Deep Q network driven power system operation mode automatic optimization adjustment method

An operation mode and network-driven technology, applied in the direction of system integration technology, neural learning method, biological neural network model, etc., can solve problems such as local optimum and difficult convergence of power flow, and achieve the effect of low power generation cost

Active Publication Date: 2020-08-11
SICHUAN UNIV +1
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Problems solved by technology

[0003] Aiming at the above-mentioned deficiencies in the prior art, a deep Q-network-driven power system operation mode automatic optimization adjustment method provided by the present invention solves the problem. The more common op

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  • Deep Q network driven power system operation mode automatic optimization adjustment method
  • Deep Q network driven power system operation mode automatic optimization adjustment method
  • Deep Q network driven power system operation mode automatic optimization adjustment method

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Embodiment Construction

[0068] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0069] Such as Image 6 As shown, a deep Q network-driven power system operation mode automatic optimization adjustment method includes the following steps:

[0070] S1: Take the typical operation mode as the benchmark mode for adjustment, determine the load fluctuation range, and combine the Latin hypercube sampling method to generate a large number of target mode sample data for training and testing;

[0071] S2: Determi...

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Abstract

The invention discloses a deep Q network driven power system operation mode automatic optimization adjustment method. The method comprises steps of determining a load fluctuation range by taking a typical operation mode as an adjustment reference mode, and generating a large amount of target mode sample data for training and testing in combination with a Latin hypercube sampling method; determining all feasible single control actions in the power grid model, numbering the control actions, and setting the control actions as action spaces; initializing a power grid model, judging whether an untrained sample exists or not, if yes, assigning load data in the sample to the power grid model, performing convergence optimization processing on the output data of the generator in the current operation mode, and if not, terminating training and the like. According to the method, the problem that the optimal power flow method is difficult to converge when solving the multi-target optimal power flow is solved while the calculation speed is ensured, various indexes of the mode obtained after adjustment have no overlarge deviation, and method reference is provided for application of deep reinforcement learning to power grid optimization and control problems.

Description

technical field [0001] The invention relates to the technical field of electric power system automation, in particular to a method for automatic optimization and adjustment of an electric power system operating mode driven by a deep Q network. Background technique [0002] As an overall technical plan for power grid operation and production compiled by the power grid operation and regulation department, the power grid operation mode plays a guiding role in the planning and design of the power grid, power generation plan arrangement, real-time dispatch of the power grid, and formulation of maintenance plans. Its compilation needs to fully consider complex factors such as the structure of the power grid, the distribution of power sources and loads, and the carrying capacity of equipment operation, so as to ensure the safety, stability, reliability, flexibility and economic operation of the overall power grid while maximally meeting the load demand. There are many factors, comp...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/04G06N3/08G06N3/00
CPCG06Q10/04G06Q10/06312G06Q50/06G06N3/08G06N3/006G06N3/045Y04S10/50
Inventor 刘友波刘季昂刘俊勇田蓓顾雨嘉李宏强
Owner SICHUAN UNIV
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