Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model-Reference-Cited by-同舟云学术

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Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model

Published:2021-05 Issue: Volume:290 Page:116717
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Welzel Fynn^ORCID,Klinck Carl-Friedrich^ORCID,Pohlmann Yannick^ORCID,Bednarczyk Mats^ORCID

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference46 articles.

1. Historic Paris agreement on climate change: 195 nations set path to keep temperature rise well below 2 degrees celsius;UNFCCC,2015

2. Future technologies: Support for electric cars – how sensible is it to subsidize individual technologies?;Buller,2009

3. Global EV outlook 2020;IEA,2020

4. Total number AF infrastructure (2020);European Alternative Fuels Observatory,2020

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2. Multilevel Deep Reinforcement Learning for Secure Reservation-Based Electric Vehicle Charging via Differential Privacy and Energy Storage System;IEEE Transactions on Vehicular Technology;2024-08

3. Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids;Computers and Electrical Engineering;2024-08

4. Research on the capacity of charging stations based on queuing theory and energy storage scheduling optimization sharing strategy;Journal of Energy Storage;2024-08

5. A Scalable Charging Algorithm for Heterogeneous EV Fleets Based on Clustering and Learning Methods;2024 American Control Conference (ACC);2024-07-10

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