Intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought

A multi-agent, reinforcement learning technology, applied in circuit devices, AC network circuits, electrical digital data processing, etc., can solve problems such as inability to win or lose standard accurate calculation

Inactive Publication Date: 2017-06-27
CHINA THREE GORGES UNIV
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  • Claims
  • Application Information

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Problems solved by technology

However, because WOLF cannot accurately calculate the winning and losing criteria in the 2*2

Method used

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  • Intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought
  • Intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought
  • Intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought

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Embodiment

[0060] In this embodiment, under the overall framework of the Central China Power Grid, each control area realizes interconnection by using a high-voltage direct current transmission system and a high-voltage alternating current transmission system. Taking Hubei Power Grid as the main research object, the simulation model is a detailed full-process dynamic simulation model built by the actual engineering project of the Hubei Provincial Power Dispatching Center. The simulation model is divided into six parts: Henan, Hubei, Jiangxi, Hunan, Chongqing and Sichuan. Regional power grid, Henan power grid is an AC / DC (AC / DC) hybrid transmission system that meets the CPS standard, and the control period of SGC is 4s. The L10 values ​​of Henan Power Grid, Hubei Power Grid, Jiangxi Power Grid, Hunan Power Grid, Chongqing Power Grid and Sichuan Power Grid are: 214MW, 118MW, 79MW, 80MW, 125MW and 190MW respectively. Pulse load disturbance (amplitude: 1000; period: 1200s; pulse width: 50% o...

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Abstract

An intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought includes the following steps: determining a state discrete set S; determining a combined action discrete set A; collecting real-time operating data of each power grid, calculating an instantaneous value of each area control error ACE(k) and an instantaneous value of a control performance standard CPS(k), and selecting search action a<k>; in the current state s, obtaining a short-term award function signal R(k) by a certain area power grid i; obtaining value function errors rho<k> and delta<k> through calculation and estimation; updating a Q function table and a time tunnel matrix e(s<k>, a<k>) corresponding to all states-actions (s, a); updating Q values and updating a mixed strategy pi(s<k>, a<k>) under the current state s; then updating a time tunnel element e (s<k>, a<k>); selecting a variable learning rate phi; and updating a decision change rate delta (s<k>, a<k>) and a decision space estimation slope delta<2>(s<k>, a<k>) according to a function. The intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought aims to solve the problem of equalization of multi-area intelligent power generation control, has a higher adaptive learning rate capability and a faster learning speed ratio, and has a faster convergence rate and higher robustness.

Description

technical field [0001] The invention relates to an intelligent power generation control technology of a power system, in particular to an intelligent power generation control method based on multi-agent reinforcement learning with the idea of ​​a time tunnel. Background technique [0002] The automatic generation control (AGC) of the interconnected grid is an important technical means to adjust the frequency and active power of the grid and ensure the safe operation of the grid. At present, the design of AGC control strategy is mostly classical PI control structure. However, because the operating point of the power system changes with the day, month, season, and year, it is difficult for the fixed-gain controller based on the traditional control method to meet the control performance requirements of the increasingly complex power system. Intelligent methods such as neural network method, fuzzy control and genetic method are successively applied to the design of AGC controll...

Claims

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Application Information

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IPC IPC(8): H02J3/24G06F17/50
CPCG06F30/20H02J3/24H02J2203/20Y02E60/00
Inventor 席磊李玉丹陈建峰柳浪
Owner CHINA THREE GORGES UNIV
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