Deep Q network based regional power-grid operation point dispatching optimization method

Abstract

The invention provides a deep Q network based regional power-grid operation point dispatching optimization method. The method comprises the steps that a dispatching configuration of a regional power-grid operation point dispatching center is determined; a dynamic decision process for dispatching operation points of regional power grid is modeled into a corresponding Markov decision process model which comprises state, action, cost and optimization target function; the deep Q network is used to carry out strategy resolution on the Markov dynamic decision process; the operation point of the regional power grid comprises a routine thermal power set output of a next dispatching period, thermal power set output and the flexible load reduction amount; and dispatching of the operation point of the region power grid comprises, at least, that the power grid operation point in the next dispatching period is determined dynamically according to wind power and load power short-term prediction information and operation point information in the present period. Thus, effective response to randomness of the new energy output and load demands can be made, source-load interactive dispatching potential gives a full play, dynamic balance of the power of the regional power grid is maintained, and the operation efficiency of the power grid system is improved.

Description

technical field [0001] This patent relates to the technical field of power grid intelligent dispatching, in particular to a method for optimizing the dispatching of regional power grid operating points based on a deep Q network. Background technique [0002] With the rapid development of large-scale wind power grid-connected and flexible loads, source-load bilateral uncertainties have an increasingly serious impact on power grid dispatching operations. Source-load interaction is an important part of the source-grid-load interaction framework system, which can Promote the rational use of power generation and consumption resources, and improve the comprehensive benefits of system operation safety, economy, and cleanliness. [0003] In the existing technology, there is a scheme that considers interruptible load and incentive load in the day-ahead scheduling model, which reflects the potential peak-shaving benefit of load-side resources, such as Yang Nan et al [Yang Nan, Wang Bo...

AI Technical Summary

Problems solved by technology

These schemes deal with the randomness of resources on the power generation side through flexible load scheduling, and have certain reference significance for dynamic scheduling of power grid operating points. However, traditional methods for dealing with uncertain grid scheduling problems mainly focus on robust optimization methods,

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