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

Author

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  • 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
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    References listed on IDEAS

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    Cited by:

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    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. 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).
    7. 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.

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