A multi-energy system scheduling method and device, electronic equipment and storage medium
By dividing the multi-energy system into sub-regions and solving them in parallel and iteratively, the serial dependency problem in large-scale multi-energy systems is solved, and fast and accurate multi-energy system optimization scheduling is achieved.
CN122840520APending Publication Date: 2026-09-29ELECTRIC POWER RES INST OF GUANGDONG POWER GRID CO LTD
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Patent Information
- Application Number
- CN202610989988.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-03
- Publication Date
- 2026-09-29
AI Technical Summary
Technical Problem
Existing multi-energy system optimization scheduling methods suffer from serial dependencies under large-scale and complex inter-regional coupling relationships, making it difficult to fully utilize parallel computing resources and resulting in low optimization scheduling speed.
Method used
The multi-energy system is divided into several sub-regions, and the cost optimization function of each sub-region is solved in parallel iteratively. Parallel computing improves the solution speed, and global boundary variables are updated synchronously between sub-regions to achieve system consistency.
Benefits of technology
It improves the speed and efficiency of optimal scheduling of multi-energy systems, enabling fast and accurate scheduling decisions in large-scale complex systems.
✦ Generated by Eureka AI based on patent content.
Abstract
The application discloses a multi-energy system scheduling method and device, electronic equipment and storage medium, and belongs to the technical field of multi-energy system operation control. The method comprises the following steps: dividing a multi-energy system into a plurality of sub-regions, and constructing a regional cost optimization model and power, natural gas, carbon intensity and energy conversion constraints based on initial operation parameters of each sub-region; under the above constraints, the cost model of each sub-region is solved by parallel iteration to obtain final node state variables; in each iteration, the node state variables are updated according to the dual variables, global boundary variables and penalty coefficients of the previous round, and the dual variables and global boundary variables are updated accordingly; the iteration is converged through residual judgment, and the final node state variables of each sub-region are output for system scheduling. The application can reduce the serial dependence of centralized solution, improve the parallel computing efficiency of multi-energy system scheduling, and further improve the optimization scheduling speed of the multi-energy system.
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