Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error

Yifan Bian; Peng Lu; Lirong Xie; Jiahao Ye; Lan Ma; Jianbin Yang

doi:10.17775/CSEEJPES.2022.08300

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Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error

Regular Papers | 更新时间：2026-10-10

- Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error
- Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第5期页码：2308-2317
- 作者机构：
  
  1. Engineering Research Center for Energy Generation & Grid of Ministry of Education, Xinjiang University,Urumqi,China
  2. State Key Laboratory of Power System, Generation Equipment Tsinghua University,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.08300
  中图分类号：
- 纸质出版：2026-09-01
- 稿件说明：
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Yifan Bian, Peng Lu, Lirong Xie, 等. Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(5):2308-2317.

Yifan Bian, Peng Lu, Lirong Xie, et al. Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error[J]. CSEE Journal of Power and Energy Systems, 2026, 12(5): 2308-2317.
Yifan Bian, Peng Lu, Lirong Xie, 等. Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(5):2308-2317. DOI： 10.17775/CSEEJPES.2022.08300.

Yifan Bian, Peng Lu, Lirong Xie, et al. Bi-Level Optimal Configuration of Hybrid Shared Energy Storage Capacity in Wind Farms Considering Prediction Error[J]. CSEE Journal of Power and Energy Systems, 2026, 12(5): 2308-2317. DOI： 10.17775/CSEEJPES.2022.08300.

摘要

Abstract

In wind farms

hybrid energy storage (HES) can effectively mitigate the fluctuation and intermittency of wind power output and effectively compensate for the prediction errors of wind power. However

the high cost of HES has prevented its large-scale adoption. Inspired by the sharing economy

this paper introduces the concept of hybrid shared energy storage (HSES) in wind farms. A rolling optimization (RO) strategy is formulated to obtain the reference power curve of the HES

and a bi-level optimal configuration model of HSES is established to minimize the wind farm's daily operating cost and optimize the compensation's performance for the prediction errors. The upper-level model aims to obtain the optimal rental capacity of HSES

and the lower level is an energy trading mechanism to optimize the trading power of the wind farm in real time. The simulation results reveal the excellent performance and economy of the proposed HSES model for wind farms.

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