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Multi-energy system optimal scheduling method based on knowledge migration Q learning algorithm

A system optimization and learning algorithm technology, applied in the field of real-time optimization and dispatching of integrated energy systems, can solve problems such as difficult interactive convergence, changing the results of demand response, affecting load curves, etc., to improve economic and environmental benefits, and reduce energy supply. The effect of cost and carbon emissions

Inactive Publication Date: 2020-10-13
STATE GRID JIANGSU ELECTRIC POWER CO LTD NANTONG POWER SUPPLY BRANCH
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Problems solved by technology

On the one hand, users will respond to the market by adjusting electricity demand to maximize profits, and the result of demand response will affect the load curve; on the other hand, economic dispatch will lead to changes in conditions such as market prices, which will change the result of demand response
If economic dispatch and demand response are performed unilaterally, it is difficult to converge interactively

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  • Multi-energy system optimal scheduling method based on knowledge migration Q learning algorithm
  • Multi-energy system optimal scheduling method based on knowledge migration Q learning algorithm
  • Multi-energy system optimal scheduling method based on knowledge migration Q learning algorithm

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Embodiment Construction

[0026] The specific implementation manner of the multi-energy system optimization scheduling method based on the knowledge transfer Q learning algorithm of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Please refer to figure 1 , figure 2 with image 3 An embodiment of the present invention provides a joint optimization scheduling method for multiple energy systems based on a knowledge transfer Q learning algorithm. This embodiment starts from the joint optimal scheduling model of the multi-energy system, and adopts the knowledge transfer Q learning algorithm to achieve rapid solution. The multi-energy system joint optimization scheduling method based on the knowledge transfer Q learning algorithm includes the following steps:

[0028] Step S1, initialize algorithm parameters.

[0029] The optimization effect of the multi-energy system joint optimization scheduling method based on the knowledge transfer Q learning algor...

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Abstract

The invention provides a multi-energy system optimization scheduling method based on a knowledge migration Q learning algorithm. According to the method, a joint optimization scheduling framework of amulti-energy system is built based on an energy center modeling method; a typical multi-energy system joint optimization scheduling model considering the energy supply cost of a valve point effect and a carbon emission target is built; and based on the constructed model, a cascaded algorithm composed of a knowledge migration Q learning algorithm and an interior point method is provided for solving, wherein a multi-energy system determined after upper layer Q learning takes the active power of an unit as an action variable and lower layer takes an interior point method to solve the active power of the unit is used for optimize the model, and the solving efficiency is improved through knowledge migration.

Description

Technical field [0001] The invention belongs to the field of real-time optimal dispatching of integrated energy systems, and particularly relates to a method for optimal dispatching of multiple energy systems based on a knowledge transfer Q learning algorithm. Background technique [0002] The concept of Energy Internet has attracted great attention from domestic and foreign scholars. The energy Internet has more far-reaching connotations than the previous smart grid: First, the primary and secondary devices of various energy networks are closely connected to form a complex network. Secondly, various energy networks form two-way flow and mutual conversion through energy conversion devices. In addition, various types of transmission and energy storage equipment support the extensive access of renewable energy sources and realize the coordination, interaction and optimization of multiple energy sources. With the large-scale exploitation of natural gas worldwide and the continuous...

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Application Information

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IPC IPC(8): G06F30/18G06F30/27G06N3/00G06F111/02G06F111/04G06F111/08G06F113/04
CPCG06N3/006G06F30/18G06F30/27G06F2111/02G06F2111/04G06F2111/08G06F2113/04
Inventor 袁健华张乐张敏杨鸣贲树俊代克丽罗云钱霜秋
Owner STATE GRID JIANGSU ELECTRIC POWER CO LTD NANTONG POWER SUPPLY BRANCH
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