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A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets

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  • Zeynali, Saeed
  • Nasiri, Nima
  • Marzband, Mousa
  • Ravadanegh, Sajad Najafi

Abstract

This paper focuses on cooperative scheduling of the integrated plug-in hybrid electric vehicle fleets and wind farm system (IWPHEVS) in the day-ahead wholesale market (DWM), as well as its effects on the market outcomes and price, as a price-maker player. In this regard, a multi-objective two-stage bi-level hybrid stochastic-robust offering/bidding and scheduling strategy is developed. The upper-level problem, which is that of the IWPHEVS operator, encompasses two objectives, namely cost and emission. The cost objective is comprised of operational costs and the cost of power that is purchased in DWM. Additionally, the plug-in hybrid electric vehicles (PHEVs) are congregated into distinct fleets through k-means clustering. To inscribe PHEVs’ battery erosion, a comprehensive battery erosion model is comprehended, which is linearized by semi-integer variables. The uncertain data sets, such as vehicle fleets arrival/departure timings and their travelled miles are represented as scenarios according to their empirical distribution, which is acquired from the National household travel survey (NHTS). On the flip side, the wind power, which is a more unpredictable parameter, is designed as a robust optimization (RO) set, as it is apt to enhance the reliability issues regarding wind volatilities. The lower-level, embodies the wholesale market operator that has the objective of maximizing social welfare. Conclusively, different case studies of dump, smart and multi-objective charging are meticulously investigated to testify the potency of the proposed method. Based on the obtained findings on the proposed smart multi-objective framework, the IWPHEVS as a price-maker player, can manipulate locational marginal price as much as 4.4%, while the emissions can be curtailed by 40%.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921008242
    DOI: 10.1016/j.apenergy.2021.117432
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    2. Zeynali, Saeed & Nasiri, Nima & Ravadanegh, Sajad Najafi & Marzband, Mousa, 2022. "A three-level framework for strategic participation of aggregated electric vehicle-owning households in local electricity and thermal energy markets," Applied Energy, Elsevier, vol. 324(C).
    3. Khalili, Reza & Khaledi, Arian & Marzband, Mousa & Nematollahi, Amin Foroughi & Vahidi, Behrooz & Siano, Pierluigi, 2023. "Robust multi-objective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs," Applied Energy, Elsevier, vol. 334(C).
    4. Dey, Bishwajit & Misra, Srikant & Garcia Marquez, Fausto Pedro, 2023. "Microgrid system energy management with demand response program for clean and economical operation," Applied Energy, Elsevier, vol. 334(C).
    5. 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).
    6. Alwesabi, Yaseen & Avishan, Farzad & Yanıkoğlu, İhsan & Liu, Zhaocai & Wang, Yong, 2022. "Robust strategic planning of dynamic wireless charging infrastructure for electric buses," Applied Energy, Elsevier, vol. 307(C).
    7. Sun, Yunpeng & Razzaq, Asif & Sun, Huaping & Irfan, Muhammad, 2022. "The asymmetric influence of renewable energy and green innovation on carbon neutrality in China: Analysis from non-linear ARDL model," Renewable Energy, Elsevier, vol. 193(C), pages 334-343.
    8. 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).
    9. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    10. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    11. Sadeghi, Delnia & Ahmadi, Seyed Ehsan & Amiri, Nima & Satinder, & Marzband, Mousa & Abusorrah, Abdullah & Rawa, Muhyaddin, 2022. "Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings," Energy, Elsevier, vol. 253(C).
    12. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Zhang, Lei & Dorrell, David G. & Sun, Fengchun, 2022. "Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation," Applied Energy, Elsevier, vol. 327(C).
    13. 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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