Low-carbon park control method and system based on digital twinning and dynamic carbon footprint

By combining digital twin technology and dynamic carbon footprint, the problems of static carbon accounting and rigid control strategies in the optimized operation of low-carbon parks have been solved. Dynamic carbon management and adaptive optimization of the low-carbon park system have been realized, improving the system's operating efficiency and carbon emission reduction capabilities under uncertain environments.

CN122092388BActive Publication Date: 2026-07-03STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
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Patent Information

Application Number
CN202610533536.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-22
Publication Date
2026-07-03
Estimated Expiration
2046-04-22

AI Technical Summary

Technical Problem

The existing low-carbon industrial park optimization operation suffers from problems such as static carbon accounting, disconnect between day-ahead planning and real-time control, and rigid control strategy parameters. As a result, the optimization model cannot identify the temporal differences in carbon emissions, is unable to cope with the fluctuations of renewable energy and load, and the system operates rigidly and lacks adaptability.

Method used

A low-carbon park control method based on digital twins and dynamic carbon footprint is adopted. By constructing a digital twin simulation model and combining dynamic carbon cost and reinforcement learning, a two-level closed-loop optimization of day-ahead optimization and real-time control is achieved. Carbon accounting is performed using dynamic marginal carbon emission factors, and carbon weights are dynamically adjusted through reinforcement learning agents. A hybrid intelligent optimization algorithm is constructed to improve the system's adaptability.

Benefits of technology

It enables dynamic sensing and optimization of carbon accounting, improves the system's robustness and carbon efficiency stability under uncertain environments, can proactively avoid high-carbon periods, adaptively adjust operating strategies, and enhance the intelligence level and long-term carbon efficiency performance of the park's energy system.

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Abstract

The application relates to a low-carbon park control method and system based on digital twinning and dynamic carbon footprint, a digital twinning simulation model mapped with a physical entity of a low-carbon park is constructed; in a day-ahead optimization stage, a day-ahead operation plan of a park energy system is formulated with the objective of minimizing total day operation cost in a scheduling period, serving as a baseline and operation boundary for real-time control rolling optimization; in a real-time control stage, a model predictive control is adopted to perform rolling optimization on the day-ahead operation plan based on a predicted curve of system state in each control period, the first control instruction in an optimized sequence obtained in the current control period is applied to an actual system, and a dynamic carbon cost weight in a model predictive control objective function is dynamically adjusted by using a reinforcement learning intelligent agent. Compared with the prior art, the application realizes accurate perception, forward deduction and intelligent decision of a whole process of the park energy system, and improves overall optimization performance and operation robustness of the system in an uncertain environment.
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