Energy storage scheduling method and device of combined cooling heating and power system

By using LSTM in cogeneration systems for load prediction, combined with deep reinforcement learning and MPC for real-time energy storage scheduling, the problem of insufficient dynamic response capabilities of traditional systems when facing rapidly changing loads is solved, and more efficient energy utilization and cost reduction are achieved.

CN119692885APending Publication Date: 2025-03-25STATE GRID JIANGSU ELECTRIC POWER CO LTD CHANGZHOU BRANCH
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Patent Information

Application Number
CN202411791645.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-06
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

Traditional cogeneration systems lack dynamic response capabilities in the face of rapidly changing load demands, resulting in low scheduling flexibility and high energy waste.

Method used

Long-term memory neural network (LSTM) is used for load prediction, combined with deep reinforcement learning algorithms and model prediction control (MPC), and real-time energy storage scheduling is performed under the framework of rolling time domain optimization.

Benefits of technology

It significantly improves energy utilization efficiency, reduces operating costs and carbon emissions, and enhances the system's response speed to load fluctuations.

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Abstract

The invention relates to the technical field of energy system management, and discloses an energy storage scheduling method and device of a combined cooling heating and power system, and the method comprises the following steps: S1, collecting and building a physical model and data of each device of the combined cooling heating and power system; s2, load prediction is carried out by using a long short-term memory (LSTM) neural network; s3, based on a load prediction result, a scheduling optimization objective function is established, and the objective function defines an optimization direction and an evaluation standard; s4, scheduling the charging and discharging processes of the energy storage device by adopting a deep reinforcement learning algorithm; s5, performing real-time scheduling adjustment by using model predictive control; and S6, using rolling horizon optimization to continuously optimize the scheduling strategy on each time step. According to the method, the future load is accurately predicted by adopting the long short-term memory neural network LSTM, and real-time scheduling is realized by combining model prediction control MPC and rolling horizon optimization.
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