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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Vydané v:IEEE transactions on smart grid Ročník 15; číslo 1; s. 1
Hlavní autori: Shi, Xiaoying, Xu, Yinliang, Chen, Guibin, Guo, Ye
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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, and its flexibility in both emission mitigation and energy/power dispatching is demonstrated. Second, a bilevel optimization model is formulated for EV aggregators to participate in day-ahead optimal scheduling, which aims to minimize the total cost without exceeding the given carbon cap. Third, to tackle the nonlinear coupling between the carbon flow and power flow, a bilevel model with a carbon cap constraint is formed as a constrained Markov decision process (CMDP). Finally, the CMDP is efficiently solved by the proposed augmented Lagrangian-based DRL algorithm featuring the soft actor-critic (SAC) method. Comprehensive numerical studies with IEEE distribution test feeders demonstrate that the proposed approach can achieve a fine tradeoff between cost and emission mitigation with a higher computation efficiency compared with the existing DRL methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2023.3289211