Partition optimization scheduling control method and system for energy storage power station

By constructing a multi-scale dynamic baseline and gradient phase tracking mechanism, the problem of coupling and resonance between short-term fluctuations and long-term trend data in the zonal optimization scheduling control of energy storage power stations was solved, realizing the stability and accuracy of the model and improving the stability and operational efficiency of power grid load forecasting.

CN121642907AInactive Publication Date: 2026-03-10INNER MONGOLIA CHINA ELECTRIC ENERGY STORAGE INTELLIGENT EQUIPMENT MANUFACTURING CO LTD +1
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Patent Information

Application Number
CN202511765102.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-27
Publication Date
2026-03-10
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the process of zonal optimization scheduling and control of energy storage power stations, the coupling resonance effect of short-term fluctuation data and long-term trend data leads to unstable gradient of prediction model parameters, resulting in serious consequences such as grid frequency imbalance and scheduling link collapse.

Method used

A multi-scale dynamic baseline is constructed, a cross-scale coupling mapping matrix is ​​generated through a time embedding operator, a gradient phase tracking network is established, and an adaptive weight transfer mechanism and a causal residual playback chain are introduced to achieve continuous alignment and adaptive phase difference correction of the model between signals at different time scales, thereby preventing gradient oscillation and parameter collapse.

Benefits of technology

It improves the temporal continuity and dynamic accuracy of load forecasting, reduces the risk of dispatch link instability, and enhances the stability and efficiency of power grid operation.

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Abstract

The invention discloses a partition optimization scheduling control method and system for an energy storage power station, and relates to the technical field of power grid load prediction scheduling, and the method comprises the following steps: constructing a multi-scale dynamic baseline of load data, carrying out the phase calibration of a short-time fluctuation signal and a long-term trend signal under a unified time coordinate, and obtaining a multi-scale dynamic baseline; generating a cross-scale coupling mapping matrix by using a time embedding operator based on a calibration result; and establishing a gradient phase tracking network based on the cross-scale coupling mapping matrix. According to the method, time alignment and self-adaptive correction of short-time fluctuation and long-term trend signals are achieved through a multi-scale dynamic baseline and gradient phase tracking mechanism, weight energy balance is kept, and prediction continuity and accuracy are remarkably improved. Meanwhile, a causal residual error playback chain and time scale self-folding mapping enable the model to have self-correction and self-stabilization capabilities, sub-models can be automatically split during phase resonance, multi-scale response coordination is ensured, and the stability and operation efficiency of power grid load prediction scheduling are improved.
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