IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5578-d877542.html
   My bibliography  Save this article

Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development

Author

Listed:
  • Paweł Piotrowski

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Dariusz Baczyński

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Marcin Kopyt

    (Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

Abstract

The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%).

Suggested Citation

  • Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5578-:d:877542
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5578/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    2. Viri, Riku & Mäkinen, Johanna & Liimatainen, Heikki, 2021. "Modelling car fleet renewal in Finland: A model and development speed-based scenarios," Transport Policy, Elsevier, vol. 112(C), pages 63-79.
    3. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
    4. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2020. "Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland," Energies, MDPI, vol. 13(1), pages 1-18, January.
    5. Galvin, Ray, 2022. "Are electric vehicles getting too big and heavy? Modelling future vehicle journeying demand on a decarbonized US electricity grid," Energy Policy, Elsevier, vol. 161(C).
    6. Krzysztof Zagrajek, 2021. "A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision," Energies, MDPI, vol. 14(21), pages 1-38, November.
    7. Krzysztof Zagrajek & Józef Paska & Mariusz Kłos & Karol Pawlak & Piotr Marchel & Magdalena Bartecka & Łukasz Michalski & Paweł Terlikowski, 2020. "Impact of Electric Bus Charging on Distribution Substation and Local Grid in Warsaw," Energies, MDPI, vol. 13(5), pages 1-13, March.
    8. Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    9. Kalhori, M. Rostam Niakan & Emami, I. Taheri & Fallahi, F. & Tabarzadi, M., 2022. "A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting," Applied Energy, Elsevier, vol. 314(C).
    10. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    11. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
    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. Mariusz Tomczyk & Henryk Wojtaszek & Małgorzata Chackiewicz & Małgorzata Orłowska, 2023. "Electromobility and Renewable Energy Sources: Comparison of Attitudes and Infrastructure in Poland and Germany," Energies, MDPI, vol. 16(24), pages 1-34, December.

    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. Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
    2. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    3. Monika Zimmermann & Florian Ziel, 2024. "Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting," Papers 2408.00507, arXiv.org, revised Dec 2024.
    4. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    6. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    7. Dong, Xianjing & Zhang, Xiaojuan & Zhang, Congcong & Bi, Chunyu, 2023. "Building sustainability education for green recovery in the energy resource sector: A cross country analysis," Resources Policy, Elsevier, vol. 81(C).
    8. Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
    9. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    10. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    11. Krzysztof Lowczowski & Jaroslaw Gielniak & Zbigniew Nadolny & Magdalena Udzik, 2024. "Analysis of the Impact of Volt/VAR Control on Harmonics Content and Alternative Harmonic Mitigation Methods," Energies, MDPI, vol. 17(22), pages 1-26, November.
    12. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.
    13. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2021. "BASS Model Analysis in “Crossing the Chasm” in E-Cars Innovation Diffusion Scenarios," Energies, MDPI, vol. 14(11), pages 1-16, May.
    14. Huang, Robert & Kahn, Matthew E., 2024. "An economic analysis of United States public transit carbon emissions dynamics," Regional Science and Urban Economics, Elsevier, vol. 107(C).
    15. Grigoroudis, Evangelos & Noel, Laurent & Galariotis, Emilios & Zopounidis, Constantin, 2021. "An ordinal regression approach for analyzing consumer preferences in the art market," European Journal of Operational Research, Elsevier, vol. 290(2), pages 718-733.
    16. Ergun Yukseltan & Esra Agca Aktunc & Ayse H. Bilge & Ahmet Yucekaya, 2024. "An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 96-111, March.
    17. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    18. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    19. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    20. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.

    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:gam:jeners:v:15:y:2022:i:15:p:5578-:d:877542. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.