Demand response scheduling decision method and apparatus based on electricity-carbon coupling, device, and system

By employing an electricity-carbon joint demand response scheduling decision-making method, and utilizing Markov decision-making and deep reinforcement learning to optimize the user-side demand response model, the problem of insufficient incentive mechanisms in existing technologies is solved. This enables coordinated adjustment of user-side load and distributed energy resources, thereby improving system flexibility and resource utilization efficiency.

WO2026123453A1 Publication Date: 2026-06-18GUANGZHOU INST OF ENERGY CONVERSION CHINESE ACAD OF SCI
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
GUANGZHOU INST OF ENERGY CONVERSION CHINESE ACAD OF SCI
Filing Date
2025-02-06
Publication Date
2026-06-18

AI Technical Summary

⚠Technical Problem

Existing research on the coordinated optimization of demand response and distributed energy resources suffers from insufficient incentive mechanisms, failing to effectively motivate users to actively participate, resulting in unbalanced resource allocation and unsatisfactory response effects. It also lacks in-depth modeling of user demand response behavior and consideration of actual constraints.

⚗Method used

A demand response scheduling decision-making method based on electricity-carbon joint is adopted. By acquiring the marginal electricity price signal of the electricity-carbon joint node at the distribution network end, a user-side demand response model is constructed. Markov decision and deep reinforcement learning methods are used for iterative optimization to obtain a scheduling decision scheme, which coordinates user-side load and distributed energy generation.

🎯Benefits of technology

It has enabled the mobilization of user response enthusiasm, coordinated the adjustment of distributed energy and user load, improved the system's flexibility and user participation, and promoted the effective application of distributed energy and the rational allocation of resources.

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Abstract

The present application relates to the field of distributed energy response scheduling, and discloses a demand response scheduling decision method and apparatus based on electricity-carbon coupling, a device, and a system. The method comprises: acquiring information data of a user end, wherein the information data comprises an electricity-carbon coupling locational marginal price signal corresponding to a distribution system operator, the information data is determined on the basis of a locational marginal price model of electricity-carbon coupling at a distribution system end, and the locational marginal price model is determined by the distribution system operator by using a distribution system branch flow algorithm; determining a user-side demand response model, wherein the user-side demand response model is constructed on the basis of the information data and with the objective of minimizing a power factor; and using Markov decision to perform mathematical transformation on the user-side demand response model, and using a deep reinforcement learning method to perform iterative optimization calculation to obtain a scheduling decision scheme, so as to coordinate a user-side load of the user end with distributed generation. The present application aims to implement coordinated adjustment and scheduling of distributed energy and user loads.
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