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Bidding strategy for wireless charging roads with energy storage in real-time electricity markets

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  • Shi, Jie
  • Yu, Nanpeng
  • Gao, H. Oliver

Abstract

The combination of wireless charging roads and energy storage systems is a promising option for electric vehicle charging because of their capabilities in mitigating range anxiety of electric vehicle drivers. Wireless charging road operators can purchase electric energy by submitting price-sensitive demand bids in real-time electricity markets. Efficient bidding strategies are crucial to minimizing the energy costs for providing wireless charging services. In this study, we first propose a composite statistical model based on graph signal processing and linear regression to forecast the future locational marginal prices (LMPs) in a power network. Then an estimate of future electric load on each wireless charging road is derived by simulating its traffic flow using a point queue-based traffic flow model. An efficient price-sensitive bidding strategy for each individual wireless charging road is developed based on its LMP forecast, wireless charging load estimate, and a model predictive control framework. Our numerical example shows that the proposed price-sensitive demand bidding strategy reduces the electric energy cost for operating a wireless charging road with an energy storage system by 6% compared to a baseline bidding strategy.

Suggested Citation

  • Shi, Jie & Yu, Nanpeng & Gao, H. Oliver, 2022. "Bidding strategy for wireless charging roads with energy storage in real-time electricity markets," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922012922
    DOI: 10.1016/j.apenergy.2022.120035
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    References listed on IDEAS

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    1. Shi, Jie & Gao, H. Oliver, 2022. "Efficient energy management of wireless charging roads with energy storage for coupled transportation–power systems," Applied Energy, Elsevier, vol. 323(C).
    2. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    3. Liu, Haoxiang & Zou, Yuncheng & Chen, Ya & Long, Jiancheng, 2021. "Optimal locations and electricity prices for dynamic wireless charging links of electric vehicles for sustainable transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    4. Jin, Wen-Long & Wang, Xuting & Lou, Yingyan, 2020. "Stable dynamic pricing scheme independent of lane-choice models for high-occupancy-toll lanes," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 64-78.
    5. Liu, Yang & Yu, Nanpeng & Wang, Wei & Guan, Xiaohong & Xu, Zhanbo & Dong, Bing & Liu, Ting, 2018. "Coordinating the operations of smart buildings in smart grids," Applied Energy, Elsevier, vol. 228(C), pages 2510-2525.
    6. Zheng, Yanchong & Yu, Hang & Shao, Ziyun & Jian, Linni, 2020. "Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets," Applied Energy, Elsevier, vol. 280(C).
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    Cited by:

    1. Bakker, J. & Lopez Alvarez, J.A. & Buijs, P., 2024. "A network design perspective on the adoption potential of electric road systems in early development stages," Applied Energy, Elsevier, vol. 361(C).

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