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Charging and Discharging Scheduling for Electrical Vehicles Using a Shapley-Value Approach

Author

Listed:
  • Marija Zima-Bockarjova

    (ABB Corporate Research Center, 5405 Baden-Dättwil, Switzerland)

  • Antans Sauhats

    (Institute of Power Engineering, Riga Technical University (RTU), LV-1658 Riga, Latvia)

  • Lubov Petrichenko

    (Institute of Power Engineering, Riga Technical University (RTU), LV-1658 Riga, Latvia)

  • Roman Petrichenko

    (Institute of Power Engineering, Riga Technical University (RTU), LV-1658 Riga, Latvia)

Abstract

The number of electric vehicles (EV) in the world has been increasing and is gaining momentum. The large-scale use of EVs in public life has initiated the need to establish EV battery charging services within the power system. Currently, EVs serve as a transportation tool and also as a flexible load. This publication examines the possibility of the owner of an electric vehicle choosing a battery recharging point, as well as of the involvement of several decision makers in the selection of a charging schedule. This problem is important because we assume that a significant proportion of EVs mainly use two parking spaces, one located close to the place of residence and another close to the workplace. We accept and prove that a car charging station can be created by the employer (company) and implemented in the best interests of the employer and the employee (EV owner). For that, a coalition between the company and the EV owner has to be formed. To support rational decisions, this study solves the problem using the cooperative game theory and designs a payment distribution mechanism based on the Shapley value. The results obtained prove that the coalition is beneficial under different conditions, which depend on the capacity of the EV, the distance between the workplace and the place of residence, the difference in the electricity prices of the day, as well as the consumption of the company. In order to estimate the coalition’s gain, it is necessary to take into account the structure of the power tariff system for both the company and the EV owner. Furthermore, we prove that the presence of a coalition allows the company and the EV owner to reduce the annual fee for consumed power. The results of this analysis could be adopted by decision makers such as government agencies, companies, EV owners, and they are recommended for potential investors for the development of transport electrification and smart energy.

Suggested Citation

  • Marija Zima-Bockarjova & Antans Sauhats & Lubov Petrichenko & Roman Petrichenko, 2020. "Charging and Discharging Scheduling for Electrical Vehicles Using a Shapley-Value Approach," Energies, MDPI, vol. 13(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1160-:d:328223
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    References listed on IDEAS

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    1. Madina, Carlos & Zamora, Inmaculada & Zabala, Eduardo, 2016. "Methodology for assessing electric vehicle charging infrastructure business models," Energy Policy, Elsevier, vol. 89(C), pages 284-293.
    2. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
    3. Gerardo J. Osório & Miadreza Shafie-khah & Pedro D. L. Coimbra & Mohamed Lotfi & João P. S. Catalão, 2018. "Distribution System Operation with Electric Vehicle Charging Schedules and Renewable Energy Resources," Energies, MDPI, vol. 11(11), pages 1-20, November.
    4. Steffen Limmer, 2019. "Evaluation of Optimization-Based EV Charging Scheduling with Load Limit in a Realistic Scenario," Energies, MDPI, vol. 12(24), pages 1-16, December.
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    2. Héricles Eduardo Oliveira Farias & Camilo Alberto Sepulveda Rangel & Leonardo Weber Stringini & Luciane Neves Canha & Daniel Pegoraro Bertineti & Wagner da Silva Brignol & Zeno Iensen Nadal, 2021. "Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(5), pages 1-27, March.
    3. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
    4. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    5. Yan Bao & Fangyu Chang & Jinkai Shi & Pengcheng Yin & Weige Zhang & David Wenzhong Gao, 2022. "An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory," Energies, MDPI, vol. 15(14), pages 1-20, July.
    6. Zeinab Teimoori & Abdulsalam Yassine, 2022. "A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation," Sustainability, MDPI, vol. 14(21), pages 1-23, October.

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