A distributed energy optimization management method and system

Through the combination of Q-Learning particle swarm optimization algorithm and short-time Fourier transform algorithm, the harmonics generated by nonlinear loads in distributed energy systems are accurately identified and converted, which solves the problem of inaccurate harmonic identification in traditional methods, and realizes efficient harmonic energy storage, improving the stability and power quality of the power grid.

CN119324531BActive Publication Date: 2025-07-11STATE GRID ZHEJIANG ELECTRIC POWER CO LTD JINYUN COUNTY POWER SUPPLY CO +1
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Patent Information

Application Number
CN202411875740.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-19
Publication Date
2025-07-11
Estimated Expiration
2044-12-19

AI Technical Summary

Technical Problem

Traditional harmonic detection and management methods are difficult to accurately identify the harmonic components and their dynamic changes generated during switching devices when dealing with nonlinear loads in distributed energy systems, resulting in aggravation of harmonic pollution and affecting the power loss and stability of the power grid.

Method used

The Q-Learning particle swarm optimization algorithm is used to determine the optimal switching control strategy of the switching device, and the frequency domain analysis is performed by combining the short-time Fourier transform algorithm. The adaptive threshold strategy is used to extract and convert the excessive harmonic signal, and store it in the energy storage device.

Benefits of technology

Accurate identification and efficient energy storage of harmonics generated by nonlinear loads are achieved, the generation of harmonics in distributed energy systems is reduced, and the power quality and operation stability of the power grid are improved.

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Abstract

The present invention relates to the technical field of energy management, and particularly to a distributed energy optimization management method and system, which includes determining an optimal switching control strategy for switching devices by using a Q-Learning particle swarm optimization algorithm; performing intermittent conduction and turn-off control on the switching devices according to the optimal switching control strategy, obtaining the time-domain action signals of the switching devices, and extracting the harmonic spectrum data during the conduction and turn-off processes of the switching devices by using a short-time Fourier transform algorithm; obtaining the total harmonic power according to the harmonic spectrum data, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting the exceeding-standard harmonic signals; converting the exceeding-standard harmonic signals into energy storage voltage and current adapted to the energy storage device, and storing the energy storage voltage and current in the energy storage device. Through the management and analysis of harmonics, the present invention realizes the accurate identification and efficient energy storage of harmonics generated by non-linear loads, thereby realizing the optimization management of the distributed energy system.
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