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Improved Control Strategy for Three-Phase Microgrid Management with Electric Vehicles Using Multi Objective Optimization Algorithm

Author

Listed:
  • Muhammad Shahab

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Current address: School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.)

  • Shaorong Wang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Current address: School of Electrical and Electronics Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.)

  • Abdul Khalique Junejo

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract
The usage of electric vehicles (EV) have been spreading worldwide, not only as an alternative to achieve a low-carbon future but also to provide ancillary services to improve the power system reliability. A common problem encountered in the existing alternating current (AC) grids is low power factor, which cause several power quality problems and has worsened with the growing application of distributed generation (DG). Therefore, considering the spread of EVs usage for ancillary services and the low power factor issue in current electrical grids, this paper proposes an improved control strategy for power factor correction of a three-phase microgrid management composed of a photovoltaic (PV) array, dynamic loads, and an EV parking lot. This control strategy aims to support power factor issues using the EV charging stations, allowing the full PV generation. Different operation modes are proposed to fulfill the microgrid and the EV users’ requirements, characterizing a Multi Objective Optimization (MOO) approach. In order to achieve these optimization requests, a dynamic programming method is used to charge the vehicle while adjusting the microgrid power factor. The proposed control algorithm is verified in different scenarios, and its results indicate a suitable performance for the microgrid even during conditions of overload and high peak power surplus in generation unit. The microgrid power factor remains above the desired reference during the entire analyzed period, in which the error is approximately 4.5 less than the system without vehicles, as well as obtains an energy price reduction.

Suggested Citation

  • Muhammad Shahab & Shaorong Wang & Abdul Khalique Junejo, 2021. "Improved Control Strategy for Three-Phase Microgrid Management with Electric Vehicles Using Multi Objective Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1146-:d:503310
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    References listed on IDEAS

    as
    1. Chao-Tsung Ma, 2019. "System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review," Energies, MDPI, vol. 12(21), pages 1-22, November.
    2. Fazel Mohammadi & Gholam-Abbas Nazri & Mehrdad Saif, 2019. "A Bidirectional Power Charging Control Strategy for Plug-in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 11(16), pages 1-24, August.
    3. Edivan Laercio Carvalho & Luiz Henrique Meneghetti & Emerson Giovani Carati & Jean Patric da Costa & Carlos Marcelo de Oliveira Stein & Rafael Cardoso, 2020. "Asymmetrical Pulse-Width Modulation Strategy for Current-Fed Dual Active Bridge Bidirectional Isolated Converter Applied to Energy Storage Systems," Energies, MDPI, vol. 13(13), pages 1-22, July.
    4. Fermín Barrero-González & María Isabel Milanés-Montero & Eva González-Romera & Enrique Romero-Cadaval & Carlos Roncero-Clemente, 2019. "Control Strategy for Electric Vehicle Charging Station Power Converters with Active Functions," Energies, MDPI, vol. 12(20), pages 1-18, October.
    5. Morsy Nour & José Pablo Chaves-Ávila & Gaber Magdy & Álvaro Sánchez-Miralles, 2020. "Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems," Energies, MDPI, vol. 13(18), pages 1-34, September.
    6. Tawfiq M. Aljohani & Ahmed F. Ebrahim & Osama Mohammed, 2020. "Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot," Energies, MDPI, vol. 13(13), pages 1-19, July.
    7. Woon-Gyu Lee & Thai-Thanh Nguyen & Hyeong-Jun Yoo & Hak-Man Kim, 2018. "Low-Voltage Ride-Through Operation of Grid-Connected Microgrid Using Consensus-Based Distributed Control," Energies, MDPI, vol. 11(11), pages 1-18, October.
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    Cited by:

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    3. Umar Salman & Khalid Khan & Fahad Alismail & Muhammad Khalid, 2021. "Techno-Economic Assessment and Operational Planning of Wind-Battery Distributed Renewable Generation System," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    4. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    5. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
    6. Bingyin Lei & Yue Ren & Huiyu Luan & Ruonan Dong & Xiuyuan Wang & Junli Liao & Shu Fang & Kaiye Gao, 2023. "A Review of Optimization for System Reliability of Microgrid," Mathematics, MDPI, vol. 11(4), pages 1-30, February.
    7. Grzegorz Karoń, 2022. "Energy in Smart Urban Transportation with Systemic Use of Electric Vehicles," Energies, MDPI, vol. 15(15), pages 1-5, August.
    8. Sheeraz Iqbal & Salman Habib & Noor Habib Khan & Muhammad Ali & Muhammad Aurangzeb & Emad M. Ahmed, 2022. "Electric Vehicles Aggregation for Frequency Control of Microgrid under Various Operation Conditions Using an Optimal Coordinated Strategy," Sustainability, MDPI, vol. 14(5), pages 1-25, March.

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