Knowledge-Based Decentralized Arbitrage Coordination between Heavy-Duty Electric Truck Aggregators and Eco-Industrial Parks

Articles in Press

Document Type : Research paper

Authors

Department of Electrical Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran.

Abstract

Existing studies on freight electrification largely rely on centralized architectures or simplified uncertainty models, leaving a gap in decentralized energy–mobility coordination under volatile electricity prices and privacy constraints. This study develops a decentralized optimization framework for coordinated operation of a heavy-duty electric truck aggregator and an eco-industrial park under concurrent market and fleet uncertainty. A hybrid distributionally robust–stochastic programming structure is adopted, where locational marginal price uncertainty is modeled through an uncertainty-budget robust layer, while fleet arrival, departure, and state-of-charge variability are represented via scenarios. The problem is decomposed using an alternating direction method of multipliers-based distributed algorithm to preserve autonomy and limit information exchange to tie-line power flows. Numerical results confirm stable convergence within approximately 130 iterations. When the price uncertainty budget spans the full 24-hour horizon, aggregator net profit decreases by 41% and eco-industrial park operating cost increases by 9% relative to deterministic pricing, without feasibility loss. The results demonstrate that integrating robust price hedging with stochastic fleet modeling enables economically consistent decentralized coordination under adverse market conditions.    

