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Erschienen in: Energy Efficiency 7/2018

15.02.2018 | Original Article

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

verfasst von: Anna-Lena Klingler, Florian Schuhmacher

Erschienen in: Energy Efficiency | Ausgabe 7/2018

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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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Fußnoten
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/​
 
Literatur
Zurück zum Zitat Azad, S., Ali, A., & Wolfs, P. (2014). Identification of typical load profiles using k-means clustering algorithm, Computer Science and Engineering (APWC on CSE), 2014 Asia-Pacific World Congress on. IEEE, 2014. S, pp. 1–6. Azad, S., Ali, A., & Wolfs, P. (2014). Identification of typical load profiles using k-means clustering algorithm, Computer Science and Engineering (APWC on CSE), 2014 Asia-Pacific World Congress on. IEEE, 2014. S, pp. 1–6.
Zurück zum Zitat BDEW. (2010). Energie-Info. Berlin: Energieverbrauch im Haushalt. BDEW-Datenkatalog. BDEW. (2010). Energie-Info. Berlin: Energieverbrauch im Haushalt. BDEW-Datenkatalog.
Zurück zum Zitat BDEW. (2016). BDEW-Strompreisanalysen Mai 2016. Berlin: Haushalte und Industrie. BDEW. (2016). BDEW-Strompreisanalysen Mai 2016. Berlin: Haushalte und Industrie.
Zurück zum Zitat Bossmann, T., Pfluger, B., & Wietschel, M. (2013). The shape matters! How structural changes in the electricity load curve affect optimal investments in generation capacity, 10th international conference on the European Energy Market (EEM), Stockholm. Bossmann, T., Pfluger, B., & Wietschel, M. (2013). The shape matters! How structural changes in the electricity load curve affect optimal investments in generation capacity, 10th international conference on the European Energy Market (EEM), Stockholm.
Zurück zum Zitat Bode, S., Grooscurth, H. (2013). Zur vermeintlichen “Grid Parity” von Photovoltaik-Anlagen, energiewirtschaftliche Tagesfragen 2013, H. 7, p. 39–43. Bode, S., Grooscurth, H. (2013). Zur vermeintlichen “Grid Parity” von Photovoltaik-Anlagen, energiewirtschaftliche Tagesfragen 2013, H. 7, p. 39–43.
Zurück zum Zitat Breyer, C., & Gerlach, A. (2013). Global overview on grid-parity. Prog. Photovolt: Res. Appl., 21, 121–136.CrossRef Breyer, C., & Gerlach, A. (2013). Global overview on grid-parity. Prog. Photovolt: Res. Appl., 21, 121–136.CrossRef
Zurück zum Zitat Bruch, M., & Müller, M. (2014). Calculations of the cost-effectiveness of a PV battery system. Energy Procedia, 46, 262–270.CrossRef Bruch, M., & Müller, M. (2014). Calculations of the cost-effectiveness of a PV battery system. Energy Procedia, 46, 262–270.CrossRef
Zurück zum Zitat Campoccia, A., Dusonchet, L., Telaretti, E., & Zizzo, G. (2013). An analysis of feed’in tariffs for solar PV in six representative countries of the European Union. Solar Energy, 107, 530–542.CrossRef Campoccia, A., Dusonchet, L., Telaretti, E., & Zizzo, G. (2013). An analysis of feed’in tariffs for solar PV in six representative countries of the European Union. Solar Energy, 107, 530–542.CrossRef
Zurück zum Zitat Carmo, C., & Christensen, T. (2016). Cluster analysis of residential heat load profiles and the role of technical and household characteristics. Energy and Buildings, 125, 171–180.CrossRef Carmo, C., & Christensen, T. (2016). Cluster analysis of residential heat load profiles and the role of technical and household characteristics. Energy and Buildings, 125, 171–180.CrossRef
Zurück zum Zitat Elsland, R., T. Boßmann, A.-L. Klingler, N. Friedrichsen, & M. Klobasa (2015). Mittelfristprognose zur Deutschland-weiten Stromabgabe an Letztverbraucher für die Kalenderjahre 2016 bis 2020. Fraunhofer ISI. Study commissioned by the German Transmission Grid Operators. Elsland, R., T. Boßmann, A.-L. Klingler, N. Friedrichsen, & M. Klobasa (2015). Mittelfristprognose zur Deutschland-weiten Stromabgabe an Letztverbraucher für die Kalenderjahre 2016 bis 2020. Fraunhofer ISI. Study commissioned by the German Transmission Grid Operators.
