Distributed optimization and spatial correlation-based wind power prediction method

Abstract

The invention relates to a distributed optimization and spatial correlation-based wind power prediction method. The method comprises the following steps of 1, normalizing same-period wind power historical data of wind power plants in a region, then performing equal-interval division on output states, generating a basic data set and a wind power output state library, and performing storage; 2, exchanging historical spatial correlation data by the wind power plants, and generating and storing a time-space Markov state transition matrix in a to-be-predicted target wind power plant; 3, based on alocal time sequence and the time-space Markov state transition matrix, building a sparse time-space wind power prediction model of each wind power plant, and performing solving; and 4, exchanging real-time measurement data among the wind power plants, predicting a power value of each wind power plant by adopting the prediction model according to an output value of each wind power plant at a current moment, and updating parameters of the time-space Markov state transition matrix and the prediction model by regularly adopting latest actual measurement data. The method effectively improves the wind power prediction precision, thereby remarkably improving the calculation efficiency.

Description

technical field [0001] The invention relates to the field of power system operation and control, in particular to a wind power prediction method based on distributed optimization and spatial correlation. Background technique [0002] With the depletion of non-renewable resources such as coal and oil and the increasingly serious energy dilemma, renewable energy such as wind energy, solar energy, tidal energy and biomass energy has attracted more and more attention worldwide. Wind power is the renewable energy with the most mature technology and the most development value in the renewable energy generation technology. The development of wind power is of great significance to ensure energy security, adjust energy structure, reduce environmental pollution, and achieve sustainable development. [0003] The intermittent nature of wind energy in nature determines that wind power has strong fluctuations. As the number and installed capacity of wind farms continue to increase, once ...

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Problems solved by technology

This feature brings convenience to historical data collection and real-time data collection in the forecasting process. The required data is single and easy to implement. However, this type of method ignores the spatial correlation between different wind farms in an area, and the prediction accuracy needs improvement

[0005] In addition, the traditional method usually needs to establish an independent and different form of prediction model for each wind farm. The prediction models of each wind farm need to set and adjust parameters separately. With the continuous expansion of wind power scale, wind farms in a region The number has increased sharply. This modeling method is cumbersome and consumes a lot of time and computer resources. It is not conducive to the rapid deployment and unified deployment of wind power forecasting systems, and cannot adapt to large-scale wind power grid integration.

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