A method, system, device and medium for constructing an electric power energy storage system based on new energy operation

By classifying the operating conditions of energy storage battery clusters and using state transition models, health losses are quantified in real time. Combined with reinforcement learning controllers to optimize the operating strategies of energy storage systems, the problem of the disconnect between battery health management and economic dispatch in energy storage systems is solved, thereby extending battery life and reducing costs.

CN122178397APending Publication Date: 2026-06-09PUYUAN CONSTRUCTION INVESTMENT (SHANGHAI) NEW ENERGY DEVELOPMENT CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
PUYUAN CONSTRUCTION INVESTMENT (SHANGHAI) NEW ENERGY DEVELOPMENT CO LTD
Filing Date
2026-02-12
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing energy storage systems neglect the complex electrochemical state changes inside batteries during the operation of new energy power, resulting in a disconnect between operation optimization and battery health management, which fails to effectively extend battery life and improve the economic efficiency throughout the entire life cycle.

Method used

The operating conditions of the energy storage battery cluster are classified by a state transition model, the internal health loss of the battery is quantified in real time, and the final power control signal is generated by a reinforcement learning controller to optimize the operation strategy of the energy storage system to minimize health loss.

Benefits of technology

It achieves synergistic optimization of battery health life and system economic operation, extends battery life and reduces total life cycle cost, and improves system safety adaptability and operating efficiency.

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Abstract

This invention relates to the field of power systems and energy storage technology. It discloses a method, system, equipment, and medium for constructing a power storage system based on new energy operation. The method includes: classifying the operating conditions of energy storage battery clusters according to operating data; identifying the state transition cost of each energy storage battery cluster using a state transition model based on the classified operating conditions and operating data; performing rolling optimization with the goal of minimizing the total system operating cost, wherein the total system operating cost includes a cost item calculated based on the state transition cost, and outputting a reference power command for each energy storage battery cluster; and generating the final power control signal for each energy storage battery cluster using a reinforcement learning controller based on the reference power command and the state transition cost. This method can quantify the internal electrochemical state transition cost of the battery and deeply integrate it with system-level economic dispatch and device-level intelligent control to maximize the value of the energy storage system throughout its entire lifecycle.
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