Scheduled Operation of Wind Farm with Battery System Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Scheduled Operation of Wind Farm with Battery System Using Deep Reinforcement Learning

Published:2021-04-02 Issue:5 Volume:16 Page:687-695
ISSN:1931-4973
Container-title:IEEJ Transactions on Electrical and Electronic Engineering
language:en
Short-container-title:IEEJ Trans Elec Electron Eng

Author:

Futakuchi Mamoru¹,Takayama Satoshi¹,Ishigame Atsushi¹

Affiliation:

1. Power System Research Group Osaka Prefecture University 1‐1 Gakuen‐cho, Nakaku, Sakai Osaka 599‐8531 Japan

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23348

Reference30 articles.

1. Impacts of Large-Scale Wind Penetration on Designing and Operation of Electric Power Systems

2. Global Wind Energy Council Global wind report 2016 – Annual market update.Accessed at May 2 2017fromhttp://files.gwec.net/files/GWR2016.pdf

3. FerreiraC GamaJ MatiasL BotterudA WangJ.A survey on wind power RAMP forecasting Argonne National lab. (ANL) DuPage Country IL USA Tech. Rep. ANL/DIS‐10‐13 December 2010.

4. An Optimized Swinging Door Algorithm for Identifying Wind Ramping Events

5. Power Fluctuations From Large Wind Farms

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