A method and system for optimizing and dispatching power energy storage systems based on reinforcement learning

Through the reinforcement learning-based power storage system optimization scheduling method, combined with battery health status and grid data, effective management of battery aging risks and real-time scheduling in low computing power environments are achieved, improving the system's responsiveness and energy utilization efficiency.

CN120357521BActive Publication Date: 2025-09-16BEIJING LUOHE TECH CO LTD
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Patent Information

Application Number
CN202510827467.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-20
Publication Date
2025-09-16
Estimated Expiration
2045-06-20

AI Technical Summary

Technical Problem

Existing power storage systems fail to effectively assess and control the risk of battery aging during frequent charging and discharging, and find it difficult to achieve real-time scheduling requirements in low computing power environments.

Method used

A reinforcement learning-based method is used to combine battery charging status, health status, grid load and electricity price data to generate a state input data set. Pre-trained intelligent agents are used to optimize charging and discharging scheduling instructions, and power distribution and coordinated control are achieved through multi-port converters. Feedback iterative optimization is performed based on historical data.

Benefits of technology

It improves the real-time response capability and battery life management of the power energy storage system, reduces scheduling delays, improves the system's robustness and energy flow coordination, and enhances energy conversion efficiency and scheduling flexibility.

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Abstract

The present application relates to the field of electrical engineering technology, and provides a method and system for optimizing and scheduling an electric energy storage system based on reinforcement learning, in order to solve the problem that it is difficult to effectively evaluate and control the risk of battery aging during frequent charging and discharging, and it is difficult to meet the real-time scheduling requirements in the low computing power environment on the edge side. The method of the present application includes: obtaining the battery charging status and health status data of the electric energy storage system and the grid load and grid electricity price data of the industrial and commercial park to generate a state input data set; using a reinforcement learning agent to obtain charging and discharging scheduling instructions; allocating power to the electric energy storage system, the multi-port converter power interface and the park load to generate multi-source collaborative power flow data; combining historical operating data, using a reinforcement learning agent to optimize the charging and discharging scheduling instructions; and optimizing and scheduling the electric energy storage system according to the optimized instructions. The present invention realizes efficient, intelligent and adaptive optimization scheduling of the electric energy storage system in the industrial and commercial park.
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