SinoTechIntel Academic Portal
Open AccessDOI: 10.16183/j.cnki.jsjtu.2025.150Original Research

Optimization of Wind Power Generation Systems with Hybrid Energy Storage and Grid Integration

ZHANG Wei¹,LI Ming¹,WANG Fang¹,CHEN Yu¹

Institute of Electrical Engineering, Chinese Academy of Sciences

Read Executive PreviewQuick FAQ
Optimization of Wind Power Generation Systems with Hybrid Energy Storage and Grid Integration
Graphical Abstract / Figure
Published In
Academic Research Journal
Published:January 15, 2025Edition:Vol. 32, Issue 1 • pp. 100-112Citation:ZHANG Wei et al. (2025), Academic Research Journal
Impact FactorPeer-Reviewed Core
Sponsored Research Partner

Key Takeaways & Executive Findings

  • • Hybrid energy storage systems significantly reduce power fluctuations in wind power generation. • The proposed control strategy enhances grid stability and power quality. • Optimization of storage sizing leads to cost-effective and efficient operation. • Simulation results validate the effectiveness of the system under dynamic wind conditions.
Sponsored Research Highlight

Abstract

This paper presents a comprehensive study on the optimization of wind power generation systems integrated with hybrid energy storage and grid connection. The proposed system combines battery and supercapacitor storage to smooth power fluctuations and enhance grid stability. A novel control strategy is developed to manage energy flow and improve overall efficiency. Simulation results demonstrate significant improvements in power quality and system reliability under varying wind conditions. The findings provide valuable insights for the design and operation of renewable energy systems.

1. Introduction

Wind power generation has become a key component of renewable energy portfolios worldwide. However, the inherent variability and intermittency of wind resources pose significant challenges to grid stability and power quality. To address these issues, energy storage systems are essential for smoothing power output and providing ancillary services. Hybrid energy storage systems, combining batteries and supercapacitors, offer complementary characteristics that can effectively mitigate power fluctuations and extend system lifetime.

This paper focuses on the optimization of a wind power generation system integrated with a hybrid energy storage system and grid connection. The objective is to develop a control strategy that optimally manages energy flow, reduces power fluctuations, and enhances overall system efficiency. The study also investigates the sizing of storage components to achieve cost-effective operation. Simulation results are presented to demonstrate the performance of the proposed system under various wind conditions.

SinoTechIntel Interactive Document Reader
Page 1–5 of Preview
100%
Download Full PDF

Loading authentic research manuscript (Pages 1–5)...

Sponsored Research Partner
Cite This Research Paper
ZHANG Wei, LI Ming, WANG Fang, CHEN Yu (2025). Optimization of Wind Power Generation Systems with Hybrid Energy Storage and Grid Integration. SinoTechIntel Verified Research. https://doi.org/10.16183/j.cnki.jsjtu.2025.150
SinoTechIntel Academic & Legal Disclaimer

Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.

Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.

Frequently Asked Questions

What is the main contribution of this paper?

The paper proposes an optimized control strategy for a wind power generation system with hybrid energy storage, improving power smoothing and grid stability.

How does the hybrid energy storage system work?

The system combines batteries and supercapacitors to balance energy density and power density, effectively smoothing power fluctuations and extending battery life.

What are the key findings of the study?

The proposed strategy significantly reduces power fluctuations, enhances grid stability, and achieves cost-effective operation through optimal storage sizing.

What methods were used in the research?

The study employs simulation-based analysis with a detailed model of the wind power system, hybrid storage, and grid interface, evaluating performance under various wind profiles.

What are the practical implications of this research?

The findings provide guidance for designing and operating wind power systems with hybrid storage, contributing to more reliable and efficient renewable energy integration.

Recommended Scientific Literature & Research Partners

Related Technical Papers & Translations

Research Paper
A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning

A Novel Approach for Enhanced Brain Tumor Segmentation Using Multimodal MRI and Deep Learning

Brain tumor segmentation from multimodal MRI is crucial for diagnosis and treatment planning. In this study, we propose a novel deep learning framework that integrates structural and functional imaging modalities to improve segmentation accuracy. Our method employs a multi-scale attention mechanism and a hybrid loss function to handle class imbalance and boundary ambiguity. Evaluated on the BraTS benchmark, our approach achieves state-of-the-art performance, with Dice scores of 0.91, 0.87, and 0.84 for whole tumor, core, and enhancing tumor, respectively. Furthermore, we demonstrate the generalizability of our model across different scanners and protocols. Our findings suggest that the proposed method can significantly aid clinical decision-making and surgical planning.

Read Abstract & PDF
Research Paper
Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal

Investigation of coupled acoustic and electrical responses and early warning approaches during re-loading of damaged coal

Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks, challenging conventional monitoring. This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multi-parameter, multi-level dynamic integrated early-warning model. Using a true-triaxial Split Hopkinson Pressure Bar (SHPB) system, we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts. Uniaxial compression reloading was conducted with synchronous acoustic emission (AE) and resistivity monitoring. Joint time-domain responses of force, acoustics, and electricity delineated distinct loading stages. Time-frequency features were extracted via Fourier and wavelet transforms; crack architecture was quantified by 3D AE localization and fractal-dimension analysis. Initial damage markedly reduced load-bearing capacity. Resistivity decreased sharply with increasing deviatoric stress, while cumulative AE counts increased strongly. The AE spectrum evolved from bimodal to broadband with low- and high-frequency enhancement. The resistivity spectrum showed progressive bandwidth broadening, energy amplification, and high-frequency advancement. The AE spatial fractal dimension rose significantly during compaction. An integrated warning system combining multiscale entropy fusion, Temporal Convolutional Network (TCN)-Transformer forecasting, recurrence-network analysis, and a Bayesian framework yielded a 28.4 s lead time, offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.

Read Abstract & PDF
Research Paper
Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage

Influence of aggregate particle size on fracture behavior and energy evolution of cemented rockfill in the post-peak stage

Cemented rockfill (CRF) combines structural support with sustainable reuse of coal-derived solid waste. This study integrates digital image correlation, acoustic emission monitoring, and finite–discrete element simulations to investigate mechanical behavior, fracture development, and energy evolution of CRF containing 54% aggregate content with three grain-size distributions (5–10, 10–20, and 20–30 mm). Results indicate finer aggregates raise compressive strength and elastic modulus, and increase post-peak softening and residual stiffness. Fracture patterns transition from dominantly unidirectional failure in coarse specimens to pronounced X-shaped conjugate shear in fine specimens, with cracks initiating at boundaries and propagating inward. The proportion of failed joints at comparable strains decreases markedly with finer gradation, reflecting a more homogeneous crack network that enhances post-peak load retention and produces frequent minor stress fluctuations. Energy analyses reveal a coarse > medium > fine ordering in cumulative dissipation; however, finer aggregates delay rapid kinetic and dissipative energy release, promoting slower energy redistribution and improved load resistance. These findings quantify how aggregate gradation controls deformational mechanisms, crack topology, and energy partitioning, and provide design guidance for optimizing aggregate size and cementitious composition to enhance ductility, energy absorption, and structural reliability of CRF in underground engineering.

Read Abstract & PDF