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

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...

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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 optimal power flow method also exists when dealing with the adjustment of the operation mode of a large power grid. It is easy to fall into local optimum, and the power flow is not easy to converge during multi-objective optimization.

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