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DBN power grid load prediction method and device based on generalized demand side resource

A technology of power grid load and forecasting method, applied in the field of power system, can solve difficult problems such as the relationship between input and output

Pending Publication Date: 2020-03-20
HEFEI UNIV OF TECH
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  • Claims
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AI Technical Summary

Problems solved by technology

However, the current load forecasting methods based on artificial intelligence are mostly three-layer shallow networks, which are difficult to deal with the relationship between input and output in the complex environment of today's power grids.

Method used

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  • DBN power grid load prediction method and device based on generalized demand side resource
  • DBN power grid load prediction method and device based on generalized demand side resource
  • DBN power grid load prediction method and device based on generalized demand side resource

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Embodiment Construction

[0081] In order to explain in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and accompanying drawings.

[0082] The invention discloses a DBN power grid load forecasting method and device based on generalized demand side resources. The generalized demand side resources include: controllable loads, distributed power sources and energy storage devices. Controllable loads include curtailable loads LC and transferable loads LS, and distributed power includes photovoltaic power generation and wind power generation. In the electricity market environment, users with generalized demand-side resources aim at electricity economy according to different price signals and incentive mechanisms. Therefore, the influence factors of generalized demand-side resources are integrated into the DBN load forecasting model, and a DBN short-term load forecasting...

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Abstract

The invention discloses a DBN power grid load prediction method and device based on generalized demand side resources. The method comprises: establishing a scheduling model based on an electricity price contract for the reducible load LC, the transferable load LS and the energy storage system ES, wherein the model determines an optimal scheduling plan of three generalized demand side resources participating in the power market by means of a load aggregator; on the basis, fusing generalized demand side resource influence factors into a DBN load prediction model, and establishing a DBN short-term load prediction model considering generalized demand side resources; and training and testing the prediction model in combination with historical load data and weather data to obtain a daily load prediction curve of the to-be-tested area. The method is high in prediction precision and good in stability, and can meet the power grid prediction requirements under load big data.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and more specifically relates to a DBN power grid load forecasting method and device based on generalized demand-side resources. Background technique [0002] Short-term load forecasting is of great significance to the dispatching department's optimal combination of units, economic dispatching, and optimal power flow, especially for the current and future power markets. Accurate load forecasting is conducive to economically and rationally arranging internal generator sets in the power grid It can improve the utilization rate of power generation equipment and the effectiveness of economic dispatch, and maintain the safety and stability of power grid operation. [0003] In the smart grid, generalized demand-side resources such as controllable loads, distributed power sources, and energy storage respond to demand in a flexible and diverse manner, which enhances the load transfer capab...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q50/06G06N3/08
CPCG06Q30/0202G06Q30/0206G06Q50/06G06N3/084Y04S10/50Y04S50/14Y04S50/16
Inventor 唐昊胡实吕凯张千里谭琦
Owner HEFEI UNIV OF TECH
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