Demand side resource regulation and control method based on reinforcement learning
By applying a reinforcement learning algorithm based on Q-learning in demand response control, combined with dynamic parameter adjustment and feedback optimization, the problems of slow convergence speed, poor control reliability and insufficient multi-objective optimization balance capability in the existing technology are solved, and efficient and accurate load regulation is achieved.
Patent Information
- Application Number
- CN202411742838.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-03-25
AI Technical Summary
The existing demand response control methods based on reinforcement learning have slow convergence speed due to fixed learning parameters, single control constraints affect control reliability, lack of effective feedback optimization mechanisms, and it is difficult to balance multiple control targets such as frequency adjustment, voltage adjustment and energy saving when dealing with multi-objective optimization problems.
Using a reinforcement learning algorithm based on Q-learning, an environmental model is established and state, action and reward functions are defined by collecting historical power consumption data, real-time load information, weather forecast data and user behavior patterns of the power system. Dynamically adjust learning parameters and load regulation constraints, and introduce feedback optimization mechanisms to respond to load fluctuations.
It realizes precise control of power demand response load regulation, improves the convergence speed of the algorithm and the reliability of control, enhances the balancing ability of multi-objective optimization, and improves the execution efficiency and control accuracy of demand response.