Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Honglu Zhu; Ruyin Hou; Tingting Jiang; Qingquan Lv

doi:10.1016/j.gloei.2022.01.004

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Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Smart Grid | 更新时间：2026-09-21

- Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications
- Global Energy Interconnection Vol. 4, Issue 6, Pages: 608-618(2021)
- 作者机构：
  
  1. School of New Energy, North China Electric Power University,Beijing,PR China,102206
  2. State Key Laboratory of Alternate Electrical Power Systems with Renewable Energy Sources, North China Electric Power University,Beijing,PR China,102206
  3. State Grid Gansu Electric Power Research Institute,Lanzhou,PR China,730070
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2022.01.004
  CLC：
- Online First：08 March 2022，
  
  Published：2021
- 稿件说明：
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Honglu Zhu, Ruyin Hou, Tingting Jiang, et al. Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications[J]. Global Energy Interconnection, 2021, 4(6): 608-618.
DOI：

Honglu Zhu, Ruyin Hou, Tingting Jiang, et al. Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications[J]. Global Energy Interconnection, 2021, 4(6): 608-618. DOI： 10.1016/j.gloei.2022.01.004.

摘要

Abstract

Compensating for photovoltaic (PV) power forecast errors is an important function of energy storage systems. As PV power outputs have strong random fluctuations and uncertainty

it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods. In this paper

a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation. A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance. The power forecast errors in different weather types are analyzed

and an energy storage system is used to compensate for the errors. The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system; the power and capacity of the energy storage unit are calculated at different confidence levels. The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction. The proposed method was validated using actual operating data from a PV power station. The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

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