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Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles

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  • Daryabari, Mohamad K.
  • Keypour, Reza
  • Golmohamadi, Hessam

Abstract

The penetration of plug-in electric vehicles is increasing in power systems all over the world. Due to imperfect data about the availability of electric vehicles, the uncertainty of the demand-side increases considerably. To overcome the problem, the power storage capacity of electric vehicles is addressed to provide demand flexibility for the supply-side. This paper proposes a novel structure for parking lot microgrids to provide day-ahead peak-shaving and valley-filling for power systems with distinct peak hours in the daily operation. The microgrid is comprised of electrical demands, e.g. heat ventilation and air conditioning, lighting, escalators, as well as self-generation facilities, including roof-top photovoltaic sites and gas-fired engines. Besides, the parking lot has smart charging stations to charge/discharge the electric vehicles based on the flexibility requirements of power systems and/or owners’ preferences. The microgrid is supplied by the wholesale electricity market and bilateral contracts. To leverage the flexibility potentials of electric vehicles, a data-driven approach is suggested that classifies the electric vehicles based on distinct characteristics, e.g. dwell time and preferred state of charge. The robust optimization approach is adopted to determine the optimum procurement strategies for the microgrid in the worst-case realization of the wholesale market price uncertainty. Finally, the robust-mixed integer linear programming is examined in Iran Power Grid not only to provide flexibility for the power network but also to make a profit for the microgrid.

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  • Daryabari, Mohamad K. & Keypour, Reza & Golmohamadi, Hessam, 2021. "Robust self-scheduling of parking lot microgrids leveraging responsive electric vehicles," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921003056
    DOI: 10.1016/j.apenergy.2021.116802
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    References listed on IDEAS

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    Cited by:

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    3. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    4. Zeynali, Saeed & Nasiri, Nima & Marzband, Mousa & Ravadanegh, Sajad Najafi, 2021. "A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets," Applied Energy, Elsevier, vol. 300(C).
    5. George-Williams, H. & Wade, N. & Carpenter, R.N., 2022. "A probabilistic framework for the techno-economic assessment of smart energy hubs for electric vehicle charging," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. Helindu Cumaratunga & Masaki Imanaka & Muneaki Kurimoto & Shigeyuki Sugimoto & Takeyoshi Kato, 2021. "Proposal of Priority Schemes for Controlling Electric Vehicle Charging and Discharging in a Workplace Power System with High Penetration of Photovoltaic Systems," Energies, MDPI, vol. 14(22), pages 1-23, November.
    7. Faris Adnan Padhilah & Kyeong-Hwa Kim, 2021. "A Centralized Power Flow Control Scheme of EV-Connected DC Microgrid to Satisfy Multi-Objective Problems under Several Constraints," Sustainability, MDPI, vol. 13(16), pages 1-37, August.
    8. Golmohamadi, Hessam, 2021. "Stochastic energy optimization of residential heat pumps in uncertain electricity markets," Applied Energy, Elsevier, vol. 303(C).
    9. Bhaskar P. Rimal & Cuiyu Kong & Bikrant Poudel & Yong Wang & Pratima Shahi, 2022. "Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues," Energies, MDPI, vol. 15(5), pages 1-24, March.
    10. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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