Method for predicting net load of distributed power supply power distribution network

A technology of distributed power supply and forecasting method, applied in forecasting, instruments, data processing applications, etc., can solve problems such as the inability to ensure reasonable and reliable scheduling of smart parks, and achieve strong practicability and reliability, convenient scheduling, and improved accuracy. Effect

Inactive Publication Date: 2016-06-15
HUNAN UNIV
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Problems solved by technology

Traditional forecasting methods see figure 1 , which cannot

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  • Method for predicting net load of distributed power supply power distribution network
  • Method for predicting net load of distributed power supply power distribution network
  • Method for predicting net load of distributed power supply power distribution network

Examples

Experimental program
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Embodiment

[0051] According to the real-time "net load" data of the Belgian power grid from 12:00 on January 16 to 11:00 on January 18, 2014, the number of "net load" in the next hour is predicted at 12:00 on January 18 . Due to the lack of other influencing factors, only the day-ahead power is used for forecasting. The predicted result "net load" is 9244315kw, and the actual data is 9264616kw.

[0052] When the wind power, photovoltaic output power and load power are predicted separately, the remaining output required to be dispatched to the grid is: 9875211.12kw, but at this time the actual demand of the grid at that time is 9264616kw, and the relative error is: 6.5%.

[0053] When the "net load" is used for joint prediction, the remaining output required to be dispatched to the power grid is: 9244315kw, and the relative error is 0.22%. By comparison, it can be seen that the accuracy of joint prediction is higher than that of individual prediction. The specific prediction results co...

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Abstract

The invention discloses a method for predicting net load of a distributed power supply power distribution network. determining impact factors which have impact on predicting photovoltaic power generation, wind power, and load power; inputting the impact factors collected into a data integration model to obtain a reference value of the net load; initially establishing a SVM regression prediction model according to all the impact factors and performing training to obtained samples to obtain a SVM model; performing net load prediction to obtain a predicting result according to the mentioned SVM model; managing the predicting result and scheduling reasonably. By use of the method, the prediction precision is greatly increased; a reasonable predicting result can be output; scheduling is facilitated and cost of power network dispatching is reduced; electric energy production of each power plant is reasonably arranged; operation economical efficiency is increased. The method has high practicality and reliability and is good for safe, stable and reliable operation of power networks.

Description

technical field [0001] The invention relates to the research field of rational utilization of new energy power generation, in particular to a net load prediction method of a distributed power distribution network. Background technique [0002] With the development of the economies of various countries, the demand for electricity is also increasing, and power plants and power grids have increased production and deployment difficulties. However, the natural ecological environment faced by human society is getting worse and worse, and the traditional fossil energy (coal, oil) is also increasingly exhausted. How to rationally utilize new energy and renewable energy (solar energy, wind energy, tidal energy, etc.) [0003] With the development of distributed energy and the progress of smart grid, it is believed that the power grid will develop in the direction of intelligentization of each unit in the future. For a system in a smart park that contains both wind energy and solar e...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y02E40/70Y04S10/50
Inventor 李勇文喆彭衍建谭益曹一家
Owner HUNAN UNIV
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