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Optimized response to electricity time-of-use tariff of a compressed natural gas fuelling station

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  • Kagiri, Charles
  • Wanjiru, Evan M.
  • Zhang, Lijun
  • Xia, Xiaohua

Abstract

Compressed natural gas propulsion of vehicles has been shown to have advantages over petrol and diesel propulsion due to lower carbon dioxide emissions as well as the increased durability of vehicle engines. The growth of compressed natural gas as an alternative fuel to petrol and diesel can be accelerated by implementing strategies that result in the economical operation of the distribution infrastructure. Economic scheduling of power consumption is a useful strategy for reducing the cost of energy for both industrial and domestic consumers who operate in time-of-use based electricity pricing environments. In this paper, an optimal energy management strategy is proposed for the operation of a compressed natural gas fuelling station. The compressor energy consumption, being the main component of the total operating cost of the fuelling station, presents a cost saving opportunity through which optimal scheduling of operation can be used to lower cost of operation of the station. The developed model shows potential average savings of 59.3% in daily electricity costs while maximizing compressor life through minimization of compressor cycling.

Suggested Citation

  • Kagiri, Charles & Wanjiru, Evan M. & Zhang, Lijun & Xia, Xiaohua, 2018. "Optimized response to electricity time-of-use tariff of a compressed natural gas fuelling station," Applied Energy, Elsevier, vol. 222(C), pages 244-256.
    • Handle: RePEc:eee:appene:v:222:y:2018:i:c:p:244-256
    DOI: 10.1016/j.apenergy.2018.04.017
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    2. Yongliang Liang & Zhiqi Li & Yuchuan Li & Shuwen Leng & Hongmei Cao & Kejun Li, 2023. "Bilevel Optimal Economic Dispatch of CNG Main Station Considering Demand Response," Energies, MDPI, vol. 16(7), pages 1-28, March.
    3. Charles Kagiri & Lijun Zhang & Xiaohua Xia, 2019. "A Hierarchical Optimisation of a Compressed Natural Gas Station for Energy and Fuelling Efficiency under a Demand Response Program," Energies, MDPI, vol. 12(11), pages 1-24, June.
    4. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    5. Chen, Sumin & Zeng, Qingcheng & Li, Yantong, 2023. "Integrated operations planning in highly electrified container terminals considering time-of-use tariffs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    6. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    7. Humberto Verdejo Fredes & Benjamin Acosta & Mauricio Olivares & Fernando García-Muñoz & Francisco Tobar & Vannia Toro & Cesar Smith & Cristhian Becker, 2021. "Impact of Energy Price Stabilization Mechanism on Regulated Clients’ Tariffs: The Case of Chile," Sustainability, MDPI, vol. 13(21), pages 1-20, October.

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