An Augmented Lagrangian-based Safe Reinforcement Learning Algorithm for Carbon-Oriented Optimal Scheduling of EV Aggregators

This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented optimal scheduling of electric vehicle (EV) aggregators in a distribution network. First, practical charging data are employed to formulate an EV aggregation model, a...

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Veröffentlicht in:	IEEE transactions on smart grid Jg. 15; H. 1; S. 1
Hauptverfasser:	Shi, Xiaoying, Xu, Yinliang, Chen, Guibin, Guo, Ye
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	aggregator Algorithms augmented Lagrangian function Carbon Carbon dioxide carbon emission mitigation carbon flow Constraint modelling Costs Deep learning deep reinforcement learning Distribution networks Electric vehicles Emission Energy states Machine learning Markov processes Optimal scheduling Optimization models Power flow Scheduling
ISSN:	1949-3053, 1949-3061
Online-Zugang:	Volltext
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