Firefly grouping method, as well as power dispatching system and power dispatching method based on same

Abstract

The invention discloses a firefly grouping method, as well as a power dispatching system and a power dispatching method based on the same. The firefly grouping method comprises the following steps: setting each parameter and initializing groups; figuring out a target value of each firefly particle and sequencing the groups according to levels of the target values; carrying out grouping operation on the groups; independently carrying out evolutionary optimization on each sub group in parallel according to a firefly algorithm; after number of iterations is reached, stopping evolutionary optimization of each sub group; combining all sub groups into one group; then carrying out optimized evolution on the total group according to the firefly algorithm; and after the number of iterations is reached, repeating the above steps until the iterations with the number of iterations are finished or requirements on the experimental accuracy are satisfied. According to the invention, the capacity of each unit is reasonably configured, so that load requirements can be satisfied through power generation of the power system, and low cost is obtained; moreover, through grouping and clustering circulation, information is shared, so that groups are prevented from falling into local extreme value; and finally, a better dispatching scheme can be obtained.

Description

technical field [0001] The present invention relates to a method of grouping fireflies and a power dispatching system and method based on the method, in particular to a method of grouping fireflies and an electric power dispatching system and method based on the method of grouping fireflies including wind farms. Background technique [0002] For the economic scheduling problem of the power system including wind farms, there are currently many solutions, the main method is the particle swarm algorithm and its improvement. In these methods, in the scheduling problem of the power system including wind farms, the model of generating unit power generation is established first. , put forward the main goal of scheduling, list the constraints, and then apply the optimization algorithm to the distribution optimization of unit output to obtain the optimal scheduling plan, so that the power generation can meet the demand and the cost is the lowest. [0003] However, for the particle sw...

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Problems solved by technology

[0003] However, for the particle swarm optimization algorithm, its disadvantage is that the population may not be able to avoid falling into a local extremum, making the algorithm unable to continue to optimize, the population may not be able to find the optimal solution, and thus the optimal scheduling scheme cannot be obtained

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