基于多能流协同的园区综合能源优化调度系统
By using a clear-sky model and LSTM neural network to predict photovoltaic output and identify load types, the energy dispatching in the park was optimized, solving the problems of photovoltaic output fluctuations and natural gas pressure changes, and achieving efficient and economical energy management and stable power supply.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANJIN ANJIE PUBLIC FACILITIES SERVICE CO LTD
- Filing Date
- 2026-04-17
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies are insufficient to effectively address the supply-side uncertainties of the park's energy system caused by fluctuations in photovoltaic output and changes in natural gas pressure. They lack high-precision forecasting mechanisms and load-side elasticity quantification methods, resulting in high operating costs and insufficient safety and stability.
A comprehensive energy optimization and scheduling system for the park is adopted, which uses a clear-sky model and LSTM neural network to predict photovoltaic output and combines load time fluctuation and substitution elasticity coefficient. By constructing an optimization scheduling model, the system can accurately identify photovoltaic power generation performance deviations and load types, balance supply and demand, and optimize the electricity/gas allocation ratio.
It improves the accuracy of photovoltaic power generation forecasting, identifies transferable and alternative loads, reduces operating costs, enhances system flexibility and safety margin, ensures supply and demand balance, and improves the stability and economic benefits of the park's energy system.
Smart Images
Figure CN122047657B_ABST