基于云边协同的分布式储能资源动态聚合与调度系统
The distributed energy storage resource dynamic aggregation and scheduling system with cloud-edge collaboration solves the problems of poor real-time performance and multi-objective consideration in traditional energy storage scheduling methods in remote environments. It achieves rapid dynamic scheduling and global optimal control, improving system stability and energy storage resource utilization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANYUN INTELLIGENT TECHNOLOGY (SHANDONG) CO LTD
- Filing Date
- 2026-01-20
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional energy storage scheduling methods suffer from poor real-time performance in off-grid or weak-grid environments such as remote mountainous areas and island border defense sites. Limited computing resources for edge autonomous regulation make it difficult to handle large-scale data analysis and prediction, leading to overcharging or over-discharging of energy storage, affecting system stability. Furthermore, traditional unified scheduling strategies cannot take into account multiple objectives, resulting in low utilization of energy storage resources and accelerated lifespan degradation.
A distributed energy storage resource dynamic aggregation and scheduling system based on cloud-edge collaboration is adopted. Through edge data acquisition, processing, perception modeling, response evaluation and scheduling intent generation, combined with multi-objective optimization algorithm and particle swarm optimization, a fast dynamic scheduling and global optimal control are achieved, and a closed-loop scheduling adaptive system is constructed.
It achieves dynamic identification of photovoltaic node power fluctuations and response matching of multi-source energy storage units, improves the local response speed and stability of the system under fluctuating environment, optimizes operating costs, lifespan loss and photovoltaic node power deviation, and forms a scheduling strategy with good practicality and scalability.
Smart Images
Figure CN121886513B_ABST