User-side optical storage system capacity planning method and system

By determining the relationship between photovoltaic power generation and environmental factors through K-Means clustering and Pearson correlation coefficient method, and combining it with particle swarm optimization algorithm to optimize energy storage system capacity, the problem of complex calculation and low efficiency of energy storage system in existing technologies is solved, and efficient photovoltaic-energy storage system capacity planning is realized.

CN122371258APending Publication Date: 2026-07-10JIANGXI THERMAL POWER CONSTR CORP
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Patent Information

Application Number
CN202610425639.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-02
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing energy storage systems involve complex calculations and have low computational efficiency, making it difficult to conduct reasonable capacity planning for user-side photovoltaic-storage systems.

Method used

The relationship between photovoltaic power generation and environmental factors is determined by K-Means clustering algorithm and Pearson correlation coefficient method. The capacity configuration of energy storage system is optimized by combining particle swarm optimization algorithm. Energy storage planning model is constructed by using load shortage rate and energy overflow ratio as indicators.

Benefits of technology

It simplifies the calculation process, improves calculation efficiency, ensures the normal operation of energy storage systems, and provides highly reliable capacity configuration results.

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Abstract

This invention provides a user-side photovoltaic-storage system capacity planning method and system. It utilizes the K-Means clustering algorithm to process the sample set and determine daily data; employs the Pearson correlation coefficient method to determine the relationship between environmental factors and daily data, thereby determining the power generation capacity, and using this power generation capacity as the basis for determining photovoltaic power generation; determines the energy storage capacity based on photovoltaic power generation and load usage; uses load deficit rate and energy overflow ratio as indicators, combined with the state of charge (SOC) of the energy storage system, to control the energy storage capacity based on photovoltaic usage and load usage; obtains the average annual operating cost of the energy storage system; constructs an energy storage planning model based on energy storage power constraints, energy storage SOC constraints, and energy storage capacity constraints, according to the average annual operating cost; and uses the particle swarm optimization algorithm to solve the energy storage planning model and plan the energy storage capacity. This invention enables the rational planning of energy storage in energy storage systems.
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