Market-Incentivized Aggregation Modeling and Capacity from Distributed Electric Vehicles with AI-Enabled Evolutionary Peer-to-Peer Game

Chaoyu Dong; Yanran Li; Yan Zheng; Hongjie Jia

doi:10.1016/j.eng.2026.06.019

Engineering ›› :202606019 DOI: 10.1016/j.eng.2026.06.019

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Market-Incentivized Aggregation Modeling and Capacity from Distributed Electric Vehicles with AI-Enabled Evolutionary Peer-to-Peer Game

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Abstract

The aggregation of distributed electric vehicles (EVs) initiates a promising avenue for constructing a virtual energy storage system within the distribution electricity market. To unlock the latent storage capacity inside the large-scale electrified transportation system and reduce redundant investment in the traditional power plant, a market-incentivized aggregation framework is developed to integrate those inherently stochastic energy resources. To address the substantial computational complexity posed by large EV populations and align with their distributed operational nature, an

Keywords

Distribution electricity market / Deep reinforcement learning / AI-enabled evolutionary peer-to-peer game / Electric vehicle / Probabilistic policy

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Chaoyu Dong, Yanran Li, Yan Zheng, Hongjie Jia. Market-Incentivized Aggregation Modeling and Capacity from Distributed Electric Vehicles with AI-Enabled Evolutionary Peer-to-Peer Game. Engineering 202606019 DOI:10.1016/j.eng.2026.06.019

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References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Englberger S, Gamra KA, Tepe B, Schreiber M, Jossen A, Hesse H . Electric vehicle multi—use: optimizing multiple value streams using mobile storage systems in a vehicle—to—grid context. Appl Energy 2021; 304:117862.

[2]	Dong C, Sun J, Li Y, Zheng Y, Hao J, Liu Y, et al. Hybrid process model and smart policy network of electric—vehicle resources for instantaneous power flow imbalances. Appl Energy 2022; 314:118531.

[3]	Dong C, Sun J, Wu F, Jia H . Probability—based energy reinforced management of electric vehicle aggregation in the electrical grid frequency regulation. IEEE Access 2020; 8:110598-610.

[4]	Xiaomi to invest $10bn in electric vehicle race. Report. London:BBC; 2021.

[5]	Why the automotive future is electric. Report. Washington, DC: McKinsey & Company; 2021.

[6]	Pradhan R, Keshmiri N, Emadi A . On—board chargers for high—voltage electric vehicle powertrains: future trends and challenges. IEEE Open J Power Electron 2023; 4:189-207.

[7]	Dong C, Gao Q, Xiao Q, Chu R, Jia H . Spectrum—domain stability assessment and intrinsic oscillation for aggregated mobile energy storage in grid frequency regulation. Appl Energy 2020; 276:115434.

[8]	Electric vehicle charging systems for Singapore. Report. Singapore: Land Transport Authority Hampshire Office; 2020.

[9]	Terra HP charger—up to 350 kW. Report. Zurich: ABB Asea Brown Boveri Ltd;2021.

[10]	About Parker. Report. Cleveland: Parker; 2016.

[11]	Al—obaidi AA, Farag HE . Optimal design of V2G incentives and V2G—capable electric vehicles parking lots considering cost—benefit financial analysis and user participation. IEEE Trans Sustain Energy 2024; 15(1):454-65.

[12]	Tomić J, Kempton W . Using fleets of electric—drive vehicles for grid support. J Power Sources 2007; 168(2):459-68.

[13]	Wolinetz M, Axsen J, Peters J, Crawford C . Simulating the value of electric—vehicle—grid integration using a behaviorally realistic model. Nat Energy 2018; 3(2):132-9.

[14]	Dong C, Li X, Jiang W, Mu Y, Zhao J, Jia H . Cyber—physical modelling operator and multimodal vibration in the integrated local vehicle—grid electrical system. Appl Energy 2021; 286:116432.

[15]

Schlund J, Pruckner M, German R . Flexability—modeling and maximizing the bidirectional flexibility availability of unidirectional charging of large pools of electric vehicles. In: Proceedings of the Eleventh ACM International Conference on Future Energy Systems; 2020 Jun 22—26; Melbourne, VIC, Australia. ACM; 2020. p. 121-32.

