swarm optimization algorithm

Abstract

The invention discloses a source-load-storage scheduling method based on an improved multi-objective particle swarm optimization algorithm. The method comprises the steps of: S1, integrating a thermalpower plant, a clean energy power plant, a user load and an energy storage device into a source-load-storage scheduling system, and determining the scheduling optimization objective function and theconstraint condition of the source-load-storage scheduling system to achieve the minimum system operating cost and the highest clean energy consumption; S2, using the scheduling optimization objectivefunction as the fitness function of the improved multi-objective particle swarm optimization algorithm, and using the constraint condition as a particle variable constraint section of the algorithm;and S3, by using the improved multi-objective particle swarm optimization algorithm, obtaining a minimum value of the fitness function and a particle variable that takes the minimum value of the fitness function according to the fitness function and the particle variable constraint section. The method is easy to implement and flexible to use, can improve the solution efficiency of a power scheduling scheme, and can obtain a reasonable power scheduling scheme with good economic benefit.

Description

technical field [0001] The invention relates to the technical field of power system control, in particular to a source-load-storage scheduling method based on an improved multi-objective particle swarm algorithm. Background technique [0002] In recent years, photovoltaic power generation and wind power generation in northern my country have developed rapidly, and the grid connection of a large amount of clean energy has alleviated the energy crisis to a certain extent. However, in the winter heating period, in order to meet the heat load demand, a large number of thermal power units work in the mode of "heating fixed power". Very restrictive. This thermoelectric coupling relationship makes the power system's peak-shaving capability insufficient, and the system usually needs to discard part of clean energy or cut off part of the load to ensure stable operation, resulting in a large amount of energy waste and reduced power supply reliability. [0003] The schedulable resour...

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Problems solved by technology

[0004] In order to obtain the best dispatching scheme, in recent years, the multi-objective particle swarm optimization algorithm has been widely used in the research of multi-objective optimization problems in power systems due to its high solution efficiency. Trapped in a local optimal problem, its solution efficiency and solution accuracy can no longer meet the needs of modern power system dispatching

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