考虑碳排不确定性的电力-碳排-绿证多时间尺度协同滚动优化方法、装置、设备及介质
By employing a multi-timescale collaborative rolling optimization method for electricity, carbon emissions, and green certificates, and utilizing historical data and the XGBOOST algorithm to generate operational scenarios, the uncertainty of carbon emissions in the power system was resolved, enabling the low-carbon transformation and market optimization of the power system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA SOUTHERN POWER GRID COMPANY
- Filing Date
- 2026-05-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies have failed to effectively address the uncertainty of carbon emissions in power systems, leading to a disconnect between electricity, carbon, and certificates at different time scales, which affects the low-carbon transformation and market optimization of power systems.
By collecting historical wind, solar and hydropower data, simulating future power output changes, generating multi-timescale operation scenarios, and combining the XGBOOST algorithm and collaborative mutual assistance mechanism, we can perform collaborative rolling optimization of electricity, carbon emissions and green certificates, determine the reserve pool capacity configuration of carbon quotas and green certificate plans, and achieve power balance at multiple time scales.
It effectively reduced the uncertainty of carbon emissions, improved the power system's ability to transition to low carbon, ensured that power generation, carbon quotas and green certificate programs were more aligned with actual operating conditions, and broke down the disconnect between electricity, carbon emissions and green certificates.
Smart Images
Figure CN122175101B_ABST