Power grid regulation and control method based on layered depth strategy gradient network

Abstract

The invention discloses a power grid regulation and control method based on a layered depth strategy gradient network. A state representation vector and an action representation vector of a power grid are designed for the power grid; the motion space is clustered, so that the motion number of each cluster is equal, a power grid regulation and control model is designed based on a layered strategy gradient network by taking a state representation vector as the input of the network and using a strategy gradient algorithm, the model has two layers, each layer is an independent strategy gradient model, the first layer selects the motion cluster firstly, and the second layer selects the motion cluster; the second layer selects specific actions in the cluster, and continuous decision making is carried out; based on a discrete power grid operation data set of a simulated power grid environment, the model and the simulated power grid operation environment are interacted, a current state is obtained from the simulated power grid operation environment, and a power grid action to be executed is executed by the simulated power grid operation environment, so that the purpose of power grid regulation and control is achieved. The invention provides a feasible means for real-time regulation and control of the power grid.

Description

technical field [0001] The invention belongs to the technical field of smart grids, relates to artificial intelligence enhancement of power flow regulation of power grids, and particularly relates to a power grid regulation method based on a layered deep policy gradient network. Background technique [0002] The power grid is the core infrastructure for the operation of the national economy, and it is a high-dimensional tightly coupled complex dynamic system. Plays a central economic and social role by providing reliable power to industry, services and consumers. The power grid operation scheduling and regulation highly relies on the safety and stability automation device as the first security line of defense. After the line of defense fails, the final security of the entire system is guaranteed by people's experience and cognitive decision on the power grid. [0003] It is difficult for the traditional power grid regulation system to adjust the regulation strategy in time,...

AI Technical Summary

Problems solved by technology

[0011] In order to overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a grid control method based on a layered deep strategy gradient network, and learn a large amount of grid control knowledge based on the interaction between the deep reinforcement learning agent and the simulated grid environment It provides a feasible means for real-time regulation of the power grid, and performs algorithm design for the high-dimensional state and action space that exists in complex problems.

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