Energy supply side optimization management system and implementation method

By constructing a multi-energy flow topology constraint matrix and a safe operating boundary, and combining machine learning and graph neural networks to optimize scheduling, the inaccuracy and safety issues of traditional energy supply-side management systems under the volatility of renewable energy have been solved, achieving efficient and reliable energy management and energy efficiency optimization.

CN121303478BActive Publication Date: 2026-04-14BEIJING NORTH KOCHIN INFORMATION TECH CO LTD +1
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Patent Information

Application Number
CN202511870804.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-12
Publication Date
2026-04-14
Estimated Expiration
2045-12-12

AI Technical Summary

Technical Problem

Traditional energy supply-side management systems face challenges such as insufficient accuracy in predicting renewable energy output, large discrepancies between scheduling plans and actual conditions, separation of optimized scheduling and safety verification, low computational efficiency, lack of proactive defense and autonomous recovery capabilities, and extensive energy efficiency management when dealing with the volatility and intermittency of renewable energy.

Method used

By collecting energy supply-side source data, using a multi-level protocol adaptation engine and a dynamic rule matching engine to parse the data, constructing a multi-energy flow topology constraint matrix, combining GIS information for geographic grid division, using machine learning and knowledge graphs for data governance, and combining population aggregation and dispersion search algorithms and graph neural networks for optimized scheduling, a safe operation boundary is constructed, and dynamic trend prediction and optimal solution determination are achieved.

Benefits of technology

It has improved the accuracy and foresight of energy management, reduced uncertainty risks, improved computing efficiency and economic benefits, built a defense-in-depth system, significantly improved power supply reliability and energy efficiency management, and achieved accurate traceability of carbon flow and energy loss.

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Abstract

The application discloses an energy supply side optimization management system and an implementation method, and relates to the technical field of energy management. The method comprises the following steps: collecting source end data of an energy supply side, analyzing the source end data through a multi-level protocol adaptation engine and a dynamic rule matching engine, and extracting various energy characteristics and GIS information of the energy supply side; constructing a multi-energy flow topology constraint matrix based on the energy characteristics, performing fine-grained geographical grid division based on the GIS information of the energy supply side, and determining grid-level energy dynamic parameters; combining the multi-energy flow topology constraint matrix to perform dynamic trend adjustment on the grid-level energy dynamic parameters, and determining a grid-level energy dynamic prediction atlas; finding a best trade-off solution set through a population aggregation and dispersion search algorithm; combining a safe operation boundary to perform constraint solving on the best trade-off solution set, and determining an optimal solution of energy dispatching. The application improves the accuracy and foresight of energy management, accurately traces loss links, and drives the system to continuously evolve in the direction of low carbon and high efficiency.
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