Power grid power flow regulation and control decision reasoning method based on depth deterministic strategy gradient network

Abstract

The invention discloses a power grid power flow regulation and control decision reasoning method based on a depth deterministic strategy gradient network. The method comprises the following steps: designing state representation vector and an action representation vector of a power network for the power network; designing an inference model based on a depth deterministic strategy gradient network, taking the state representation vectors as the input of an Actor network to obtain a plurality of similar discrete actions, taking the state-action pair vectors as the input of a Critic network, and outputting the value estimation of each state-action pair vector; selecting the action with the highest estimated value as a final action to be executed in the environment in the state; and simulating a power grid operation environment based on the discretized power grid operation data set, interacting the model with the simulated power grid operation environment, obtaining a current state and a final action to be executed from the simulated power grid operation environment, and executing the final action to be executed by the simulated power grid operation environment. The invention provides a feasible means for real-time regulation and control of the power network.

Description

technical field [0001] The invention belongs to the technical field of smart grids, and relates to an artificial intelligence enhancement for power network power flow control, in particular to a power grid flow control decision-making reasoning method based on a deep deterministic strategy gradient network. Background technique [0002] As a pipeline for transporting electric energy, the large power grid is a complex dynamic system with high dimensionality and tight coupling. Ensuring the safe operation, scheduling and control (regulation) of the large power grid has always been a problem widely related to industry and academia. At present, the first line of defense for large power grid regulation is a safe and stable automation device, and the second line of defense is to rely on manual experience to make final decisions on power grid regulation. Due to the extensive access of large-scale new energy, the grid regulation is uncertain, the interconnection of multiple types of...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a decision-making reasoning method for power flow control based on a deep deterministic policy gradient network, based on the interactive learning of a deep reinforcement learning algorithm and a simulated power network environment, to obtain a large number of power grid control The mapping relationship between operation knowledge and power grid state and control behavior provides a feasible means for real-time control of the power network, and algorithm design for the high-dimensional state and action space of complex problems

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