Energy system operation optimization-oriented reinforcement learning control system

Abstract

The invention discloses an energy system operation optimization-oriented reinforcement learning control system, which is suitable for various energy systems, such as a gas distributed energy system and the like, and comprises an energy system (such as a gas distributed energy system), an operation data module (a database), a benefit evaluation module (a reward item), a deviation calculation module (a punishment item) and an intelligent agent (operation optimization). Through online reading and writing of the operation data module (a database), isolation of model training and energy system control is realized. Through reward feedback and punishment feedback of a benefit evaluation and deviation calculation model on an agent (operation optimization) model, intelligent agent (operation optimization) model training under the safe operation requirement of the energy system is realized. The optimal control output obtained through model training acts on the energy system, and online closed-loop control is achieved.

Description

technical field [0001] The invention relates to a reinforcement learning control system oriented to energy system operation optimization, which can be applied to the operation optimization control of various energy systems (such as gas distributed energy systems, etc.). Background technique [0002] Because the energy system inputs fuel, outputs electricity, cold / heat energy, has the characteristics of diverse energy supply and complex control, the unit structure is complex, and the coupling is strong. Manual control often depends on manual experience, and the operation and operation levels of different personnel are uneven. , resulting in uneconomical or inefficient system operation. At the same time, due to the complex process technology of the energy system, many equipments, variable characteristics, and multiple input and output forms, the conventional mechanism modeling is complicated, the accuracy is not enough, and it is more difficult to match the actual system opera...

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Problems solved by technology

[0003] The purpose of the present invention is to overcome the above-mentioned deficiencies in the prior art, and provide a reinforcement learning control system oriented to energy system operation optimization, which solves the problem of energy diversity due to the energy system input fuel, output electricity, cold / heat energy, The control is complicated and other characteristics, the structure of the unit is complex, the coupling is strong, and the manual control often depends on the manual experience. The operation level of different personnel is uneven, which leads to problems such as uneconomical or low efficiency in system operation.

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