IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3843-d390573.html
   My bibliography  Save this article

Development of an Integrated Simulation Model for Load and Mobility Profiles of Private Households

Author

Listed:
  • Mathias Müller

    (Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 München, Germany
    Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 München, Germany)

  • Florian Biedenbach

    (Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 München, Germany)

  • Janis Reinhard

    (Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 München, Germany)

Abstract

The electrification of the mobility and heating sectors will significantly change the electrical behavior of households in the future. To investigate this behavior, it is important to include the heating and mobility sectors in load profile models. Existing models do not sufficiently consider these sectors. Therefore, this work aims to develop an integrated, consistent model for the electrical and thermal load of private households and their mobility behavior. The model needs to generate regionally distinct profiles depending on the building, household and resident type and should be valid for Germany. Based on a bottom-up approach, a model consisting of four components is developed. In an activity model based on a modified Markov chain process, persons are assigned to activities. The activities are then allocated to devices in the electrical and thermal models. A mobility model assigns distances to the journey activities. The results of the simulation to validate the model shows an average annual energy consumption per household of 2751 kWh and a shape of the average load profile, both in good agreement with the reference. Furthermore, the temporal distribution of the vehicles to the locations is in accordance with the reference but the annual mileage is slightly underestimated with 10,730 km.

Suggested Citation

  • Mathias Müller & Florian Biedenbach & Janis Reinhard, 2020. "Development of an Integrated Simulation Model for Load and Mobility Profiles of Private Households," Energies, MDPI, vol. 13(15), pages 1-33, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3843-:d:390573
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3843/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/15/3843/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    2. Dennis J. Aigner & Cynts Sorooshian & Pamela Kerwin, 1984. "Conditional Demand Analysis for Estimating Residential End-Use Load Profiles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 81-98.
    3. Grandjean, A. & Adnot, J. & Binet, G., 2012. "A review and an analysis of the residential electric load curve models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6539-6565.
    4. Bartels, Robert & Fiebig, Denzil G. & Garben, Michael & Lumsdaine, Robert, 1992. "An end-use electricity load simulation model : Delmod," Utilities Policy, Elsevier, vol. 2(1), pages 71-82, January.
    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. Kern, Timo & Dossow, Patrick & Morlock, Elena, 2022. "Revenue opportunities by integrating combined vehicle-to-home and vehicle-to-grid applications in smart homes," Applied Energy, Elsevier, vol. 307(C).
    2. Beck, J.-P. & Reinhard, J. & Kamps, K. & Kupka, J. & Derksen, C., 2022. "Model experiments in operational energy system analysis: Power grid focused scenario comparisons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    3. Andreas Weiß & Florian Biedenbach & Mathias Müller, 2022. "Probabilistic Load Profile Model for Public Charging Infrastructure to Evaluate the Grid Load," Energies, MDPI, vol. 15(13), pages 1-28, June.
    4. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    5. Maciej Neugebauer & Adam Żebrowski & Ogulcan Esmer, 2022. "Cumulative Emissions of CO 2 for Electric and Combustion Cars: A Case Study on Specific Models," Energies, MDPI, vol. 15(7), pages 1-17, April.
    6. Vollmuth, Patrick & Wohlschlager, Daniela & Wasmeier, Louisa & Kern, Timo, 2024. "Prospects of electric vehicle V2G multi-use: Profitability and GHG emissions for use case combinations of smart and bidirectional charging today and 2030," Applied Energy, Elsevier, vol. 371(C).
    7. Tepe, Benedikt & Haberschusz, David & Figgener, Jan & Hesse, Holger & Uwe Sauer, Dirk & Jossen, Andreas, 2023. "Feature-conserving gradual anonymization of load profiles and the impact on battery storage systems," Applied Energy, Elsevier, vol. 343(C).
    8. Alexander J. Bogensperger & Yann Fabel & Joachim Ferstl, 2022. "Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation," Energies, MDPI, vol. 15(3), pages 1-42, February.

    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. Huang, Yunyou & Zhan, Jianfeng & Luo, Chunjie & Wang, Lei & Wang, Nana & Zheng, Daoyi & Fan, Fanda & Ren, Rui, 2019. "An electricity consumption model for synthesizing scalable electricity load curves," Energy, Elsevier, vol. 169(C), pages 674-683.
    2. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    3. Grandjean, A. & Adnot, J. & Binet, G., 2012. "A review and an analysis of the residential electric load curve models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6539-6565.
    4. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    5. Chen, Jianli & Adhikari, Rajendra & Wilson, Eric & Robertson, Joseph & Fontanini, Anthony & Polly, Ben & Olawale, Opeoluwa, 2022. "Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model," Applied Energy, Elsevier, vol. 325(C).
    6. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    7. Tepe, Benedikt & Haberschusz, David & Figgener, Jan & Hesse, Holger & Uwe Sauer, Dirk & Jossen, Andreas, 2023. "Feature-conserving gradual anonymization of load profiles and the impact on battery storage systems," Applied Energy, Elsevier, vol. 343(C).
    8. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    9. McKenna, R. & Hofmann, L. & Merkel, E. & Fichtner, W. & Strachan, N., 2016. "Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake," Energy Policy, Elsevier, vol. 97(C), pages 13-26.
    10. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    11. Jingrong Tan & Lin Chen, 2022. "Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China," IJERPH, MDPI, vol. 19(21), pages 1-20, November.
    12. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    13. Lombardi, Francesco & Balderrama, Sergio & Quoilin, Sylvain & Colombo, Emanuela, 2019. "Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model," Energy, Elsevier, vol. 177(C), pages 433-444.
    14. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    15. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    16. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    17. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    18. Sahoo, Somadutta & Zuidema, Christian & van Stralen, Joost N.P. & Faaij, André, 2025. "Regionalized decision-supporting tool application for scenario analyses considering stakeholder interactions: A case study of the Groningen province in the northern Netherlands," Applied Energy, Elsevier, vol. 377(PD).
    19. Powell, Siobhan & Cezar, Gustavo Vianna & Rajagopal, Ram, 2022. "Scalable probabilistic estimates of electric vehicle charging given observed driver behavior," Applied Energy, Elsevier, vol. 309(C).
    20. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Working Papers e098, Tokyo Center for Economic Research.

    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:13:y:2020:i:15:p:3843-:d:390573. 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.