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References

  1. H. Shayeghi, P. Zare, I. F. Davoudkhani, and N. Bizon, “A comparative study of holistic expansion planning using osprey optimization algorithm for modern active electric distribution networks,” in Proc. Int. Conf. Electron. Comput. Artif. Intell., pp. 1–7, 2024.
  2. A. Karam and J. Rich, “Planning the transition to electric trucks: An optimization framework for line-haul logistics,” Res. Transp. Bus. Manag., vol. 61, p. 101413, 2025.
  3. A. Alonso-Villar, B. Davíðsdóttir, H. Stefánsson, E. I. Ásgeirsson, and R. Kristjánsson, “Optimising fast-charging infrastructure for long-haul electric trucks in remote regions under adverse climate conditions,” ETransportation, vol. 24, p. 100414, 2025.
  4. D. Speth, P. Plötz, and M. Wietschel, “An optimal capacity-constrained fast charging network for battery electric trucks in Germany,” Transp. Res. Part A Policy Pract., vol. 193, p. 104383, 2025.
  5. T. Dimatulac, H. Maoh, and R. Carriveau, “Electricity demand assessment and charging infrastructure planning for long-haul electric vehicle operations in Ontario, Canada,” Transp. Policy, vol. 178, p. 103970, 2026.
  6. S. Johansson, D. Jung, and C. Sundström, “Cost-effective routing of a single heavy-duty battery electric truck,” IFAC-PapersOnLine, vol. 59, pp. 31–36, 2025.
  7. E. Mahyari and N. Freeman, “Electric vehicle fleet charging management: An approximate dynamic programming policy,” Eur. J. Oper. Res., vol. 327, pp. 263–279, 2025.
  8. O. Anderson, N. Yu, W. Hong, and B. Wang, “Impact of flexible and bidirectional charging in medium- and heavy-duty trucks on California’s decarbonization pathway,” Appl. Energy, vol. 377, p. 124450, 2025.
  9. A. Golab, C. Loschan, S. Zwickl-Bernhard, and H. Auer, “The value of flexibility of commercial electric vehicle fleets in the redispatch of congested transmission grids,” Energy, vol. 316, p. 134385, 2025.
  10. A. Syla, D. Parra, and M. K. Patel, “Assessing flexibility from electric vehicles using an open-source energy system model: Trade-offs between smart charging, vehicle-to-grid and an extensive charging infrastructure,” Energy, vol. 326, p. 136236, 2025.
  11. Q. Wang, C. Huang, C. Wang, K. Li, and M. Shafie-khah, “Risk-averse frequency regulation strategy of electric vehicle aggregator considering multiple uncertainties,” Appl. Energy, vol. 382, p. 125259, 2025.
  12. A. Pradana, M. M. Haque, and M. Nadarajah, “A comprehensive framework for harnessing energy arbitrage benefits through an electric vehicle aggregator,” J. Energy Storage, vol. 139, p. 118617, 2025.
  13. T. Robbiano, M. Fresia, M. Caliano, and S. Bracco, “Optimal energy management strategies for aggregators in renewable energy communities,” Int. J. Electr. Power Energy Syst., vol. 172, p. 111085, 2025.
  14. X. Audet, K. Qako, and A. Lesage-Landry, “A distributionally robust optimization strategy for virtual bidding in two-settlement electricity markets,” Sustain. Energy Grids Netw., vol. 43, p. 101904, 2025.
  15. Z. Siqin, R. Diao, and M. Zhao, “Two-stage distributionally robust bidding strategies for multi-energy virtual power plants in energy, reserve and carbon markets,” Energy, vol. 334, p. 137455, 2025.
  16. W. Li, F. Dong, Z. Ji, and P. Wang, “Internal and external coordinated distributionally robust bidding strategy of virtual power plant operator participating in day-ahead electricity spot and peaking ancillary services markets,” Appl. Energy, vol. 386, p. 125514, 2025.
  17. W. Shen, H. Ding, M. Zeng, and X. Zhang, “Optimal bidding strategy for integrated energy system participating in spot power market: A Wasserstein metric-based DRO method,” Energy, vol. 327, p. 136402, 2025.
  18. X. Lei, H. Yu, J. Zhong, Z. Shao, and L. Jian, “A data-driven robust optimal day-ahead bidding strategy considering V2G operation for distribution-system-side virtual power plant,” Energy, vol. 338, p. 138795, 2025.
  19. S. Fatemi, A. Ketabi, and S. A. Mansouri, “A Bi-level distributed optimization framework to unlock flexibility in grid-connected energy storage systems and electric vehicle fleets,” J. Energy Storage, vol. 140, p. 119107, 2025.
  20. W. N. Tayari, Y. Merrad, S. B. Elghali, and M. Benbouzid, “Privacy-preserving ADMM-Based multiagent energy sharing framework between communities using ring signature multi-party computation,” Energy Rep., vol. 14, pp. 3783–3794, 2025.
  21. A. Quteishat, M. A. Younis, S. R. Seyednouri, and A. Safari, “Privacy-preserving and stochastic energy management of multi-microgrid systems with bidirectional electric vehicle integration,” Renew. Energy Focus, vol. 56, p. 100784, 2026.
