Dynamic power system economic dispatching method based on deep reinforcement learning

Abstract

The invention provides a dynamic power system economic dispatch method based on deep reinforcement learning. The method converts dynamic economic dispatch into a multi-stage sequential decision model,takes a dispatch center for action decision as a decision-making main body and an actual power system as an environment, and improves economic dispatch efficiency by designing elements such as actions, states and rewards in reinforcement learning. and converts the economic dispatching model of the power system into a typical multi-stage sequential decision model. According to the model, modelingof an increasingly complex power system is avoided, an accurate thermal power generating unit output cost function is not required, and dynamic economic dispatching of the power system in any scene isrealized through continuous interaction of an intelligent agent and the environment, updating of a strategy, self-adaptive load and uncertainty of new energy output.

Description

technical field [0001] The invention belongs to the field of power systems, and relates to a method for economic scheduling of dynamic power systems based on deep reinforcement learning. Background technique [0002] With the deepening of electric power reform, the production and consumption of electric energy will be determined by the ever-changing market demand, and the uncertainty of load fluctuation will increase, making it more difficult to predict accurately. The output of new energy has the characteristics of volatility, intermittence and randomness, and their large-scale grid connection adds a variety of uncertain factors to the operation of the grid. The uncertainty of load and new energy output has brought more serious problems to the safety and reliability of the power system, and also brought great challenges to the economic dispatch of the power grid. [0003] Economic dispatch is a classic optimization problem in the power system. It aims to reduce the fuel co...

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Problems solved by technology

Although robust optimization does not depend on the probability distribution of uncertain parameters, it is easy to describe, but the setting of its conservative degree is also a problem worth studying

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