Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation

Cunzhi Zhao; Xingpeng Li

doi:10.1109/pesgm51994.2024.10689047

ScienceGate Book Chapters

JOURNAL ARTICLE

Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation

Cunzhi Zhao Xingpeng Li

Year: 2024 Pages: 1-1

DOI: 10.1109/pesgm51994.2024.10689047

Get Full-Text PDF Get Analytical Report

Keywords:

Microgrid Computer science Degradation (telecommunications) Scheduling (production processes) Artificial neural network Battery (electricity) Distributed computing Computer network Power (physics) Engineering Telecommunications Artificial intelligence

Metrics

Cited By

0.40

FWCI (Field Weighted Citation Impact)

Refs

0.52

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Battery Technologies Research

Physical Sciences → Engineering → Automotive Engineering

Microgrid Control and Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Electric Vehicles and Infrastructure

Physical Sciences → Engineering → Electrical and Electronic Engineering

Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation

Metrics

Citation History

Topics

Related Documents

Microgrid Optimal Energy Scheduling Considering Neural Network Based Battery Degradation

Optimal sizing and energy scheduling of isolated microgrid considering the battery lifetime degradation

Microgrid cooperative distributed energy scheduling (CoDES) considering battery degradation cost

Optimal Scheduling of Hybrid Fuel Cell-Battery Storage in Microgrid Considering Battery Degradation Cost

Artificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Scheduling