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

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...

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However, because WOLF cannot accurately calculate the winning and losing criteria in the 2*2 game, the decision of WoLF-PHC can only be explored based on the valuation equilibrium reward

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