Zurück zum Zitat EU Commission (2015). Best practices on renewable energy self-consumption, SWD 141 final, Brussels. EU Commission (2015). Best practices on renewable energy self-consumption, SWD 141 final, Brussels.
Zurück zum Zitat Fahrmeir, L., Kneib, T., & Lang, S. (2009). Regression–Modelle, Methoden und Anwendungen. Berlin Heidelberg: Springer.MATH Fahrmeir, L., Kneib, T., & Lang, S. (2009). Regression–Modelle, Methoden und Anwendungen. Berlin Heidelberg: Springer.MATH
Zurück zum Zitat Flath, C., Nicolay, D., Conte, T., Dinther, C. V., & Filipova-Neumann, L. (2012). Cluster analysis of smart metering data–an implementation in Practice, BISE-Research paper, pp. 31–39.CrossRef Flath, C., Nicolay, D., Conte, T., Dinther, C. V., & Filipova-Neumann, L. (2012). Cluster analysis of smart metering data–an implementation in Practice, BISE-Research paper, pp. 31–39.CrossRef
Zurück zum Zitat Gerblinger, A., Finkel, M., Witzmann, R. (2014). Entwicklung und Evaluierung von neuen Standardlastprofilen für Haushaltskunden, 13. Symposium Energieinnovationen, Graz. Gerblinger, A., Finkel, M., Witzmann, R. (2014). Entwicklung und Evaluierung von neuen Standardlastprofilen für Haushaltskunden, 13. Symposium Energieinnovationen, Graz.
Zurück zum Zitat Gouveia, J., & Seixas, J. (2016). Unraveling electricity consumption profiles in households through clusters: combining smart meters and door-to-door surveys. Energy and Buildings, 116, 666–676.CrossRef Gouveia, J., & Seixas, J. (2016). Unraveling electricity consumption profiles in households through clusters: combining smart meters and door-to-door surveys. Energy and Buildings, 116, 666–676.CrossRef
Zurück zum Zitat Haben, S., Singleton, C., & Grindrod, P. (2016). Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Transactions on Smart Grid, 7(1), 136–144.CrossRef Haben, S., Singleton, C., & Grindrod, P. (2016). Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Transactions on Smart Grid, 7(1), 136–144.CrossRef
Zurück zum Zitat Hayn, M., Bertsch, V., & Fichtner, W. (2014). Electricity load profiles in Europe: the importance of household segmentation. Energy Research & Social Science, 3, 30–45.CrossRef Hayn, M., Bertsch, V., & Fichtner, W. (2014). Electricity load profiles in Europe: the importance of household segmentation. Energy Research & Social Science, 3, 30–45.CrossRef
Zurück zum Zitat Hinterstocker, M., Roon, S., & Rau, M. (2014). Bewertung der aktuellen Standardlastprofile österreichs und analyse zukünftiger Anpassungsmöglichkeiten im Strommarkt, 13. Symposium Energieinnovationen, Graz. Hinterstocker, M., Roon, S., & Rau, M. (2014). Bewertung der aktuellen Standardlastprofile österreichs und analyse zukünftiger Anpassungsmöglichkeiten im Strommarkt, 13. Symposium Energieinnovationen, Graz.
Zurück zum Zitat Hoppmann, J., Volland, J., Schmidt, T., & Hoffmann, V. (2014). The economic viability of battery storage for residential solar photovoltaic systems—a review and simulation model. Renewable and Sustainable Energy Reviews, 39, 1101–1118.CrossRef Hoppmann, J., Volland, J., Schmidt, T., & Hoffmann, V. (2014). The economic viability of battery storage for residential solar photovoltaic systems—a review and simulation model. Renewable and Sustainable Energy Reviews, 39, 1101–1118.CrossRef
Zurück zum Zitat Intelliekon (2017). Website of the Intelliekon project with publications and presentations about the project. www.intelliekon.de. Accessed 12.4.2017. Intelliekon (2017). Website of the Intelliekon project with publications and presentations about the project. www.​intelliekon.​de. Accessed 12.4.2017.
