Power grid multi-section power automatic control method based on distributed multi-agent reinforcement learning

Abstract

The invention discloses a power grid multi-section power automatic control method based on distributed multi-agent reinforcement learning. The method can achieve the autonomous learning of a proper multi-section power control strategy for a complex power grid through the interaction of multiple agents and a power simulation environment. The method comprises the following steps of firstly, N target sections are selected according to the need of power grid control, and basic elements such as an environment, an intelligent agent, an observation state, an action and a reward function of the reinforcement learning method are constructed; secondly, a multi-section power control task interaction environment is operated, and an initial power flow data set is created; then, a decision network and an estimation network based on a deep neural network are constructed for each agent, an MADDPG (multi-agent deep deterministic strategy gradient) model is constructed, and a distributed method is introduced to train an autonomous learning optimal control strategy; and finally, the trained strategy network is applied to perform automatic section control. The method is advantaged in that a complex power grid multi-section power control problem is solved through the multi-agent reinforcement learning method, the control success rate is high, expert experience is not needed, and meanwhile agent training efficiency is greatly improved by introducing the distributed method.

Description

technical field [0001] The invention relates to the technical field of smart grid power control, in particular to the technical field of smart grid section power control based on reinforcement learning. Background technique [0002] With the construction of large-scale modern power grids, the composition structure and operating environment of power systems are becoming increasingly complex. In order to ensure the safety and economy of power system operation, it is necessary to closely monitor the operation status of the power grid. In recent years, due to the increasing shortage of fossil energy, renewable new energy such as wind energy and solar energy have gradually penetrated into the modern power grid, and the uncertainty and complexity of power grid operation have increased. Monitoring and regulating a large number of electrical devices presents a difficult challenge. Therefore, the contemporary power grid urgently needs to transform into a more robust and adaptable s...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the deficiencies of the existing multi-section power control methods of the power grid relying on expert experience, low efficiency, and limited application scenarios, the present invention proposes an adaptive, efficient, and scalable power grid based on distributed multi-agent reinforcement learning Multi-Section Power Control Method

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