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Stochastic energy management of responsive plug-in electric vehicles characterizing parking lot aggregators

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

Abstract

Rapid advances in Plug-in Electrical Vehicle (PEV) technologies are expanding charging infrastructures especially in large shopping centers. Flexibility potential of PEVs can compensate for volatility and intermittency of electricity generation in power systems with high penetration of renewable energy sources. This paper proposes a novel multi-stage stochastic-based structure for Parking Lot Aggregators (PLA) to integrate flexibility potentials of PEVs into power systems. The proposed approach uses three trading floors of a short-term electricity market, including day-ahead, adjustment, and balancing markets, to trade the PEVs’ flexibility, from 24 h before the energy delivery time until near real-time, in the electricity market. In order to extract the behavior of the PEVs in Shopping Center Parking Lots (SCPL), a data-driven approach is formulated. The suggested data-driven approach makes it possible to optimize the charging/discharging operation of a huge number of PEVs with low time and computational burdens, which is a clear need for near real-time optimization. Instead of subsidizing the responsive PEVs, the PLA maximizes the profit of the market participants, i.e. PEV owners, through trading integrated flexibility in three floors of the electricity market. The suggested approach not only maximizes the income of the PEV owners but also provides functional flexibility to electricity markets, which is a clear need for future power systems with high penetration of intermittent power. Numerical studies with real data on an SCPL and the Danish sector of the Nordic Electricity Market assures competence of the proposed approach to guarantee power system flexibility against the intermittency of renewable energy sources.

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  • Daryabari, Mohamad K. & Keypour, Reza & Golmohamadi, Hessam, 2020. "Stochastic energy management of responsive plug-in electric vehicles characterizing parking lot aggregators," Applied Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920312381
    DOI: 10.1016/j.apenergy.2020.115751
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    Cited by:

    1. Edgar Ramos Muñoz & Faryar Jabbari, 2022. "An Octopus Charger-Based Smart Protocol for Battery Electric Vehicle Charging at a Workplace Parking Structure," Energies, MDPI, vol. 15(17), pages 1-25, September.
    2. 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.
    3. Mahdi Azimian & Vahid Amir & Reza Habibifar & Hessam Golmohamadi, 2021. "Probabilistic Optimization of Networked Multi-Carrier Microgrids to Enhance Resilience Leveraging Demand Response Programs," Sustainability, MDPI, vol. 13(11), pages 1-30, May.
    4. Wang, Yi & Qiu, Dawei & He, Yinglong & Zhou, Quan & Strbac, Goran, 2023. "Multi-agent reinforcement learning for electric vehicle decarbonized routing and scheduling," Energy, Elsevier, vol. 284(C).
    5. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    6. Zhu, Dafeng & Yang, Bo & Ma, Chengbin & Wang, Zhaojian & Zhu, Shanying & Ma, Kai & Guan, Xinping, 2022. "Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints," Applied Energy, Elsevier, vol. 317(C).
    7. 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).
    8. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    9. Hessam Golmohamadi & Saeed Golestan & Rakesh Sinha & Birgitte Bak-Jensen, 2024. "Demand-Side Flexibility in Power Systems, Structure, Opportunities, and Objectives: A Review for Residential Sector," Energies, MDPI, vol. 17(18), pages 1-22, September.
    10. Bagheri Tookanlou, Mahsa & Pourmousavi, S. Ali & Marzband, Mousa, 2023. "A three-layer joint distributionally robust chance-constrained framework for optimal day-ahead scheduling of e-mobility ecosystem," Applied Energy, Elsevier, vol. 331(C).
    11. Hashemipour, Naser & Crespo del Granado, Pedro & Aghaei, Jamshid, 2021. "Dynamic allocation of peer-to-peer clusters in virtual local electricity markets: A marketplace for EV flexibility," Energy, Elsevier, vol. 236(C).
    12. Golmohamadi, Hessam, 2021. "Stochastic energy optimization of residential heat pumps in uncertain electricity markets," Applied Energy, Elsevier, vol. 303(C).
    13. Khashayar Hamedi & Shahrbanoo Sadeghi & Saeed Esfandi & Mahdi Azimian & Hessam Golmohamadi, 2021. "Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    14. 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.
    15. 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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