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Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress

Author

Listed:
  • Hadi Suyono

    (Electrical Engineering Department, Faculty of Engineering, Universitas Brawijaya, Malang 65145, Jawa Timur, Indonesia)

  • Mir Toufikur Rahman

    (Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Hazlie Mokhlis

    (Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Mohamadariff Othman

    (Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Hazlee Azil Illias

    (Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Hasmaini Mohamad

    (Faculty of Electrical Engineering, University of Technology MARA (UiTM), Shah Alam 40450, Malaysia)

Abstract

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

Suggested Citation

  • Hadi Suyono & Mir Toufikur Rahman & Hazlie Mokhlis & Mohamadariff Othman & Hazlee Azil Illias & Hasmaini Mohamad, 2019. "Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress," Energies, MDPI, vol. 12(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1500-:d:224626
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    References listed on IDEAS

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    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Dennis Aigner, 1985. "The Residential Electricity Time-of-Use Pricing Experiments: What Have We Learned?," NBER Chapters, in: Social Experimentation, pages 11-54, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Junaid Bin Fakhrul Islam & Mir Toufikur Rahman & Shameem Ahmad & Tofael Ahmed & G. M. Shafiullah & Hazlie Mokhlis & Mohamadariff Othman & Tengku Faiz Tengku Mohmed Noor Izam & Hasmaini Mohamad & Moham, 2023. "Multi-Objective-Based Charging and Discharging Coordination of Plug-in Electric Vehicle Integrating Capacitor and OLTC," Energies, MDPI, vol. 16(5), pages 1-20, February.
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    3. Jaehyun Lee & Eunjung Lee & Jinho Kim, 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme," Energies, MDPI, vol. 13(8), pages 1-18, April.
    4. Eric Galvan & Paras Mandal & Shantanu Chakraborty & Tomonobu Senjyu, 2019. "Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
    5. Junpeng Cai & Dewang Chen & Shixiong Jiang & Weijing Pan, 2020. "Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    6. Muhammad Usman & Wajahat Ullah Khan Tareen & Adil Amin & Haider Ali & Inam Bari & Muhammad Sajid & Mehdi Seyedmahmoudian & Alex Stojcevski & Anzar Mahmood & Saad Mekhilef, 2021. "A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization," Energies, MDPI, vol. 14(17), pages 1-16, August.
    7. 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.
    8. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    9. Nur Ayeesha Qisteena Muzir & Md. Rayid Hasan Mojumder & Md. Hasanuzzaman & Jeyraj Selvaraj, 2022. "Challenges of Electric Vehicles and Their Prospects in Malaysia: A Comprehensive Review," Sustainability, MDPI, vol. 14(14), pages 1-40, July.

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