System and method for performing AGC instruction dynamic scheduling energy storage charging and discharging based on photovoltaic power generation volatility

By combining a hybrid energy storage system with an AGC scheduling and control module and using data-driven prediction, the problems of grid instability and shortened battery life caused by the volatility of photovoltaic power generation have been solved, and the fluctuations of photovoltaic power have been effectively mitigated and the battery life extended.

CN121769967APending Publication Date: 2026-03-31KUNMING UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-05
Publication Date
2026-03-31

AI Technical Summary

Technical Problem

The volatility of photovoltaic power generation leads to grid frequency instability, voltage sags and harmonic interference. Traditional energy storage systems are inefficient and have shortened battery life. Existing control strategies cannot effectively mitigate photovoltaic power fluctuations.

Method used

A hybrid energy storage system (supercapacitor and lithium-ion battery) and an AGC scheduling and control module are used, combined with an LSTM-Transformer model to predict photovoltaic power generation. Through multi-timescale collaborative control and health adaptive management, the charging and discharging strategy is dynamically adjusted to smooth out photovoltaic power fluctuations.

Benefits of technology

It significantly improves grid frequency stability, extends the lifespan of energy storage batteries, reduces carbon emissions, and enhances the system's economy and robustness.

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Abstract

The invention relates to the technical field of intelligent control of new energy power systems, in particular to a system and method for performing AGC instruction dynamic scheduling energy storage charging and discharging based on photovoltaic power generation volatility, and the system comprises a photovoltaic power generation unit, an intelligent inverter, a hybrid energy storage system composed of a super capacitor and a lithium ion battery, and an AGC scheduling control module. The photovoltaic power is predicted through an LSTM model, and an optimal charging and discharging instruction of the energy storage system is generated by adopting a mixed integer linear programming optimization algorithm and taking minimization of the power grid electricity purchasing cost, the frequency deviation and the battery health degree loss as targets. Through the multi-time scale cooperation of the transient response of the super capacitor and the steady-state scheduling of the lithium battery, the photovoltaic power fluctuation is effectively stabilized, the frequency stability of the power grid is ensured, and the service life of the energy storage battery is remarkably prolonged through a self-adaptive management strategy. The system also has multiple fault-tolerant mechanisms, supports multi-energy complementary access, and improves the reliability and economy of the system.
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Citation Information

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