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CO2 and Cost Impact Analysis of a Microgrid with Electric Vehicle Charging Infrastructure: A Case Study in Southern California

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  • Enriquez-Contreras, Luis Fernando
  • Barth, Matthew
  • Ula, Sadrul

Abstract

As a part of an innovative Intelligent Transportation System (ITS), this paper investigates the effectiveness of transportation-based microgrid configurations in reducing carbon dioxide (CO2) emissions and electricity costs. A case study at the University of California, Riverside (UCR) utilizes high-resolution California Independent System Operator (CAISO) CO2 emission data to assess the environmental impact of each microgrid configuration. It also compares electricity costs to determine potential consumer savings. The results demonstrate that a load-following transportation microgrid strategy can significantly reduce CO2 emissions (67%–84%) and achieve annual cost savings of approximately $24,000, even when accounting for the additional demand from daily electric vehicle (EV) charging at the building. However, battery sizing is crucial for cost-effectiveness, as load-following exhibits diminishing returns. Doubling battery capacity may yield negligible reductions in electricity costs and CO2 emissions after exceeding certain threshold. This emphasizes the importance of optimizing battery capacity to achieve a balance between cost and environmental impact. The study further reveals that Level 2 chargers in a commercial building generally have minimal impact on building demand and energy charges. Conversely, a single Level 3 DC fast charger has a more significant impact, requiring increased solar and battery storage capacity for further cost reduction. View the NCST Project Webpage

Suggested Citation

  • Enriquez-Contreras, Luis Fernando & Barth, Matthew & Ula, Sadrul, 2024. "CO2 and Cost Impact Analysis of a Microgrid with Electric Vehicle Charging Infrastructure: A Case Study in Southern California," Institute of Transportation Studies, Working Paper Series qt8br5m587, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt8br5m587
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    References listed on IDEAS

    as
    1. Sung-Guk Yoon & Seok-Gu Kang, 2017. "Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands," Energies, MDPI, vol. 10(10), pages 1, September.
    2. Tan, Bifei & Chen, Haoyong, 2020. "Multi-objective energy management of multiple microgrids under random electric vehicle charging," Energy, Elsevier, vol. 208(C).
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    Keywords

    Engineering; Infrastructure for charging; communication and controls; energy storage and control systems; electric vehicles;
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