Optimization of grid-connected voltage support technology and intelligent control strategies for new energy stations based on deep learning-Reference-Cited by-同舟云学术

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Optimization of grid-connected voltage support technology and intelligent control strategies for new energy stations based on deep learning

Published:2024-08-23 Issue:1 Volume:7 Page:
ISSN:2520-8942
Container-title:Energy Informatics
language:en
Short-container-title:Energy Inform

Author:

Lv Leiyan,Fang Xuan,Zhang Si,Ma Xiang,Liu Yong

Funder

Analysis of Operation Mechanism and Coordinated Optimization Configuration Technology for Hybrid New Energy Stations with Grid/Off-grid Connection

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s42162-024-00382-8.pdf

Reference28 articles.

1. Abualigah L, Zitar RA, Almotairi KH et al (2022) Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: a survey of advanced machine learning and deep learning techniques. Energies 15(2):578

2. Ahmad T, Madonski R, Zhang D et al (2022) Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renew Sustain Energy Rev 160(22):112128

3. Alam MM, Rahman MH, Ahmed MF et al (2022) Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system. Sci Rep 12(1):15133

4. Albogamy FR, Paracha MYI, Hafeez G et al (2022) Real-time scheduling for optimal energy optimization in smart grid integrated with renewable energy sources. IEEE Access 10(2):35498–35520

5. Al-Ja’Afreh MAA, Amjad B, Rowe K et al (2023) Optimal planning and forecasting of active distribution networks using a multi-stage deep learning based technique. Energy Rep 10(11):686–705

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