Region energy storage system operation regulation and control method and device

Through deep learning prediction and multi-objective optimization, intelligent control of the energy storage system in the distribution area is achieved, which solves the power quality problem in the distribution area, extends battery life, reduces operating costs, and adapts to changes in the operating characteristics of the distribution area.

CN122456589APending Publication Date: 2026-07-24STATE GRID HENAN INTEGRATED ENERGY SERVICE CO LTD
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Patent Information

Application Number
CN202610506552.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-16
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing control methods for distribution area energy storage systems cannot effectively predict power quality problems, lack multi-objective collaborative optimization capabilities, have poor adaptability, cannot adapt to dynamic changes in distributed power sources and loads, and ignore the lifespan of energy storage batteries, leading to power quality problems and equipment aging.

Method used

By employing a deep learning prediction model combined with multi-objective optimization, ultra-short-term predictions are made by collecting multi-source data, a multi-objective optimization model is constructed, the optimal charging and discharging power command sequence is generated, and the model parameters are monitored and updated adaptively in real time to realize intelligent control of the energy storage system in the distribution area.

Benefits of technology

It effectively solves problems such as line overload, three-phase imbalance and voltage exceeding limits, extends the life of energy storage batteries, reduces operating costs, improves power quality and control efficiency, and adapts to changes in the operating characteristics of distribution areas.

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Abstract

The invention provides a transformer area energy storage system operation regulation and control method and device, and belongs to the technical field of power system regulation and control. The method comprises the steps that transformer area historical data are collected, and a historical database is constructed; training a prediction model based on a historical database, inputting the transformer area real-time data into the prediction model for ultra-short-term prediction, and outputting future power supply output and load demand prediction values; on the basis of power output and load demand predicted values, eliminating line overload, three-phase imbalance and voltage out-of-limit as optimization objectives, and considering the service life of an energy storage battery and power utilization economy, constructing and solving a multi-objective model to obtain an instruction sequence; issuing the instruction sequence to a transformer area energy storage system for execution; actual data are collected in real time; and calculating execution deviation according to the actual data of the transformer area, and adaptively updating and correcting the parameters of the prediction model and the parameters of the multi-target model. Through intelligent prediction and optimization regulation and control, the power quality problem of the transformer area is solved, the service life of the energy storage battery is prolonged, and the operation cost is reduced.
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