Title
Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition
Abstract
In responseto low-carbon requirements, a large amount of renewable energy resources (RESs) have been deployed in power systems; nevertheless, the intermittency of RESs raises the system vulnerability and even causes severe damage under extreme events. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services meanwhile enhancing system resilience. The distributed control of EVs under such scenarios in power-transportation network becomes a complex decision-making problem with enormous dynamics and uncertainties. To this end, a multiagent reinforcement learning method is proposed to compute both discrete and continuous actions simultaneously that aligns with the nature of EV routing and scheduling problems. Furthermore, the proposed method can enhance the learning stability and scalability with privacy perseverance in the multiagent setting. Simulation results based on IEEE 6- and 33-bus power networks integrated with transportation systems validate its effectiveness in providing system resilience and carbon intensity service.
Year
DOI
Venue
2022
10.1109/TII.2022.3166215
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Carbon intensity,electric vehicles (EVs),multiagent reinforcement learning (MARL),power-transportation network,resilience
Journal
18
Issue
ISSN
Citations 
11
1551-3203
0
PageRank 
References 
Authors
0.34
17
5
Name
Order
Citations
PageRank
Dawei Qiu1133.09
Yi Wang200.34
Tingqi Zhang300.34
Mingyang Sun4148.36
Goran Strbac57519.95