Lazy reinforcement learning for real-time generation control of parallel cyber–physical–social energy systems-Reference-Cited by-同舟云学术

Lazy reinforcement learning for real-time generation control of parallel cyber–physical–social energy systems

Published:2020-02 Issue: Volume:88 Page:103380
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yin Linfei,Li Shengyuan,Liu Hui

Funder

National Natural Science Foundation of China

Natural Science Foundation for Distinguished Young Scholars of Guangxi

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference52 articles.

1. A cyber-physical energy management system and optimal sizing of networked nanogrids with battery swapping stations;Ban;IEEE Trans. Sustain. Energy,2018

2. Optimal management of the automatic generation control service in smart user grids including electric vehicles and distributed resources;Battistelli;Electr. Power Syst. Res.,2014

3. Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: A case study of illinois;Botterud;IEEE Trans. Sustain. Energy,2013

4. Time-scale modeling of wind-integrated power systems;Chandra;IEEE Trans. Power Syst.,2016

5. Guest editorialspecial issue on situation, activity, and goal awareness in cyber-physical human–machine systems;Chen;IEEE Trans. Hum.-Mach. Syst.,2017

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