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Residential photovoltaic self-consumption: Identifying representative household groups based on a cluster analysis of hourly smart-meter data

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Abstract

The on-site generation and direct consumption of electricity, so-called self-consumption, with a combined photovoltaic (PV) and battery storage system is becoming increasingly profitable for private households. The profitability of PV self-consumption system largely depends on the match of PV output and the household’s electricity consumption. In energy system modelling, the household’s consumption behaviour is represented by means of a standard load profile. However, the household sector’s heterogeneity is not reflected in one single profile, and the use of only one load profile results in a misjudgement of the profitability of self-consumption. In this study, we present a set of representative household groups that better represent the heterogeneous residential consumption behaviour. The household groups were compiled through the cluster analysis of smart-meter data based on hourly electricity consumption, using household characteristics as explanatory variables. Between the average load profiles of the groups, significant differences were found. Subsequently to the clustering, self-consumption based on a combined PV and battery system was simulated for each household. We found that the achievable level of self-consumption also differs between the groups, which in turn affect the profitability of the PV and battery systems. A statistical analysis revealed that employment and the presence of children are distinguishing factors for the different types of self-consumers. These results suggest that (i) the residential sector is not well represented by a single standard load profile, particularly so in the context of self-consumption modelling. (ii) Different self-consumer types can be identified through socio-demographic characteristics: We found that unemployed households achieve the highest self-sufficiency rates with an average of 40%, the lowest rates with 30% on average occur within households of educated families. (iii) Although the discrepancies are significant, the effect of these differences on profitability is still limited under the current market conditions.

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Notes

  1. According to Meier et al. (1999), the winter period in Germany is defined as lasting from 1 November until 20 March, and the summer period from 15 May until 14 September and the transition period is the time between summer and winter.

  2. According to our findings and Meier et al. (1999), the differences between individual days are minor compared to the differences between weekdays and weekends.

  3. Examples are the companies “Caterva” in Germany http://www.caterva.de/ and “Ampard” in Switzerland http://www.ampard.com/

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Acknowledgements

This work has been financially supported by the German Federal Ministry for Economic Affairs and Energy in the context of a project “Flexible Nachfrage als wichtiger Beitrag zur Energiewende und Baustein in der Energiesystemanalyse” as a part of the 6. Energieforschungsprogramm. Additionally, we would like to thank Tobias Fleiter, Jan Kersting and Katharina Wohlfarth for their input and valuable discussions.

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  1. Fraunhofer Institute for Systems and Innovation Research, Breslauer Str. 48, 76139, Karlsruhe, Germany

    Anna-Lena Klingler & Florian Schuhmacher

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  1. Anna-Lena Klingler
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Correspondence to Anna-Lena Klingler.

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Klingler, AL., Schuhmacher, F. Residential photovoltaic self-consumption: Identifying representative household groups based on a cluster analysis of hourly smart-meter data. Energy Efficiency 11, 1689–1701 (2018). https://doi.org/10.1007/s12053-017-9554-z

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