Batch-type power data collection particle size calibration method and system

Abstract

The present invention relates to a batch-type power data collection particle size calibration method and system. The method comprises the steps of: drawing a maximum first-order difference output changing curve trend chart according to preset collection particle sizes; identifying continuous changing features in the trend chart, and constructing a power trapezoid curve with alternate increased anddecreased power; defining a target function of the power trapezoid curve, employing a multi-target genetic algorithm to calculate a continuous state meeting the target function, defining the collection particle sizes according to the continuous time of the state, and recording the output track of the batch-type power according to the collection particle sizes. The batch-type power data collectionparticle size calibration method and system solve the problem that the optimal particle sizes are selected to record the output track of the batch-type power.

Description

technical field [0001] The invention relates to the technical field of power system energy storage equipment, in particular to a method and system for calibrating the collection granularity of intermittent power supply data. Background technique [0002] As large-scale renewable intermittent energy is connected to the grid, its power random fluctuations will have a serious impact on the safe operation of the grid. As a beneficial supplement to intermittent energy, the energy storage system can take the lead in coordinating with the control strategy of the energy management system on the power generation side to perform key technologies such as power forecasting, tracking planned output, and fluctuation stabilization. However, in scientific research and field application, the granularity of power generation data information collection is different, and the granularity is often set only by experience, lacking theoretical basis. [0003] Taking the photovoltaic power station a...

AI Technical Summary

Problems solved by technology

If the granularity is too large, some information will obviously be missed, which may cause model recognition errors, affect effective information extraction, and even lead to reverse conclusions

However, the collection granularity is not the denser the better. The data collection method with dense sampling time intervals must be equipped with more sufficient storage space and an effective management system. Although it can truly reproduce the system running track, it will also cause waste of resources and increase costs.

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