[16]	Han R, Fu W, Qin J . Risk—aware multi—stage stochastic optimization for battery swapping station scheduling with quality of service assurance. IEEE Trans Intell Transp Syst 2025; 26(10):16676-87.

[17]	Vagropoulos SI, Kyriazidis DK, Bakirtzis AG . Real—time charging management framework for electric vehicle aggregators in a market environment. IEEE Trans Smart Grid 2016; 7(2):948-57.

[18]	Kandpal B, Pareek P, Verma A . A robust day—ahead scheduling strategy for EV charging stations in unbalanced distribution grid. Energy 2022; 249:123737.

[19]	Tuchnitz F, Ebell N, Schlund J, Pruckner M . Development and evaluation of a smart charging strategy for an electric vehicle fleet based on reinforcement learning. Appl Energy 2021; 285:116382.

[20]	Papadaskalopoulos D, Strbac G, Mancarella P, Aunedi M, Stanojevic V . Decentralized participation of flexible demand in electricity markets—part ii: application with electric vehicles and heat pump systems. IEEE Trans Power Syst 2013; 28(4):3667—74.

[21]	Asrari A, Ansari M, Khazaei J, Fajri P . A market framework for decentralized congestion management in smart distribution grids considering collaboration among electric vehicle aggregators. IEEE Trans Smart Grid 2020; 11(2):1147-58.

[22]	Zou Y, Xu Y, Feng X, Naayagi R, Soong BH . Transactive energy systems in active distribution networks: a comprehensive review. CSEE J Power Energy Syst 2022; 8(5):1302—17.

[23]	Li K, Shao C, Huang X, Shahidehpour M, Wang X . Day—ahead charging capacity market for power distribution network with electric vehicle charging stations. IEEE Trans Power Syst 2024; 40(3):2258-71.

[24]	Mohammed MA, Nassar ME, Shaaban MF, Ali A . Complementary EV scheduling in peer—to—peer energy markets. IEEE Access 2025; 13:209237—46.

[25]	Zhou Z, Li X, Ge H, Zhang J, Xue Y . Congestion—aware rebalancing and vehicle—to—grid coordination of shared electric vehicles: an aggregative game approach. IEEE Trans Transp Electrif 2024; 11(1):275-85.

[26]	Zheng Y, Yan Z, Chen K, Sun J, Xu Y, Liu Y . Vulnerability assessment of deep reinforcement learning models for power system topology optimization. IEEE Trans Smart Grid 2021; 12(4):3613—23.

[27]	Kullback S, Leibler RA . On information and sufficiency. Ann Math Stat 1951; 22(1):79-86.

[28]

Zheng Y, Meng Z, Hao J, Zhang Z . Weighted double deep multiagent reinforcement learning in stochastic cooperative environments. in: Proceedings of 15th Pacific Rim International Conference on Artificial Intelligence; 2018 Aug 28—31; Nanjing, China. Cham: Springer International Publishing; 2018. p. 421—9.

[29]	Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O . Proximal policy optimization algorithms. 2017. arXiv:1707.06347.

[30]	Yang Y, Hao J, Zheng Y, Yu C . Large—scale home energy management using entropy—based collective multiagent deep reinforcement learning framework. In: Proceedings of the Twenty—Eighth International Joint Conference on Artificial Intelligence; 2019 Aug 10—16; Macao, China. IJCAI; 2019. p. 630-6.

[31]	Wu D, Rabusseau G, François—lavet V, Precup D, Boulet B . Optimizing home energy management and electric vehicle charging with reinforcement learning. In: Proceedings of the 16th Adaptive Learning Agents; 2018 Jul; Stockholm, Sweden.

[32]	Muratori M. Impact of uncoordinated plug—in electric vehicle charging on residential power demand. Nat Energy 2018;3(3):193-201.

[33]	Lin Z, Dong J, Liu C, Greene D . Estimation of energy use by plug—in hybrid electric vehicles: validating gamma distribution for representing random daily driving distance. Transp Res Rec 2012; 2287(1):37-43.

[34]	UQ solar: the challenge of sustainable, clean energy. Report. Queensland: The University of Queensland; 2021.

[35]	Berlink H, Costa AH . Batch reinforcement learning for smart home energy management. In: Proceedings of the Twenty—Fourth International Joint Conference on Artificial Intelligence;2015 Jul; Buenos Aires, Argentina.