IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v273y2023ics0360544223006370.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223006370
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127243?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Adil Amin & Wajahat Ullah Khan Tareen & Muhammad Usman & Haider Ali & Inam Bari & Ben Horan & Saad Mekhilef & Muhammad Asif & Saeed Ahmed & Anzar Mahmood, 2020. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network," Sustainability, MDPI, vol. 12(23), pages 1-28, December.
    3. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Han, Yang & Lam, Jacqueline C.K. & Li, Victor O.K. & Newbery, David & Guo, Peiyang & Chan, Kelvin, 2024. "A deep learning approach for fairness-based time of use tariff design," Energy Policy, Elsevier, vol. 192(C).
    4. 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).
    5. Yang, So Young & Woo, JongRoul & Lee, Wonjong, 2024. "Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system," Energy Economics, Elsevier, vol. 138(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
    2. Zhaoming Yang & Qi Xiang & Yuxuan He & Shiliang Peng & Michael Havbro Faber & Enrico Zio & Lili Zuo & Huai Su & Jinjun Zhang, 2023. "Resilience of Natural Gas Pipeline System: A Review and Outlook," Energies, MDPI, vol. 16(17), pages 1-19, August.
    3. F. Isorna Llerena & E. López González & J. J. Caparrós Mancera & F. Segura Manzano & J. M. Andújar, 2021. "Hydrogen vs. Battery-Based Propulsion Systems in Unipersonal Vehicles—Developing Solutions to Improve the Sustainability of Urban Mobility," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    4. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    5. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Jicheng Liu & Fangqiu Xu & Shuaishuai Lin & Hua Cai & Suli Yan, 2018. "A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization," Energies, MDPI, vol. 11(12), pages 1-22, November.
    7. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    8. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    9. Mohammed Abdullah H. Alshehri & Youguang Guo & Gang Lei, 2023. "Energy Management Strategies of Grid-Connected Microgrids under Different Reliability Conditions," Energies, MDPI, vol. 16(9), pages 1-22, May.
    10. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    11. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    12. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).
    13. Xueying Song & Hongyu Lin & Gejirifu De & Hanfang Li & Xiaoxu Fu & Zhongfu Tan, 2020. "An Energy Optimal Dispatching Model of an Integrated Energy System Based on Uncertain Bilevel Programming," Energies, MDPI, vol. 13(2), pages 1-24, January.
    14. Vladimir Z. Gjorgievski & Nikolas G. Chatzigeorgiou & Venizelos Venizelou & Georgios C. Christoforidis & George E. Georghiou & Grigoris K. Papagiannis, 2020. "Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus," Energies, MDPI, vol. 13(8), pages 1-18, April.
    15. Wu, Xiong & Qi, Shixiong & Wang, Zhao & Duan, Chao & Wang, Xiuli & Li, Furong, 2019. "Optimal scheduling for microgrids with hydrogen fueling stations considering uncertainty using data-driven approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Sulaima, Mohamad Fani & Dahlan, Nofri Yenita & Yasin, Zuhaila Mat & Rosli, Marlinda Mohd & Omar, Zulkiflee & Hassan, Mohammad Yusri, 2019. "A review of electricity pricing in peninsular Malaysia: Empirical investigation about the appropriateness of Enhanced Time of Use (ETOU) electricity tariff," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 348-367.
    17. Maria Grazia Marchesano & Guido Guizzi & Silvestro Vespoli & Gabriella Ferruzzi, 2023. "Battery Swapping Station Service in a Smart Microgrid: A Multi-Method Simulation Performance Analysis," Energies, MDPI, vol. 16(18), pages 1-21, September.
    18. Nicolás Müller & Samir Kouro & Pericle Zanchetta & Patrick Wheeler & Gustavo Bittner & Francesco Girardi, 2019. "Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations," Energies, MDPI, vol. 12(9), pages 1-17, May.
    19. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    20. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223006370. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.