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A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

Author

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  • Davide Falabretti

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Francesco Gulotta

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

Abstract

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach in identifying a suitable schedule for the charging requests of a large EV fleet (up to 3200 units), with acceptable computational effort. The benefits on the economic sustainability of the E-carsharing fleet given by the participation in the electricity market are also confirmed by an extensive sensitivity analysis.

Suggested Citation

  • Davide Falabretti & Francesco Gulotta, 2022. "A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3023-:d:798239
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    References listed on IDEAS

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    1. Hu, Zechun & Zhan, Kaiqiao & Zhang, Hongcai & Song, Yonghua, 2016. "Pricing mechanisms design for guiding electric vehicle charging to fill load valley," Applied Energy, Elsevier, vol. 178(C), pages 155-163.
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    3. Davide Falabretti & Francesco Gulotta & Lorenzo Spinelli, 2022. "Participation of Aggregated DERs to the Ancillary Services Market: A Monte Carlo Simulation-Based Heuristic Greedy-Indexing Model," Energies, MDPI, vol. 15(3), pages 1-23, January.
    4. repec:cdl:itsrrp:qt6wr90040 is not listed on IDEAS
    5. María Ampudia-Renuncio & Begoña Guirao & Rafael Molina-Sanchez & Luís Bragança, 2020. "Electric Free-Floating Carsharing for Sustainable Cities: Characterization of Frequent Trip Profiles Using Acquired Rental Data," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    6. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.
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