10.57647/ijeee.2026.1701.01

A Novel Market-Based Strategic CoordinationFramework for Aggregators and V2G Owners UsingBi-Level MPEC

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  • Saeed Zafaranchi Zadeh Moghadam*1,
  • Mohammad Agha Shafiyi1,
  • Gholam Reza Latif Shabgahi1,
  • Seyed Saied Taheri2,
  1. Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
  2. Transmission Line and Substation Equipment Research Department, Niroo Research Institute (NRI), Tehran, Iran

Received: 2026-01-07

Revised: 2026-03-13

Accepted: 2026-03-23

Published in Issue 2026-03-31

Copyright (c) 2026 Saeed Zafaranchi Zadeh Moghadam, Mohammad Agha Shafiyi, Gholam Reza Latif Shabgahi, Seyed Saied Taheri (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Zafaranchi Zadeh Moghadam, S., Agha Shafiyi, M., Latif Shabgahi, G. R., & Taheri, S. S. (2026). A Novel Market-Based Strategic CoordinationFramework for Aggregators and V2G Owners UsingBi-Level MPEC. International Journal of Energy and Environmental Engineering, 17(01). https://doi.org/10.57647/ijeee.2026.1701.01
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Abstract

The accelerating electrification of societies is imposing stress on power grids, particularly during peak demand. While renewable integration offers a promising pathway to address challenges, its intermittency necessitates advanced storage strategies. Vehicle-to-Grid (V2G) technology, leveraging electric vehicles (EVs) as distributed storage, provides a scalable cost-effective solution to balance supply and demand. This paper introduces a novel Stackelberg bi-level optimization framework establishing a local market between the aggregator and EV owners, enabling fair dynamic price-setting for energy exchange. Recognizing emerging trends where aggregators increasingly own distributed energy resources (DERs), the model accounts for their influence on market prices strategically. The aggregator’s commitments to the grid are embedded as prioritized constraints, ensuring reliability and hierarchical coordination with operators. Crucially, battery degradation costs for both EVs and stationary battery energy storage systems (BESS) are modeled, balancing short-term revenues and long-term health. The model incorporates key battery and charger technical limitations—state of charge (SoC), charging rate (C-rate), and depth of discharge (DOD)—along with a minimum SoC requirement to meet the EV owner’s personal mobility demand, all within a continuous framework avoiding explicit binary variables. Implemented in GAMS and validated under diverse scenarios, the model demonstrates potential for scalable V2G integration and market-driven grid flexibility.

Keywords

  • Vehicle-to-Grid (V2G),
  • Electric vehicle (EV),
  • Renewable energy sources,
  • Bi-level optimization Framework,
  • Electricity Storage,
  • Mathematical programming with equilibrium constraints (MPEC)
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References

