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A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem

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  • Najafi, Arsalan
  • Jasiński, Michał
  • Leonowicz, Zbigniew

Abstract

Electric vehicle (EV) aggregator is an entity, which facilitates the interaction between the grid and EV owners. The EV aggregator not only supports the EV owners but also participates in the electricity market by proper charging/discharging schedule of EVs. Due to the increasing number of parking lots (PLs) in the power system, there is an obvious benefit from the management of interconnected multi PLs with independent aggregators. Hence, this paper proposes a new distributed framework for the optimal management of two interconnected PLs. The alternating direction method of multipliers (ADMM) is utilized to coordinate the electricity exchange between the two PLs. The locational marginal price (LMP) uncertainty of the upstream grid as one of the most important challenges for the EV aggregators, is developed through robust optimization (RO). The driving pattern uncertainties of the EVs including the arrival time (AT), departure time (DT), and the initial state of charge of the EVs are taken into account by stochastic programming (SP). Therefore, a hybrid distributed RO-SP is created to deal with the EV aggregators problem. The numerical results reveal the effectiveness of the proposed model to participate in electricity market, support EV owners and coordinate the electricity exchange of PLs.

Suggested Citation

  • Najafi, Arsalan & Jasiński, Michał & Leonowicz, Zbigniew, 2022. "A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222005771
    DOI: 10.1016/j.energy.2022.123674
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    2. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach," Energy, Elsevier, vol. 283(C).
    3. Yin, WanJun & Wen, Tao & Zhang, Chao, 2023. "Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism," Energy, Elsevier, vol. 263(PA).
    4. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    5. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    6. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).
    7. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).

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