Zurück zum Zitat Jägemann, C., Hagspiel, S., Lindenberger, D. (2013), The economic inefficiency of grid parity: the case of German PV, EWI Working Paper, No. 13/19. Jägemann, C., Hagspiel, S., Lindenberger, D. (2013), The economic inefficiency of grid parity: the case of German PV, EWI Working Paper, No. 13/19.
Zurück zum Zitat Kairies, K., Haberschusz, D., Magnor, D., Leuthold, M., Badeda, J., & Sauer, D. (2015). Wissenschaftliches Mess- und Evaluierungsprogramm Solarstromspeicher. Jahresbericht 2015. Aachen: RWTH. Kairies, K., Haberschusz, D., Magnor, D., Leuthold, M., Badeda, J., & Sauer, D. (2015). Wissenschaftliches Mess- und Evaluierungsprogramm Solarstromspeicher. Jahresbericht 2015. Aachen: RWTH.
Zurück zum Zitat Kavousian, A., Rajagopal, R., Fischer, M. (2013), Determinants of residential electricity conumption: using smart meter data to examine the effect of climate, building charachteristics, appliance stock, and occupants’ behavior. Energy, 55, 184–194.CrossRef Kavousian, A., Rajagopal, R., Fischer, M. (2013), Determinants of residential electricity conumption: using smart meter data to examine the effect of climate, building charachteristics, appliance stock, and occupants’ behavior. Energy, 55, 184–194.CrossRef
Zurück zum Zitat Keitsch, K., Kondziella, H., Bruckner, T. (2016). Methodology for extracting dynamic standard load profiles from smart meter data, 14. Symposium Energieinnovationen, Graz. Keitsch, K., Kondziella, H., Bruckner, T. (2016). Methodology for extracting dynamic standard load profiles from smart meter data, 14. Symposium Energieinnovationen, Graz.
Zurück zum Zitat Kim, Y. I., Shin, J. H., Song, J. J., & Yang, I. K. (2009). Customer clustering and TDLP (typical daily load profile) generation using the clustering algorithm. In Transmission & Distribution Conference & Exposition: Asia and Pacific, pp. 1–4, IEEE. Kim, Y. I., Shin, J. H., Song, J. J., & Yang, I. K. (2009). Customer clustering and TDLP (typical daily load profile) generation using the clustering algorithm. In Transmission & Distribution Conference & Exposition: Asia and Pacific, pp. 1–4, IEEE.
Zurück zum Zitat Klingler, A., & Marwitz, S. (2016). Can residential self-consumption contribute to load reduction in low-voltage grids? 14. Symposium energieinnovationen, Graz. Klingler, A., & Marwitz, S. (2016). Can residential self-consumption contribute to load reduction in low-voltage grids? 14. Symposium energieinnovationen, Graz.
Zurück zum Zitat Klingler, A., Schuhmacher, F., & Wohlfarth, K. (2016). Identifying representative types of residential electricity consumers—a cluster analysis of hourly smart meter data, 4th European Conference on Behaviour and Energy Efficiency (Behave 2016), Coimbra. Klingler, A., Schuhmacher, F., & Wohlfarth, K. (2016). Identifying representative types of residential electricity consumers—a cluster analysis of hourly smart meter data, 4th European Conference on Behaviour and Energy Efficiency (Behave 2016), Coimbra.
Zurück zum Zitat Lund, P. (2015). Energy policy planning near grid parity using a price-driven technology penetration model. Technological Forecasting and Social Change, 90, 389–399.CrossRef Lund, P. (2015). Energy policy planning near grid parity using a price-driven technology penetration model. Technological Forecasting and Social Change, 90, 389–399.CrossRef
Zurück zum Zitat Luthander, R., Widén, J., Munkhammar, J., & Lingfors, D. (2016). Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment. Energy, 112, 221–231.CrossRef Luthander, R., Widén, J., Munkhammar, J., & Lingfors, D. (2016). Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment. Energy, 112, 221–231.CrossRef
Zurück zum Zitat May, N., & Neuhoff, K. (2016). Eigenversorgung mit Solarstrom—ein Treiber der Energiewende? DIW Roundup: Politik im Fokus, No. 89. May, N., & Neuhoff, K. (2016). Eigenversorgung mit Solarstrom—ein Treiber der Energiewende? DIW Roundup: Politik im Fokus, No. 89.
