Integrated energy system double-layer optimization operation method based on multivariable predictive control

By introducing a two-layer optimization operation method of multivariate predictive control into the integrated energy system, combining the dynamic scheduling layer and the real-time control layer, the problem of system instability caused by the uncertainty of renewable energy power generation is solved, and the system achieves stable and economical operation.

CN115271194BActive Publication Date: 2026-02-03SOUTHEAST UNIV
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Patent Information

Application Number
CN202210862400.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-21
Publication Date
2026-02-03
Estimated Expiration
2042-07-21

AI Technical Summary

Technical Problem

The randomness and uncertainty of renewable energy power generation lead to errors between predicted and actual data, affecting the stable operation of the integrated energy system. There are discrepancies between the existing dispatch layer model and the actual equipment model, making it difficult to guarantee the smooth and economical operation of the system.

Method used

A two-layer optimization operation method based on multivariable predictive control is adopted. The dynamic scheduling layer performs rolling scheduling based on the dynamic characteristic model of the equipment, and the real-time control layer corrects the power scheduling command through multivariable predictive control to reduce the impact of prediction deviation and model deviation.

Benefits of technology

It enables real-time control of the integrated energy system, reduces the impact of renewable energy generation and load forecasting deviations on system operation, and ensures reliable power supply and stable operation of the system.

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Abstract

The present application relates to a kind of integrated energy system double-layer optimization operation method based on multivariable predictive control, comprising: the latest operation data of sampling system in dynamic scheduling layer, based on the dynamic characteristic model of equipment that can be used to describe the heating link of system equipment, according to the latest renewable energy and load demand forecast data within day, with the minimum economic cost and the minimum of dynamic supply-demand deviation index as optimization goal, with power balance constraint and equipment characteristic constraint as constraint, carry out within day rolling scheduling, provide the power scheduling instruction of each equipment in the next scheduling period;Real-time control layer takes the heat-electricity coupling link of gas internal combustion engine and its waste heat recovery equipment and heat pump as control object, based on multivariable predictive control method, the power scheduling instruction issued by dynamic scheduling layer is corrected in real time, using the power scheduling instruction after correction acts on system, can reduce the disturbance influence brought by prediction deviation, improves system energy supply accuracy and operating stability.
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