Distributed photovoltaic energy storage charging pile data processing and analyzing system and method

By constructing a nonlinear coupling relationship model between photovoltaic power generation and energy storage batteries, and adjusting the charging and discharging strategies in real time, the problem of difficulty in adapting to photovoltaic power generation fluctuations is solved, and the system efficiency and battery life are improved.

CN119696121AInactive Publication Date: 2025-03-25苏州久为新能源科技有限公司
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Patent Information

Application Number
CN202411969808.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-30
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing photovoltaic energy storage systems are difficult to adapt to fluctuations in photovoltaic power generation and nonlinear changes in the charging and discharging process of energy storage batteries in real time, resulting in low system operation efficiency and affecting battery life.

Method used

By arranging multi-point sensors in the photovoltaic power generation system and energy storage battery system, collecting multi-dimensional data in real time, combining deep learning algorithms to build a nonlinear coupling relationship model between photovoltaic power generation and energy storage battery, predict dynamic changes in real time, and dynamically adjust the charging and discharging strategies to optimize the charging and discharging strategies of energy storage batteries.

Benefits of technology

The best matching between photovoltaic power generation and energy storage batteries is achieved, the energy use efficiency of the system is improved, the battery life is extended, and the stability and reliability of the system are improved.

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Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a distributed photovoltaic energy storage charging pile data processing analysis system and method, and the system comprises a dynamic data collection module, a nonlinear coupling model construction module, a prediction and adjustment module, a strategy optimization module and a self-adaptive adjustment module. Multi-point sensors are arranged in a photovoltaic power generation system and an energy storage battery system, multi-dimensional data are collected in real time, a nonlinear coupling relation model between photovoltaic power generation and an energy storage battery is constructed by adopting a long-short-term memory network, and the state change of the system is predicted in real time. The charging and discharging strategy is optimized through the genetic algorithm, the service life of the battery is prolonged, the system efficiency is improved, the charging and discharging strategy is adaptively adjusted through the fuzzy logic control algorithm, the method has wide application prospects in photovoltaic power generation and energy storage battery systems, and the stability and the operation efficiency of the system can be remarkably improved.
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