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Time-of-Use tariff rates estimation for optimal demand-side management using electric vehicles

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  • Kaur, Amrit Pal
  • Singh, Mukesh

Abstract

The exponential growth of electric vehicles (EVs) has raised the electricity burden that may resolve through demand-side management (DSM). DSM restructure the power system that allows sustainable development without substantial expansion in the smart grid (SG). DSM with EVs is in the preliminary stage, relying on existing advanced metering infrastructure (AMI) to enable diverse motivational techniques. Amongst various schemes, Time-of-Use (ToU) price-based mechanism is the most accepted, where tariff rates vary with the day timing. However, determining the tariff rates is significant to motivate EV prosumers for efficient DSM. The paper proposes a methodology for ToU tariff estimation to provide optimal DSM using EVs with big data technology. The NoSQL database allows the accumulation of historical and real-time data with the computational environment for electric power. A novel mathematical model calculates the tariff rates using EVs’ peak and off-peak contribution coefficients. Besides, conditional prioritization is presented based on EVs’ State-of-Charge (SoC) to mitigate the simultaneous charging of numerous EVs. In the simulation, the aggregator (AG) manages the data from multiple internet-of-thing (IoT) based smart net meters with the proposed computational facility. Results demonstrated with realistic data have effectively reduced the peak consumption by 6%–7% with an elasticity of 0.45.

Suggested Citation

  • Kaur, Amrit Pal & Singh, Mukesh, 2023. "Time-of-Use tariff rates estimation for optimal demand-side management using electric vehicles," Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223006370
    DOI: 10.1016/j.energy.2023.127243
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    References listed on IDEAS

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    1. Venizelou, Venizelos & Philippou, Nikolas & Hadjipanayi, Maria & Makrides, George & Efthymiou, Venizelos & Georghiou, George E., 2018. "Development of a novel time-of-use tariff algorithm for residential prosumer price-based demand side management," Energy, Elsevier, vol. 142(C), pages 633-646.
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    4. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
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    Cited by:

    1. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    2. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
    3. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).

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