  22. C. Wang, X. Li, R. I. Usmonovich, A. K. Rakhimovich, and X. Li, “Emission-aware energy management for smart buildings and energy hubs: A pathway to climate-neutral cities,” Sustain. Cities Soc., vol. 136, p. 107059, 2026.
  23. Y. Wang, Z. Fan, Y. Z. Alharthi, S. Huang, and S. A. Mansouri, “Towards carbon-neutral energy systems: A two-layer robust model for day-ahead scheduling of emission-aware microgrids,” Sustain. Cities Soc., vol. 131, p. 106764, 2025.
  24. S. S. Fazlhashemi, M. E. Khodayar, M. Sedighizadeh, and M. Khodayar, “Risk-averse operation of microgrids in distribution systems with price-responsive demands,” Int. J. Electr. Power Energy Syst., vol. 168, p. 110581, 2025.
  25. J. Zhang, Z. Liang, L. Guan, W. Lu, C. Jiang, and X. Zeng, “Distributed multiple time scales energy management strategy for microgrid cluster with electric vehicles participation,” Electr. Power Syst. Res., vol. 248, p. 111923, 2025.
  26. A. Kumar, A. Maulik, and K. A. Chinmaya, “A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response,” Electr. Power Syst. Res., vol. 238, p. 111093, 2025.
  27. B. Ahmadi, A. Pappu, G. Hoogsteen, M. E. T. Gerards, and J. L. Hurink, “A novel user-centric decentralized multi-objective energy management system for energy communities,” Sustain. Energy Grids Netw., vol. 44, p. 101936, 2025.
  28. S. S. Martín, F. García-Muñoz, F. Quezada, and S. Dávila, “User-centered decentralized P2P energy trading model for managing line congestion in energy communities,” Sustain. Energy Grids Netw., vol. 44, p. 101931, 2025.
  29. A. Golshanfard, Y. Noorollahi, H. Hashemi-Dezaki, and H. Lund, “Mitigating emissions: Energy balancing in eco-industrial zones considering renewable energy and electric vehicle uncertainties,” Renew. Energy Focus, vol. 56, p. 100768, 2026.
  30. Y. Li, Z. Lu, X. Kong, Y. Cai, E. Du, and J. Zhang, “Robust planning of low-carbon energy transition in eco-power coupling system considering meteorological uncertainty,” Energy Convers. Manag., vol. 345, p. 120339, 2025.
  31. C. Fan, J. Wu, C. Zhang, Z. Zhu, J. Ma, and X. Zheng, “Coordinated optimization of logistics scheduling for electric logistics vehicles and energy management in integrated logistics parks with PV and ESS,” Energy, vol. 341, p. 139296, 2025.
  32. P. Zare, H. Shayeghi, S. SeyedShenava, and B. Mohamadi, “Interactive scheduling for climate-neutral sustainable cities via carbon capture-enabled virtual energy communities considering digital-social welfare and joint certificate trading,” Sustain. Cities Soc., p. 107208, 2026.
  33. A. Mohammadian, A. Dejamkhooy, S. SeyedShenava, P. Zare, and B. Mohamadi, “Cross-sectoral synergy policy in climate-neutral sustainable cities with eco-friendly public transportation fleets and hydrogen-pivoted multi-energy transactions,” Energy, p. 140296, 2026.
  34. H. Yang, P. You, and C. Shang, “Distributed planning of electricity and natural gas networks and energy hubs,” Appl. Energy, vol. 282, p. 116090, 2021.
  35. J. Zhong, Y. Tan, Y. Li, Y. Cao, Y. Peng, Z. Zeng, Y. Nakanishi, and Y. Zhou, “Distributed operation for integrated electricity and heat system with hybrid stochastic/robust optimization,” Int. J. Electr. Power Energy Syst., vol. 128, p. 106680, 2021.
  36. P. Aliasghari, B. Mohammadi-Ivatloo, and M. Abapour, “Risk-based scheduling strategy for electric vehicle aggregator using hybrid stochastic/IGDT approach,” J. Clean. Prod., vol. 248, p. 119270, 2020.
  37. A. Najafi, A. Tavakoli, M. Pourakbari-Kasmaei, and M. Lehtonen, “A risk-based optimal self-scheduling of smart energy hub in the day-ahead and regulation markets,” J. Clean. Prod., vol. 279, p. 123631, 2021.
  38. P. Cai, C. Wen, B. Cao, and J. Qiao, “A Wasserstein metric distributionally robust chance-constrained peer aggregation energy sharing mechanism for hydrogen-based microgrids considering low-carbon drivers,” Energy, vol. 325, p. 136178, 2025.
  39. A. Najafi, H. Falaghi, J. Contreras, and M. Ramezani, “Medium-term energy hub management subject to electricity price and wind uncertainty,” Appl. Energy, vol. 168, pp. 418– 433, 2016.
  40. A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, B. Mohammadi-Ivatloo, M. Abapour, and K. Zare, “Robust scheduling of hydrogen-based smart micro energy hub with integrated demand response,” J. Clean. Prod., vol. 267, p. 122041, 2020.
Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 04 August 2026

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History

  • Receive Date: 30 December 2025
  • Revise Date: 14 February 2026
  • Accept Date: 09 May 2026
  • First Publish Date: 04 August 2026

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