Zurück zum Zitat McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economoic variables: an Irish case study. Energy and Buildings, 48, 240–248.CrossRef McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economoic variables: an Irish case study. Energy and Buildings, 48, 240–248.CrossRef
Zurück zum Zitat Meier, H., Fünfgeld, C., Adam, T., & Schieferdecker, B. (1999), Repräsentative VDEW-Lastprofile, VDEW Materialien M-32/99, Frankfurt. Meier, H., Fünfgeld, C., Adam, T., & Schieferdecker, B. (1999), Repräsentative VDEW-Lastprofile, VDEW Materialien M-32/99, Frankfurt.
Zurück zum Zitat Moshövel, J., Kairies, K., Magnor, D., Leuthold, M., Bost, M., Gährs, S., Szczechowicz, E., Cramer, M., & Sauer, D. (2015). Analysis of the maximal possible grid relief from PV-peak-power impacts by using storage systems for increased self-consumption. Applied Energy, 137, 567–575.CrossRef Moshövel, J., Kairies, K., Magnor, D., Leuthold, M., Bost, M., Gährs, S., Szczechowicz, E., Cramer, M., & Sauer, D. (2015). Analysis of the maximal possible grid relief from PV-peak-power impacts by using storage systems for increased self-consumption. Applied Energy, 137, 567–575.CrossRef
Zurück zum Zitat Munoz, L., Huijben, J., Verhees, B., & Verbon, G. (2014). The power of grid parity: a discursive approach. Technological Forecasting and Social Change, 87, 179–190.CrossRef Munoz, L., Huijben, J., Verhees, B., & Verbon, G. (2014). The power of grid parity: a discursive approach. Technological Forecasting and Social Change, 87, 179–190.CrossRef
Zurück zum Zitat Mutanen, A., Ruska, M., Repo, S., & Jarventausta, P. (2011). Customer classification and load profiling method for distribution systems. IEEE Transactions on Power Delivery, 26(3), 1755–1763.CrossRef Mutanen, A., Ruska, M., Repo, S., & Jarventausta, P. (2011). Customer classification and load profiling method for distribution systems. IEEE Transactions on Power Delivery, 26(3), 1755–1763.CrossRef
Zurück zum Zitat Parra, D., Walkers, G., & Gillot, M. (2014). Modeling of PV generation, battery and hydrogen storage to investigate the benefits of energy storage for single dwelling. Sustainable Cities and Society, 10, 1–10.CrossRef Parra, D., Walkers, G., & Gillot, M. (2014). Modeling of PV generation, battery and hydrogen storage to investigate the benefits of energy storage for single dwelling. Sustainable Cities and Society, 10, 1–10.CrossRef
Zurück zum Zitat Rhodes, J., Wesley, C., Upshaw, C., Edgar, T., & Webber, M. (2014). Clustering analysis of residential electricity demand profiles. Applied Energy, 135, 461–471.CrossRef Rhodes, J., Wesley, C., Upshaw, C., Edgar, T., & Webber, M. (2014). Clustering analysis of residential electricity demand profiles. Applied Energy, 135, 461–471.CrossRef
Zurück zum Zitat Rodrigues, F., Duarte, J., Figueiredo, V., Vale, Z., & Cordeiro, M. (2003). A comparative analysis of clustering algorithms, applied to load profiling, Proceedings of MLDM, pp. 73–85, Leipzig. Rodrigues, F., Duarte, J., Figueiredo, V., Vale, Z., & Cordeiro, M. (2003). A comparative analysis of clustering algorithms, applied to load profiling, Proceedings of MLDM, pp. 73–85, Leipzig.
Zurück zum Zitat Schleich, J., Brunner, M., Götz, K., Klobasa, M., Gölz, S., & Sunderer, G. (2011). Smart metering in Germany—results of providing feedback information in a field trial. ECEE Summer Study, 2011, 1667–1674. Schleich, J., Brunner, M., Götz, K., Klobasa, M., Gölz, S., & Sunderer, G. (2011). Smart metering in Germany—results of providing feedback information in a field trial. ECEE Summer Study, 2011, 1667–1674.
