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Two-stage stochastic micro-market model for isolated microgrids considering PEVs traffic flow

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  • Abedi, Tohid
  • Alavi-Eshkaftaki, Amin
  • Zandrazavi, Seyed Farhad
  • Shafie-khah, Miadreza

Abstract

The growing adoption of plug-in electric vehicles (PEVs) and developments of renewable energy resources (RES) in isolated microgrids (IMGs) pose various challenges to these networks. To cope with the operational problems of IMGs in the presence of PEVs, this paper proposes a comprehensive market-based energy management system (EMS), called micro-market (μM), incorporating different participants and PEVs traffic flow. The proposed model considers both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes with different traffic behavior of parking lots (PLs) and charging stations (CSs) in an IMG market environment (including uniform electricity pricing and unit commitment). Establishing a bidirectional linearized AC (BLAC) power flow enables the μM to efficiently schedule the joint active-reactive power of all participants and provide local energy trading conditions. Moreover, the developed μM schedules the cost of joint up/downward active-reactive reserve power effectively, ensuring the IMG will be operated securely. The suggested model is a two-stage stochastic mixed integer linear programming (MILP), with the objective of maximizing social welfare (SW) for the upcoming day. The introduced model's efficacy is assessed by testing the μM in an IMG on the CIGRE medium-voltage benchmark, considering three different operational cases. The results reveal that incorporating PEVs traffic flow into the μM model increases SW by 3.7 %. However, adding battery degradation costs, while causing a 3 % reduction in the objective function, significantly enhances the model's practicality by meeting IMGs real-world operational requirements. Overall, the results verified the ability of the proposed model to address different operational challenges while the PEV owners' preferences are satisfied and secure operation is ensured.

Suggested Citation

  • Abedi, Tohid & Alavi-Eshkaftaki, Amin & Zandrazavi, Seyed Farhad & Shafie-khah, Miadreza, 2025. "Two-stage stochastic micro-market model for isolated microgrids considering PEVs traffic flow," Applied Energy, Elsevier, vol. 388(C).
  • Handle: RePEc:eee:appene:v:388:y:2025:i:c:s0306261925003824
    DOI: 10.1016/j.apenergy.2025.125652
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    References listed on IDEAS

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    1. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Ghoreishi, Niloofar & Catalão, João P.S., 2020. "Optimal power management of dependent microgrid considering distribution market and unused power capacity," Energy, Elsevier, vol. 200(C).
    2. Jani, Ali & Jadid, Shahram, 2023. "Two-stage energy scheduling framework for multi-microgrid system in market environment," Applied Energy, Elsevier, vol. 336(C).
    3. Doumen, Sjoerd C. & Nguyen, Phuong & Kok, Koen, 2022. "Challenges for large-scale Local Electricity Market implementation reviewed from the stakeholder perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    4. Zandrazavi, Seyed Farhad & Guzman, Cindy Paola & Pozos, Alejandra Tabares & Quiros-Tortos, Jairo & Franco, John Fredy, 2022. "Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles," Energy, Elsevier, vol. 241(C).
    5. Rodriguez, Mauricio & Arcos-Aviles, Diego & Guinjoan, Francesc, 2024. "Simple fuzzy logic-based energy management for power exchange in isolated multi-microgrid systems: A case study in a remote community in the Amazon region of Ecuador," Applied Energy, Elsevier, vol. 357(C).
    6. Jafari, Amirreza & Ganjeh Ganjehlou, Hamed & Khalili, Tohid & Bidram, Ali, 2020. "A fair electricity market strategy for energy management and reliability enhancement of islanded multi-microgrids," Applied Energy, Elsevier, vol. 270(C).
    7. Fathy, Ahmed, 2023. "Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles," Applied Energy, Elsevier, vol. 334(C).
    8. 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).
    9. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources," Energy, Elsevier, vol. 239(PB).
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