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Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost

Author

Listed:
  • Chitchai Srithapon

    (Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Prasanta Ghosh

    (Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA)

  • Apirat Siritaratiwat

    (Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Rongrit Chatthaworn

    (Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners’ energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator.

Suggested Citation

  • Chitchai Srithapon & Prasanta Ghosh & Apirat Siritaratiwat & Rongrit Chatthaworn, 2020. "Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost," Energies, MDPI, vol. 13(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:349-:d:307322
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    References listed on IDEAS

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    Cited by:

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    2. Rittichai Liemthong & Chitchai Srithapon & Prasanta K. Ghosh & Rongrit Chatthaworn, 2022. "Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs," Energies, MDPI, vol. 15(2), pages 1-22, January.
    3. Pattasad Seangwong & Supanat Chamchuen & Nuwantha Fernando & Apirat Siritaratiwat & Pirat Khunkitti, 2022. "A Novel Six-Phase V-Shaped Flux-Switching Permanent Magnet Generator for Wind Power Generation," Energies, MDPI, vol. 15(24), pages 1-11, December.
    4. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    5. Xiaohong Jiang & Xiucheng Guo, 2020. "Evaluation of Performance and Technological Characteristics of Battery Electric Logistics Vehicles: China as a Case Study," Energies, MDPI, vol. 13(10), pages 1-23, May.
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    7. Rahman, Md Mustafizur & Gemechu, Eskinder & Oni, Abayomi Olufemi & Kumar, Amit, 2023. "The development of a techno-economic model for assessment of cost of energy storage for vehicle-to-grid applications in a cold climate," Energy, Elsevier, vol. 262(PA).
    8. 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.
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