Zurück zum Zitat Schubert, G. (2012). Modelling hourly electricity generation from PV and wind plants in Europe, 9th international Conference on the European Energy Market (EEM), Florence. Schubert, G. (2012). Modelling hourly electricity generation from PV and wind plants in Europe, 9th international Conference on the European Energy Market (EEM), Florence.
Zurück zum Zitat Statistisches Bundesamt (2013). Wirtschaftsrechnungen–Einkommens- und Verbrauchsstichprobe Wohnverhältnisse privater Haushalte. Fachserie 15 Sonderheft 1. Wiesbaden. Statistisches Bundesamt (2013). Wirtschaftsrechnungen–Einkommens- und Verbrauchsstichprobe Wohnverhältnisse privater Haushalte. Fachserie 15 Sonderheft 1. Wiesbaden.
Zurück zum Zitat Stenzel, P., Lissen, J., & Fleer, J. (2015). Impact of different load profiles on cost optimal system designs for battery supported PV systems, the 7th international conference on applied energy—ICAE2015. Energy Procedia, 75, 1862–1868.CrossRef Stenzel, P., Lissen, J., & Fleer, J. (2015). Impact of different load profiles on cost optimal system designs for battery supported PV systems, the 7th international conference on applied energy—ICAE2015. Energy Procedia, 75, 1862–1868.CrossRef
Zurück zum Zitat Waffenschmidt, E. (2014). Dimensioning of decentralized photovoltaic storages with limited feed-in power and their impact on the distribution grid. Energy Procedia, 46, 78–87.CrossRef Waffenschmidt, E. (2014). Dimensioning of decentralized photovoltaic storages with limited feed-in power and their impact on the distribution grid. Energy Procedia, 46, 78–87.CrossRef
Zurück zum Zitat Weniger, J., Bergner, J., Tjaden, T., & Quaschning, V. (2015). Dezentrale Solarstromspeicher für die Energiewende. Berlin: Hochschule für Technik und Wirtschaft Berlin. Weniger, J., Bergner, J., Tjaden, T., & Quaschning, V. (2015). Dezentrale Solarstromspeicher für die Energiewende. Berlin: Hochschule für Technik und Wirtschaft Berlin.
Zurück zum Zitat Yohanis, Y., Mondol, J., Wright, A., Norton, B. (2008). Real-life energy use in the UK: how occupancy and dwelling characteristics affect domestic electricity use. Energy and Buildings, 40, 1053–1059.CrossRef Yohanis, Y., Mondol, J., Wright, A., Norton, B. (2008). Real-life energy use in the UK: how occupancy and dwelling characteristics affect domestic electricity use. Energy and Buildings, 40, 1053–1059.CrossRef
Zurück zum Zitat Zhou, K., Yang, S., & Shen, C. (2013). A review of electric load classification in smart grid environment. Renewable and Sustainable Energy Reviews, 24, 103–110.CrossRef Zhou, K., Yang, S., & Shen, C. (2013). A review of electric load classification in smart grid environment. Renewable and Sustainable Energy Reviews, 24, 103–110.CrossRef
Zurück zum Zitat Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Review, 56, 215–225.CrossRef Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Review, 56, 215–225.CrossRef
Zurück zum Zitat Zhou, K., Yang, S., & Shao, Z. (2017). Household monthly electricity consumption pattern mining: a fuzzy clustering-based model and a case study. Journal of Cleaner Production, 141, 900–908.CrossRef Zhou, K., Yang, S., & Shao, Z. (2017). Household monthly electricity consumption pattern mining: a fuzzy clustering-based model and a case study. Journal of Cleaner Production, 141, 900–908.CrossRef
Metadaten
Titel
Residential photovoltaic self-consumption: Identifying representative household groups based on a cluster analysis of hourly smart-meter data
verfasst von
Anna-Lena Klingler
Florian Schuhmacher
Publikationsdatum
15.02.2018
Verlag
Springer Netherlands
Erschienen in
Energy Efficiency / Ausgabe 7/2018
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
DOI
https://doi.org/10.1007/s12053-017-9554-z

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