Hybrid Deep Learning-Based Grid-Supportive Renewable Energy Systems for Maximizing Power Generation Using Optimum Sizing-Reference-Cited by-同舟云学术

Hybrid Deep Learning-Based Grid-Supportive Renewable Energy Systems for Maximizing Power Generation Using Optimum Sizing

Published:2023-05-08 Issue:15 Volume:51 Page:1597-1611
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Kirubakaran Sakthivel¹,Nagarajan Vijayasharathi²,Dhayapulley Sai Chaitanya Kishore³,Soubache Irissappsne Dhanusu⁴,Pasupuleti Subrahmanya Ranjit⁵,Kumar Avinash⁶,Rastogi Ravi⁷,Vasudevan Saravanan⁸

Affiliation:

1. Department of CSE, CMR College of Engineering and Technology, Kandlakoya, Hyderabad, Telangana, India

2. Department of Humanities and Science, Panimalar Engineering College, Chennai City Campus, Chennai, Tamil Nadu, India

3. Department of Mechanical Engineering, Srinivasa Ramanujan Institute of Technology, Rotarypuram Village, BK Samudram Mandal, Anantapur, Andhra Pradesh, India

4. Department of EEE, Rajiv Gandhi College of Engineering and Technology—Puducherry Cuddalore Main Road, Puducherry, India

5. Department of Mechanical Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, India

6. Department of EEE, GGSESTC Bokaro, Bokaro, Jharkhand, India

7. Department of EEE, NIELIT Gorakhpur, Gorakhpur, Uttar Pradesh, India

8. Department of Aeronautical Engineering, Nehru Institute of Technology, Coimbatore, Tamil Nadu, India

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Energy Engineering and Power Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2023.2201249

Reference32 articles.

1. A Predictive Control Strategy for Energy Management in Micro-Grid Systems

2. Fully Decentralized, Cost-Effective Energy Demand Response Management System with a Smart Contracts-Based Optimal Power Flow Solution for Smart Grids

3. Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

4. A Study of Fuel Cell Scheduling Effect on Local Energy Markets with Heterogeneous Renewable Sources

5. Data-Driven Risk-Adjusted Robust Energy Management for Microgrids Integrating Demand Response Aggregator and Renewable Energies

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