Method for dividing photovoltaic output fluctuation types

Abstract

The present invention provides a method for dividing photovoltaic output fluctuation types, comprising: I, collecting and processing historical data; II, determining feature vectors according to feature indexes reflecting photovoltaic output features; III, performing clustering analysis by using a self-organizing feature map neural network; and IV, dividing photovoltaic output fluctuation types. The method of the invention can serve as an early basis to be applied in medium and long term output time series modeling of photovoltaic power generation, and provides early technical means for generation of photovoltaic simulation data required by time series production simulation containing large-scale new energy sources, and annual new energy source consumption ability analysis.

Description

technical field [0001] The invention belongs to the technical field of new energy power generation, and specifically relates to a method for classifying fluctuation types of photovoltaic output. Background technique [0002] Against the backdrop of rapid growth in energy consumption and people's increasing concern about the climate and environment, in order to promote sustainable economic and social development, new energy sources such as solar energy and wind energy must be vigorously developed. In recent years, photovoltaic power generation has grown rapidly, transitioning from supplementary energy to alternative energy. However, large-scale photovoltaic power generation grid connection will increase the uncertainty of system power generation, which will have a certain impact on the peak regulation and frequency regulation of the power grid. Accurate long-term output modeling of photovoltaic power plants can provide a basis for grid dispatching plans and operation arrange...

AI Technical Summary

Problems solved by technology

[0003] Photovoltaic output is closely related to weather conditions, and the simulated photovoltaic output time series is also modeled separately according to weather types. However, since some photovoltaic power plants currently do not have weather forecast services, it is impossible to accurately obtain daily weather types, or weather type information corresponding to some historical data Inaccurate PV output modeling due to missing due to transmission errors, communication failures, service interruptions, etc.

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