Smart City Gnosys

Smart city article details

Title	Intelligent Distributed Charging Control For Large Scale Electric Vehicles: A Multi-Cluster Mean Field Game Approach
ID_Doc	32353
Authors	Dey S.; Xu H.
Year	2023
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3576914.3587709
Abstract	This paper presents an intelligent distributed optimal charging control for large-scale Electric Vehicles (LS-EVs) systems. The rising popularity of electric vehicles in smart cities is putting a strain on the power capacity, leading to high demand for energy and a surge in time-of-use (TOU) prices, which is becoming a pressing issue to address. When solving this issue for LS-EVs, the conventional cooperative centralized system encounters communication and computational complexity challenges. To address these issues, a distributed optimal charging control strategy has been developed. Specifically, the LS-EVs in a smart city has been divided into a finite number of clusters. Then, a mean field game (MFG) theory has been utilized for intra and inter clusters EVs to obtain the distributed optimal charging control. The MFG technique incorporates the state of charge (SOC) of all EVs from a particular cluster into a probability density function(PDF). Then, the PDFs of all clusters are employed to achieve the optimal charging decision for each EV in the city. In addition, a multi-actor-critic-mass (MACM) reinforcement learning algorithm is constructed to learn the optimal solution of the multi-cluster mean field game distributed charging control. Finally, to show the effectiveness of the presented schemes, numerical simulations have been performed. © 2023 ACM.
Author Keywords	LS-EVs; Mean Field Game; Reinforcement Learning.

Similar Articles

Id	Similarity	Authors	Title	Published
36544	0.949	Zhou Z.; Xu H.	Mean Field Game-Based Decentralized Optimal Charging Control For Large-Scale Of Electric Vehicles	IFAC-PapersOnLine, 55, 15 (2022)
38088	0.883	Bachiri K.; Yahyaouy A.; Gualous H.; Malek M.; Bennani Y.; Makany P.; Rogovschi N.	Multi-Agent Ddpg Based Electric Vehicles Charging Station Recommendation	Energies, 16, 16 (2023)
40790	0.883	Suanpang P.; Jamjuntr P.	Optimizing Electric Vehicle Charging Recommendation In Smart Cities: A Multi-Agent Reinforcement Learning Approach	World Electric Vehicle Journal, 15, 2 (2024)
1879	0.879	Modarresi J.; Ahmadian A.; Diabat A.; Elkamel A.	A Game Theory Based Scheduling Approach For Charging Coordination Of Multiple Electric Vehicles Aggregators In Smart Cities	Energy, 313 (2024)
7054	0.875	Chavhan S.; Gupta D.; Alkhayyat A.; Alharbi M.; Rodrigues J.J.P.C.	Ai-Empowered Game Theoretic-Enabled Dynamic Electric Vehicles Charging Price Scheme In Smart City	IEEE Systems Journal, 17, 4 (2023)
38094	0.874	Pok P.J.; Yeo H.; Woo S.	Multi-Agent Game-Theoretic Modelling Of Electric Vehicle Charging Behavior And Pricing Optimization In Dynamic Ecosystems	Procedia Computer Science, 257 (2025)
7022	0.874	Khan A.A.; Mahendran R.K.; Ullah F.; Ali F.; Bashir A.K.; Dabel M.M.A.; Omar M.	Ai-Driven Dynamic Allocation And Management Optimization For Ev Charging Stations	IEEE Transactions on Intelligent Transportation Systems (2025)
17670	0.871	Ghavami M.; Haeri M.; Kebriaei H.	Decentralized Pricing Mechanism For Traffic And Charging Station Management Of Evs In Smart Cities	IEEE Transactions on Intelligent Transportation Systems, 25, 6 (2024)
17635	0.87	Martinez-Piazuelo J.; Quijano N.; Ocampo-Martinez C.	Decentralized Charging Coordination Of Electric Vehicles Under Feeder Capacity Constraints	IEEE Transactions on Control of Network Systems, 9, 4 (2022)
8542	0.864	Suanpang P.; Jamjuntr P.; Kaewyong P.; Niamsorn C.; Jermsittiparsert K.	An Intelligent Recommendation For Intelligently Accessible Charging Stations: Electronic Vehicle Charging To Support A Sustainable Smart Tourism City	Sustainability (Switzerland), 15, 1 (2023)