Multi-target peak regulation optimizing method and system

Abstract

The invention provides a multi-target peak regulation optimizing method and system. The method comprises the following steps: initial values are respectively assigned for corresponding target weights in pre-established multi-target peak regulation optimizing models; a first peak regulation scheme is obtained according to the multi-target peak regulation optimizing models after the initial values are assigned, and the value of each optimizing target in each multi-target peak regulation optimizing model is determined to be used as the original value of the corresponding optimizing target through the first peak regulation scheme; the entropy weight of each optimizing target is determined respectively according to each original value in an entropy weight method, and each entropy weight is assigned to the corresponding target weight in each multi-target peak regulation optimizing model; and a second peak regulation scheme is obtained according to the multi-target peak regulation optimizing model after the entropy weights are assigned. The multi-target peak regulation optimizing method disclosed by the invention is independent of artificial experiences, the randomness of dereferencing of a multi-target optimizing problem is effectively avoided, and the peak regulation optimizing effect is further improved.

Description

technical field [0001] The invention relates to the field of electric power technology, in particular to a multi-objective peak regulation optimization method and system. Background technique [0002] For large-scale power systems including thermal power, hydropower, nuclear power, pumped storage, wind power, biomass power, gas power and other types of power sources, it is difficult to optimize peak regulation. Mainly manifested in: [0003] (1) In terms of peak shaving optimization modeling, traditional peak shaving optimization is usually modeled as a single-objective optimization problem, that is, only the minimum coal consumption is used as the optimization objective function. With the promotion of low-carbon economy and clean energy international situation, multi-objective peak shaving optimization has gradually become a research hotspot. At present, most of the peak shaving schemes are evaluated from the perspective of economic indicators such as coal consumption, net...

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Problems solved by technology

[0004] (2) In terms of determining the target weights of multi-objective optimization problems, it is still largely based on the manual experience of the operation and scheduling personnel, which is highly subjective and lacks a refined or more objective method for determining the target weights

However, the objective weights of multiple optimization objectives in the objective function are directly related to the final optimization results. The existing commonly used weight assignment methods include fuzzy mathematics, analytic hierarchy process, and expert scoring. To a certain extent, multi-objective optimization can be carried out, but there are still some problems, which are highlighted by strong subjectivity, and the optimization results are heavily dependent on manual experience, etc., such as: the level of AHP has large differences, and depends on the subjective bias of the evaluators for various indicators The fuzzy mathematics method overemphasizes the role of extreme values, resulting in the lack of information; the expert scoring method is difficult to operate

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