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Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household

Author

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  • Stefan Arens

    (DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Karen Derendorf

    (DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Frank Schuldt

    (DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Karsten Von Maydell

    (DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Carsten Agert

    (DLR Institute for Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

Abstract

An energy management system (EMS) for a household energy system is proposed in this paper, which is composed of a photovoltaic (PV) generator , a home energy storage (HES), an electric vehicle (EV), an electrical household load and a grid connection, with 24 h operation horizon. The EMS objective is to reduce the electricity cost of the household by using a linear optimization algorithm. Two different EV schedules are utilized for simulations. One mainly describes rides to work and the other describes rides in a domestic context, such as rides to a supermarket. A forecast algorithm for the electrical load of the household, based on k-means clustering and an artificial neural network, is evaluated and integrated into the EMS to realistically represent the household’s load profile. It is shown that the developed forecast algorithm performs better than two of the benchmarks. Another finding is that the more storage is available at PV-production intervals, the higher the effect of forecast uncertainties and the lower the electricity cost of the household, disregarding the investment cost.

Suggested Citation

  • Stefan Arens & Karen Derendorf & Frank Schuldt & Karsten Von Maydell & Carsten Agert, 2018. "Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household," Energies, MDPI, vol. 11(11), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2913-:d:178353
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    References listed on IDEAS

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    Cited by:

    1. Marco Pasetti & Stefano Rinaldi & Alessandra Flammini & Michela Longo & Federica Foiadelli, 2019. "Assessment of Electric Vehicle Charging Costs in Presence of Distributed Photovoltaic Generation and Variable Electricity Tariffs," Energies, MDPI, vol. 12(3), pages 1-20, February.
    2. Marlon Schlemminger & Raphael Niepelt & Rolf Brendel, 2021. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles," Energies, MDPI, vol. 14(8), pages 1-24, April.
    3. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    4. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.

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