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Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands

Author

Listed:
  • Sung-Guk Yoon

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Seok-Gu Kang

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

Abstract

Two of the most important technologies for future power systems to reduce greenhouse gas are electric vehicles (EVs) and renewable generation. When EVs become more common, the overall demand of electricity will significantly increase because EVs consume a large amount of electricity. Also, a daily load curve with EVs heavily depends on how much electricity EVs consume and when electricity is consumed. The microgrid is an important technology to promote renewable generation, and the increased demand and changed load curve should be considered in the microgrid planning stage to install robust and economical microgrids. In this paper, we propose an algorithm for microgrid planning with EV charging demand to find the most economical configuration through which to maximally utilize renewable generation. The algorithm uses a renewable generation-following EV charging scheme and HOMER. Through simulations, it is shown that the microgrid constructed by the proposed algorithm reduces the investment cost and CO 2 emission.

Suggested Citation

  • Sung-Guk Yoon & Seok-Gu Kang, 2017. "Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands," Energies, MDPI, vol. 10(10), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1487-:d:113164
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    References listed on IDEAS

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    1. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    2. Hafez, Omar & Bhattacharya, Kankar, 2012. "Optimal planning and design of a renewable energy based supply system for microgrids," Renewable Energy, Elsevier, vol. 45(C), pages 7-15.
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    Cited by:

    1. Hao Pan & Ming Ding & Anwei Chen & Rui Bi & Lei Sun & Shengliang Shi, 2018. "Research on Distributed Power Capacity and Site Optimization Planning of AC/DC Hybrid Micrograms Considering Line Factors," Energies, MDPI, vol. 11(8), pages 1-18, July.
    2. Dominic A. Savio & Vimala A. Juliet & Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg, 2019. "Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach," Energies, MDPI, vol. 12(1), pages 1-28, January.
    3. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    4. Hao Pan & Ming Ding & Rui Bi & Lei Sun, 2019. "Research on Cooperative Planning of Distributed Generation Access to AC/DC Distribution (Micro) Grids Based on Analytical Target Cascading," Energies, MDPI, vol. 12(10), pages 1-20, May.
    5. Liu, Jia & Cao, Sunliang & Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2021. "Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage," Applied Energy, Elsevier, vol. 281(C).
    6. Jean-Michel Clairand & Carlos Álvarez-Bel & Javier Rodríguez-García & Guillermo Escrivá-Escrivá, 2020. "Impact of Electric Vehicle Charging Strategy on the Long-Term Planning of an Isolated Microgrid," Energies, MDPI, vol. 13(13), pages 1-18, July.
    7. Jing Liu & Yongping Li & Guohe Huang & Cai Suo & Shuo Yin, 2017. "An Interval Fuzzy-Stochastic Chance-Constrained Programming Based Energy-Water Nexus Model for Planning Electric Power Systems," Energies, MDPI, vol. 10(11), pages 1-23, November.
    8. 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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