  1. Helmi H, Abedinzadeh T, Beiza J, Shahmohammadi S, Daghigh A. Peer-to-peer electricity trading via a bi-level optimization approach for renewable energy-driven microgrids connected to the distribution grid. IET Gener Transm Distrib. 2024; 18(16): 2705-2718. doi: https://doi.org/10.1049/gtd2.13235
  2. Sadeghian O, Nazari-Heris M, Abapour M, Taheri SS, Zare K. Improving reliability of distribution networks using plug-in electric vehicles and demand response. J Mod Power Syst Clean Energy. 2019; 7(5): 1189-1199. doi: https://doi.org/10.1007/s40565-019-0523-8
  3. Dehghani F, Shafiyi MA. Integration of hybrid renewable energy sources with the power system considering their economic complementarity. IET Renew Power Gener. 2023; 17(15): 3638-3650. doi: https://doi.org/10.1049/rpg2.12871
  4. Vaziri Rad MA, Kasaeian A, Niu X, Zhang K, Mahian O. Excess electricity problem in off-grid hybrid renewable energy systems: A comprehensive review from challenges to prevalent solutions. Renew Energy. 2023; 212: 538-560. doi: https://doi.org/10.1016/j.renene.2023.05.073
  5. Yingliang L, Zhiwei D. Coordinated optimization of source-grid-load-storage for wind power grid-connected and mobile energy storage characteristics of electric vehicles. IET Gener Transm Distrib. 2024; 18(8): 1528-1547. doi: https://doi.org/10.1049/gtd2.13105
  6. Li S, Zhao P, Gu C, Huo D, Li J, Cheng S. Linearizing battery degradation for health-aware vehicle energy management. IEEE Trans Power Syst. 2023; 38(5): 4890-4899. doi: https://doi.org/10.1109/TPWRS.2022.3217981
  7. Amamra S-A, Marco J. Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost. IEEE Access. 2019; 7: 178528-178538. doi: https://doi.org/10.1109/ACCESS.2019.2958664
  8. Shafie-khah M, Heydarian-Forushani E, Osório GJ, Gil FAS, Aghaei J, Barani M, et al. Optimal behavior of electric vehicle parking lots as demand response aggregation agents. IEEE Trans Smart Grid. 2016; 7(6): 2654-2665. doi: https://doi.org/10.1109/TSG.2015.2496796
  9. Wu W, Zhu J, Liu Y, Luo T, Chen Z, Dong H. A coordinated model for multiple electric vehicle aggregators to grid considering imbalanced liability trading. IEEE Trans Smart Grid. 2024; 15(2): 1876-1890. doi: https://doi.org/10.1109/TSG.2023.3294608
  10. Zheng Y, Song Y, Hill DJ, Meng K. Online distributed MPC-based optimal scheduling for EV charging stations in distribution systems. IEEE Trans Ind Inform. 2019; 15(2): 638-649. doi: https://doi.org/10.1109/TII.2018.2812755
  11. Maigha, Crow ML. Electric vehicle scheduling considering co-optimized customer and system objectives. IEEE Trans Sustain Energy. 2018; 9:410-419. doi: https://doi.org/10.1109/TSTE.2017.2737146
  12. Hatefi Einaddin A, Sadeghi Yazdankhah A. A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic conditions. Int J Electr Power Energy Syst. 2020; 117: 1-18. doi: https://doi.org/10.1016/j.ijepes.2019.105617
  13. Tianyu L, Terence Shengyu T, Kun H, Mengke L, Binglei X, Biao Y, et al. V2G multi-objective dispatching optimization strategy based on user behavior model. Front Energy Res. 2021; 9:1-14. doi: https://doi.org/10.3389/fenrg.2021.739527
  14. Shaofeng L, Bing H, Fei X, Lin J, Kejun Q. Multi-objective optimization of EV charging and discharging for different stakeholders. CSEE J Power Energy Syst. 2023; 9(6): 2301-2308. doi: https://doi.org/10.17775/CSEEJPES.2020.02300
  15. Bo Z, Houqi D, Fuqiang X, Ming Z. Bilevel programming approach for optimal planning design of EV charging station. IEEE Trans Ind Appl. 2020; 56(3): 2314-2323. doi: https://doi.org/10.1109/TIA.2020.2973189
  16. Yilu W, Zixuan J, Jianing L, Xiaoping Z, Ray Z. Optimal bi-level scheduling method of vehicle-to-grid and ancillary services of aggregators with conditional value-at-risk. Energies. 2021; 14(21): 1-16. doi: https://doi.org/10.3390/en14217015
  17. Qiwei Y, Yantai H, Qiangqiang Z, Jinjiang Z. A bi-level optimization and scheduling strategy for charging stations considering battery degradation. Energies. 2023; 16(13): 1-15. doi: https://doi.org/10.3390/en16135070
  18. Sumit KR, Saxena D. Decentralized energy management system for LV microgrid using stochastic dynamic programming with game theory approach under stochastic environment. IEEE Trans Ind Appl. 2021; 57(4): 3990-4000. doi: https://doi.org/10.1109/TIA.2021.3069840
  19. Ruifeng S, Yang Y, Lili S, Kwang YL. Bi-level day ahead optimization of V2G dispatch strategy based on the dynamic discharging electricity price. IFAC PapersOnLine. 2018; 51(28): 462-467. doi: https://doi.org/10.1016/j.ifacol.2018.11.746
  20. Dawei Q, Yujian Y, Dimitrios P, Goran S. A deep reinforcement learning method for pricing electric vehicles with discrete charging levels. IEEE Trans Ind Appl. 2020; 56(5): 5901-5912. doi: https://doi.org/10.1109/TIA.2020.2984614
  21. Jiyong L, Chengye L, Yasai W, Ran C, Xiaoshuai X. Bi-level programming model approach for electric vehicle charging stations considering user charging costs. Electr Power Syst Res. 2023; 214: 1-8. doi: https://doi.org/10.1016/j.epsr.2022.108889
  22. Jingxiang W, Zhaojian W, Bo Y, Feng L, Wei W, Xinping G. V2G for frequency regulation service: A Stackelberg game approach considering endogenous uncertainties. IEEE Trans Transp Electrification. 2025; 11(1): 463-475. doi: https://doi.org/10.1109/TTE.2024.3392496
  23. Ginigeme K, Wang Z. Distributed optimal vehicle-to-grid approaches with consideration of battery degradation cost under real-time pricing. IEEE Access. 2020; 8: 5225-5235. doi: https://doi.org/10.1109/ACCESS.2019.2963692
  24. Taheri SS, Seyed-Shenava S-J, Mohadesi V, Esmaeilzadeh R. Improving operation indices of a micro-grid by battery energy storage using multi objective cuckoo search algorithm. Int J Electr Eng Inform. 2021; 13(1): 132-151. doi: https://doi.org/10.15676/ijeei.2021.13.1.7
  25. Taheri SS, Kazempour J, Seyedshenava S. Transmission expansion in an oligopoly considering generation investment. Energy Econ. 2017; 64: 55-62. doi: https://doi.org/10.1016/j.eneco.2017.03.003
  26. Kempton W, Tomic J. Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. J Power Sources. 2005; 144(1): 268-279. doi: https://doi.org/10.1016/j.jpowsour.2004.12.025
  27. GAMS Development Corporation. EMP bilevel programs [Internet]. Available from: https://www.gams.com/latest/docs/S_JAMS.html#EMP_BILEVEL_PROGRAMS