Renewable Energy Maximization for Pelagic Islands Network of Microgrids Through Battery Swapping Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Renewable Energy Maximization for Pelagic Islands Network of Microgrids Through Battery Swapping Using Deep Reinforcement Learning

Published:2023 Issue: Volume:11 Page:86196-86213
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Amin M. Asim¹^ORCID,Suleman Ahmad²,Waseem Muhammad³^ORCID,Iqbal Taosif⁴^ORCID,Aziz Saddam³^ORCID,Faiz Muhammad Talib³,Zulfiqar Lubaid²,Saleh Ahmed Mohammed⁵^ORCID

Affiliation:

1. Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Genoa, Italy

2. Rapid Volt (PVT) Ltd., Rajanpur, Punjab, Pakistan

3. Department of Electronics and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

4. Electrical Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

5. Electrical Engineering Department, University of Aden, Aden, Yemen

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10210341.pdf?arnumber=10210341

Reference50 articles.

1. Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

2. An Affine Arithmetic-Based Energy Management System for Isolated Microgrids

3. Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters

4. Including Smart Loads for Optimal Demand Response in Integrated Energy Management Systems for Isolated Microgrids

5. Two-Stage Cooperative Intelligent Home Energy Management System for Optimal Scheduling

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