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Real-time supply and demand interaction method of power system based on deep transfer learning and stackelberg game

A transfer learning and power system technology, applied in the field of real-time supply and demand interaction in power systems, can solve problems such as changing demand response results, difficult interaction convergence, and affecting load curves, so as to achieve low dependence, maximize overall benefits, and improve learning efficiency Effect

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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

Method used

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  • Real-time supply and demand interaction method of power system based on deep transfer learning and stackelberg game
  • Real-time supply and demand interaction method of power system based on deep transfer learning and stackelberg game
  • Real-time supply and demand interaction method of power system based on deep transfer learning and stackelberg game

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Embodiment Construction

[0047] The specific implementation of the real-time supply and demand interaction method for power systems based on deep transfer learning and Stackelberg game of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Please refer to figure 1 , an embodiment of the present invention provides a real-time supply and demand interaction method for power systems based on deep transfer learning and Stackelberg game. This embodiment starts from the actual model of grid supply and demand interaction, and players correspond to generators on the supply side and flexible loads on the demand side. Therefore, any generator or flexible load participating in the interactive game can be selected as the leader. The real-time supply and demand interaction method of power system based on deep transfer learning and Stackelberg game includes the following steps:

[0049] Step S1, initialize algorithm parameters.

[0050] The optimization ef...

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Abstract

The invention provides a power system real-time supply-demand interaction method based on deep migration learning and Stackelberg game. Based on the Stackelberg game strategy, the method builds a real-time supply-demand interaction model of the power system that simultaneously realizes the maximization of supply-demand side benefits. And for the constructed model, an optimization algorithm based on deep transfer learning is proposed. The algorithm is based on the reinforcement learning mechanism and has low dependence on the mathematical model. It can solve the non-convex supply-demand interaction model of the power system with the valve point effect; it is suitable for the distributed framework of the Stackelberg game strategy construction; and it can effectively use the history through the deep neural network The legacy information of the optimization task is used for transfer learning, which significantly improves the solution speed of the new optimization task, and can quickly and efficiently solve the real-time supply-demand interaction model.

Description

technical field [0001] The invention relates to the field of real-time supply and demand interaction of power systems, in particular to a real-time supply and demand interaction method of power systems based on deep migration learning and Stackelberg game. Background technique [0002] Economic dispatch is an important part of the daily dispatch of the power system. Its purpose is to minimize the power generation cost of the system under the operating constraints such as power balance, which is of great significance to the economic and safe operation of the system. With the development of smart electricity consumption, there are more and more flexible loads that can be adjusted within a certain range, such as transferable loads such as air conditioners and washing machines, and bidirectional controllable loads such as electric vehicles and energy storage. It is becoming a new trend to tap the potential of system adjustment from demand response to improve the relationship bet...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/00
CPCG06Q10/04G06Q10/067G06Q50/06G06N3/006
Inventor 周宇胡卫丰景春明周洪益余涛曾江瞿凯平
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