Capacity Optimization Configuration of Hybrid Energy Storage Systems for Wind Farms Based on Improved k-means and Two-Stage Decomposition

Xi Zhang, Longyun Kang (), Xuemei Wang (), Yangbo Liu and Sheng Huang
Additional contact information
Xi Zhang: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Longyun Kang: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Xuemei Wang: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Yangbo Liu: School of Electric Power, South China University of Technology, Guangzhou 510641, China
Sheng Huang: School of Electric Power, South China University of Technology, Guangzhou 510641, China

Energies, 2025, vol. 18, issue 4, 1-24

Abstract: To address the issue of excessive grid-connected power fluctuations in wind farms, this paper proposes a capacity optimization method for a hybrid energy storage system (HESS) based on wind power two-stage decomposition. First, considering the susceptibility of traditional k-means results to initial cluster center positions, the k-means++ algorithm was used to cluster the annual wind power, with the optimal number of clusters determined by silhouette coefficient and Davies–Bouldin Index. The overall characteristics of each cluster and the cumulative fluctuations were considered to determine typical daily data. Subsequently, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) was used to decompose the original wind power data for typical days, yielding both the grid-connected power and the HESS power. To leverage the advantages of power-type and energy-type storage while avoiding mode aliasing, the improved pelican optimization algorithm—variational mode decomposition (IPOA-VMD) was applied to decompose the HESS power, enabling accurate distribution of power for different storage types. Finally, a capacity optimization model for a HESS composed of lithium batteries and supercapacitors was developed. Case studies showed that the two-stage decomposition strategy proposed in this paper could effectively reduce grid-connected power fluctuations, better utilize the advantages of different energy storage types, and reduce HESS costs.

Keywords: power fluctuations; hybrid energy storage system; k-means++; improved complete ensemble empirical mode decomposition with adaptive noise; variational mode decomposition; improved pelican optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/4/795/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/4/795/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:4:p:795-:d:1586678

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().