一种综合能源系统低碳经济调度方法及系统
By constructing a dynamic energy hub model and an energy flow-carbon flow two-layer model, combined with a backpropagation neural network and a tiered carbon trading mechanism, the problems of low scheduling accuracy and insufficient low-carbon optimization caused by the influence of equipment operating conditions in existing technologies are solved, and efficient, low-carbon, and economical scheduling of integrated energy systems is realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUNAN UNIV
- Filing Date
- 2025-06-03
- Publication Date
- 2026-07-17
AI Technical Summary
Existing integrated energy systems neglect the impact of equipment operating conditions, resulting in low dispatch accuracy and a lack of effective low-carbon optimization dispatch methods, making it difficult to balance economic efficiency and low carbon emissions.
A dynamic energy hub model based on backpropagation neural network is constructed, combined with a two-layer energy flow-carbon flow model, and linearization processing and a tiered carbon trading mechanism are adopted. The scheduling strategy is optimized through iterative solution, which accurately characterizes the variable efficiency characteristics of equipment and explores the potential of the demand side.
It has improved the accuracy and economy of integrated energy system dispatch, promoted low-carbon dispatch, achieved synergistic optimization of energy flow and carbon flow, and supported the sustainable development of the energy system.
Smart Images
Figure CN120875298B_ABST