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Two-Stage Optimization Strategies for Integrating Electric Vehicles in the Energy Internet

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Abstract

Electric vehicles (EVs) form an important part of the energy internet, as they connect a transportation network with an electricity network. EV uptake largely depends on the optimization strategies of charging infrastructures such as battery swapping stations (BSSs). These stations can potentially reduce the upfront expenses of EV owners, range anxiety, long charging times and electricity grid strain. Currently, the major challenge in BSSs is the creation of robust business strategies. This chapter proposes BSS stochastic optimization strategies that consider EV uptake uncertainties and power distribution company decisions. Two stochastic optimizations involving two stages are investigated: (a) optimization with recourse and (b) bilevel optimization. The recourse optimization recommends initial battery investment even before the station visits are known in the planning stage and recommends battery allocations in the operation stage. This optimization links a transport network to a distribution line network, providing energy arbitrage and curtailment tractability. The bilevel optimization further links the transport network to a transmission line network using aggregated EV batteries as a form of flexible load to compensate for intermittent renewable source generation. The flexible load is a lower-level decision made by distribution company operators, and the same flexible load is a constraint in the upper-level decisions made by BSS owners. Furthermore, this optimization can link the transportation and electricity networks to a gas network in the presence of gas as a power source with varying marginal prices. The proposed strategies provide a pathway for integrating EVs in the energy internet.

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Acknowledgments

This work has been supported in part by the University of Sydney FEIT Mid-Career Research Development Scheme.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

    William Infante & Jin Ma

  2. Shanxi Key Laboratory of Power System Operation and Control, Taiyuan University of Technology, Taiyuan, China

    Xiaoqing Han

  3. School of Computer Science, The University of Sydney, Sydney, Australia

    Wei Li & Albert Y. Zomaya

Authors
  1. William Infante
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  2. Jin Ma
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  3. Xiaoqing Han
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  4. Wei Li
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  5. Albert Y. Zomaya
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Correspondence to Jin Ma .

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Editors and Affiliations

  1. College of Engineering, Design, and Physical Sciences, Brunel University London, Uxbridge, Middlesex, UK

    Ahmed F Zobaa

  2. Tsinghua University, Beijing, China

    Junwei Cao

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Infante, W., Ma, J., Han, X., Li, W., Zomaya, A.Y. (2020). Two-Stage Optimization Strategies for Integrating Electric Vehicles in the Energy Internet. In: Zobaa, A., Cao, J. (eds) Energy Internet. Springer, Cham. https://doi.org/10.1007/978-3-030-45453-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-45453-1_8

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  • Online ISBN: 978-3-030-45453-1

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