Energy storage system with dynamic management based on solar input

By combining photovoltaic prediction calibration and energy storage twin assessment modules, a multi-objective optimization decision-making system is constructed, which enables adaptive management of photovoltaic and energy storage systems, solves the problems of low photovoltaic absorption rate and insufficient system robustness, and improves the system's operating efficiency and safety.

CN122394029APending Publication Date: 2026-07-14YANTAI HANZHI NEW ENERGY TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
YANTAI HANZHI NEW ENERGY TECHNOLOGY CO LTD
Filing Date
2026-04-23
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing battery management systems cannot adapt to ultra-short-term fluctuations in solar irradiance and changes in grid demand, resulting in low photovoltaic absorption rates, delayed charging and discharging decisions, and an inability to achieve coordinated optimization of the source-grid-load-storage system. Furthermore, the lack of cluster-level full-state dynamic mapping and multi-dimensional consistency assessment leads to overcharging and over-discharging of some units, accelerated cycle life degradation, and insufficient system robustness and safety.

Method used

A photovoltaic prediction calibration module is used to predict photovoltaic power at multiple time scales. Combined with an energy storage twin assessment module, a dynamic mapping and consistency assessment of the entire state are performed. A multi-objective optimization decision-making system is constructed through a source-grid-load-storage decision-making module to realize adaptive time-sharing charging and discharging plans. Combined with an energy storage collaborative control module, cluster-level active balancing and protection are performed, protection thresholds are dynamically adjusted, and a three-factor battery degradation model is constructed for lifespan optimization.

Benefits of technology

It has achieved efficient utilization of photovoltaic resources, improved photovoltaic absorption rate, stabilized available system capacity, extended the life of energy storage system, ensured safe system operation and fault early warning, and improved system robustness and security.

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Abstract

The application belongs to the technical field of battery control, and discloses an energy storage collaborative dynamic management system based on solar input. A photovoltaic prediction calibration module comprehensively collects meteorological, photovoltaic component and battery charging related data. Based on a time sequence prediction model suitable for a solar scene and an irradiation mutation recognition technology, multi-time scale photovoltaic power accurate prediction is completed. The dynamic calibration photovoltaic adjustable power interval, the maximum charging power threshold and the power abandonment early warning threshold are determined. A source network load storage decision module integrates photovoltaic, energy storage, power grid and load full-dimensional data, constructs a multi-objective optimization decision system, solves the optimal operation strategy through an intelligent optimization algorithm, generates an adaptive time-sharing charging and discharging plan, and responds to irradiation fluctuation, power grid demand and load power consumption characteristic changes in real time. An energy storage twin evaluation module collects full-dimensional operation data of energy storage units, constructs an electrochemical, thermal and force digital twin, and real-time maps the operation state and health characteristics of each group of energy storage units.
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