A Data Mining-Based Optimization Method for Order Scheduling of Cascade Hydropower Station Groups

Abstract

The invention discloses a step hydropower station group instruction scheduling optimization method based on data mining. Cluster analysis of a typical daily load curve is realized on the basis that the power generating feature of a power station itself is fully utilized, and a step hydropower station group short-term optimization scheduling scheme according to scheduling habits can be obtained by use of a layered solving method. The technical scheme provided by the invention is as follows: first of all, on the basis that the power generating feature of a hydropower station is fully analyzed and utilized, typical load curve feature research is conducted, the cluster analysis is performed by use of a data mining technology so as to form a step hydropower station decision support database, then an instruction scheduling optimization model taking a routine object function in short-term optimization scheduling and complex constraint conditions into compressive consideration is constructed, an object function conversion mechanism based on different conditions and a complex contain processing method are given, and finally, through combination with a large-system decomposition coordination idea, a step hydropower station group daily generating plan is rapidly made by use of the layered solving method. According to the invention, a step combined scheduling scheme according with scheduling demands and habits can be rapidly obtained, and the method is a feasible method for realizing practicality of a hydropower station group short-term scheduling scheme under a complex condition.

Description

technical field [0001] The invention relates to the field of hydropower system power generation dispatching, in particular to a data mining-based command dispatching optimization method for cascade hydropower station groups. technical background [0002] The short-term optimal dispatching of cascade hydropower station groups is a discrete, multi-dimensional, nonlinear multi-objective large-scale spatio-temporal decision-making optimization problem. With the increasing regulatory role of hydropower in the power system and the large-scale grid connection of intermittent new energy, affected by factors such as uncertain water supply, lagging flow propagation, different regulation capabilities, and variable load demand, the short-term plan for cascade hydropower station groups Compilation is increasingly complex. From a mathematical point of view, system engineering theory is an important means to solve such problems. Various mathematical methods including classical operations ...

AI Technical Summary

Problems solved by technology

However, the main problems of the existing methods are: (1) Solving the short-term optimal dispatching problem of cascade hydropower station groups is always from the perspective of the power grid, and rarely takes the hydropower station (group) as the basis

This method often fully considers the dispatching needs and constraints of the power grid, but ignores the research on the characteristics of the hydropower station (group)'s own power generation, resulting in the optimization results being unpractical

(2) Overemphasize and rely on the establishment of mathematical models and the application of optimization methods, ignoring the practicability and timeliness of optimization results

The actual application effect of the optimization model mainly depends on the accuracy of its description of the original state of the problem. However, with the continuous expansion of the scale of hydropower station groups and the scope of power transmission, there are more and more requirements that are difficult to model in dispatching practice, mainly including production practice. Scheduling habits and external requirements of the hydropower station group system make it difficult to apply the optimization results in practice

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