Power system deep peak shaving adaptive control system

By constructing an adaptive control system for deep peak shaving in the power system, the problems of boiler combustion stability and boiler-turbine coordination in the deep peak shaving process of thermal power units have been solved, realizing safe, efficient and intelligent control of the units in the deep peak shaving process and improving the capacity to accept new energy sources.

CN122246870APending Publication Date: 2026-06-19华能(浙江)能源开发有限公司长兴分公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
华能(浙江)能源开发有限公司长兴分公司
Filing Date
2026-03-10
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies for deep peak shaving in thermal power units suffer from problems such as poor boiler combustion stability, difficulty in coordinating boiler and turbine operations, crude control strategies, and severe model mismatch, making it difficult to achieve safe, efficient, and intelligent control.

Method used

A deep peak-shaving adaptive control system for power systems is constructed, including modules for data acquisition, data preprocessing, fusion dynamic model, online parameter identification, adaptive gain scheduling, and control command generation. This enables real-time panoramic data perception, dynamic model adaptive correction, refined interval division of control gain, and multi-objective collaborative optimization.

Benefits of technology

It improves the control precision and efficiency of the unit during deep peak shaving, ensures equipment safety and environmental compliance, and enhances the ability to accept new energy sources.

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Abstract

This invention relates to the field of power system operation and control technology, and discloses a deep peak-shaving adaptive control system for power systems. The system includes a data acquisition module, a data preprocessing module, a fusion dynamic model module, an online parameter identification module, an adaptive gain scheduling module, a control command generation module, and an execution feedback module. Through multi-source heterogeneous data acquisition, data features are extracted and a fusion dynamic model is established, calculating the actual load rise and fall rate of the generating units, the real-time peak-shaving capacity margin, and the control deviation benchmark value. Based on the calculation results, online parameter identification is performed to correct the model, and adaptive scheduling control gain is implemented according to the unit status and peak-shaving interval. Finally, a multi-objective optimization algorithm generates coordinated commands for fuel, air volume, feedwater, and valve opening. The execution feedback module completes command issuance and closed-loop correction. Through the above closed-loop process, the system achieves safe, rapid, stable, and environmentally friendly operation under all deep peak-shaving conditions.
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