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Behavioural Change in Green Transportation: Micro-Economics Perspectives and Optimization Strategies

Author

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  • Chiara Bordin

    (Department of Computer Science, UiT, The Arctic University of Norway, 9019 Tromsø, Norway)

  • Asgeir Tomasgard

    (Department of Industrial Economics and Technology Management, NTNU, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

Abstract

The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at finding an optimal set of price signals to be sent to the drivers based on price elasticities. The objective is to satisfy the demand for a given price structure, or minimize the curtailment of loads, when there is a shortage of capacity. The key contribution is the use of elasticity matrices through which it is possible to estimate the EV drivers’ reactions to the price signals. As real-world data on relating the elasticity values to the EV driver’s behaviour are currently non-existent, we concentrate on sensitivity analysis to test how different assumptions on elasticities affect the optimal price structure. In particular, we study how market segments of drivers with different elasticities may affect the ability of the operator to both handle a capacity problem and properly satisfy the charging needs.

Suggested Citation

  • Chiara Bordin & Asgeir Tomasgard, 2021. "Behavioural Change in Green Transportation: Micro-Economics Perspectives and Optimization Strategies," Energies, MDPI, vol. 14(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3728-:d:579536
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    References listed on IDEAS

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    Cited by:

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    3. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).

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