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A Decentralized q Method for Power System Economic Dispatch Based on Extreme Transfer Learning

A transfer learning and economic dispatching technology, applied in the decentralized field of power system economic dispatching based on extreme transfer learning, can solve problems such as non-convex function optimization problems with steam turbines that cannot be solved, and achieve the effect of strong adaptability and improved convergence speed.

Active Publication Date: 2021-01-08
ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD +1
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Problems solved by technology

[0004] However, in terms of optimization algorithms, traditional artificial intelligence algorithms, such as GA algorithm, ABC algorithm, and PSO algorithm, are all centralized. Classical optimization methods, such as Newton’s method and interior point method, cannot solve the optimization problem of non-convex functions involving steam turbines.

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  • A Decentralized q Method for Power System Economic Dispatch Based on Extreme Transfer Learning
  • A Decentralized q Method for Power System Economic Dispatch Based on Extreme Transfer Learning
  • A Decentralized q Method for Power System Economic Dispatch Based on Extreme Transfer Learning

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Embodiment Construction

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0035] see Figure 1-3 , one embodiment of the present invention provides a decentralized Q method for power system economic dispatch based on extreme transfer learning, comprising the following steps:

[0036] S1. Determine the economic dispatch model based on the power generation cost parameters and constraints of the generator, and establish the fitness function to minimize the power generation cost of the system;

[0037] S2. Establish a distributed econo...

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Abstract

The invention discloses an electric system economic dispatching Q dispersion method based on extreme transfer learning. According to the electric system economic dispatching Q dispersion method based on extreme transfer learning, the problem of non-convex economic dispatching containing a turbine valve point effect can be solved, scattered optimizing calculation can be carried out, the requirements of current scattered economic dispatching are met, the method can modify an initial matrix of the Q dispersion method through a mechanism of extreme transfer learning, and the rate of convergence of the algorithm is greatly increased.

Description

technical field [0001] The invention relates to the technical field of power system economic dispatching, in particular to a decentralized Q method for power system economic dispatching based on extreme transfer learning. Background technique [0002] In modern society, people pay more and more attention to energy conservation and emission reduction. The Chinese government attaches great importance to energy conservation, and power supply enterprises should also respond to national policies. The research of economic dispatch is how to achieve the optimal combination of unit power generation under the rated load, and its ultimate goal is to study how to reduce the cost of system power generation. [0003] However, the traditional economic dispatching methods of the power system are all centralized. Centralized scheduling is usually inflexible and slow to converge. It requires a complex communication network to collect global information and a powerful central controller to p...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 郑宇孟科徐岩邱靖董朝阳李正佳
Owner ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD