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Integration of prosumer peer-to-peer trading decisions into energy community modelling

Nature Energy volume 7, pages 74–82 (2022)Cite this article

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Abstract

Peer-to-peer (P2P) exchange of renewable energy is an attractive option to empower citizens to actively participate in the energy transition. Whereas previous research has assessed P2P communities primarily from a techno-economic perspective, little is yet known about prosumer preferences for solar power trading. Importantly, impacts of community members’ trading decisions on key performance indicators, such as individual electricity bills, community autarky and grid stress, remain unknown. Here, we assess P2P trading decisions of German homeowners on the basis of an online experimental study, and simulate how various decision-making strategies impact the performance of P2P communities. The findings suggest that community autarky is slightly higher when prosumers are enabled to trade energy compared to when they merely aim to maximize their self-consumption. Our analysis, moreover, shows that P2P energy trading based on human decision-making may lead to financial benefits for prosumers and traditional consumers, and reduced stress for the grid.

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Fig. 1: Impact of P2P trading and self-consumption maximization strategies at the individual and aggregated level.
Fig. 2: Impact of P2P community trading and self-consumption maximization strategies at the grid level.
Fig. 3: Impact of different P2P trading strategies on the individual and aggregated level.

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Data availability

All the raw data, including the online study, and the data required to create each figure, are available at https://doi.org/10.5281/zenodo.5571499. The electricity consumption data from Germany are available at http://pvspeicher.htw-berlin.de

Code availability

All the code that supports the findings of this study, and the code used to generate the figures is available in https://github.com/alefunxo/P2P-communities-PV-Battery. Python (v.3.7.2) and R (v.3.6.3) were used for data analysis and simulation, including the following packages: pandas (v.0.24.2), numpy (v.1.16.1), seaborn (v.0.7.1), matplotlib (v.3.0.2), prosumpy (unique version), ggplot2 (v.3.3.3), ggpubr (v.0.4.0), grid (base), gridExtra (v.2.3), reshape2 (v.1.4.3), dplyr (.1.0.2).

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Acknowledgements

This research project was financially supported by the Swiss Innovation Agency Innosuisse and is part of the Swiss Competence Center for Heat and Electricity Storage (SCCER-HaE) with the following grant number: 1157002526 awarded to M.P. and D.P., as well as part of the activities of the Competence Center for Research in Energy, Society and Transition (CREST). This work was supported by a grant from the Swiss National Research Foundation (grant no. 188637) awarded to D.P and U.J.J.H. Additionally, funding was provided to V.T. by the Bavarian State Ministry of Science and the Arts (coordinated by the Bavarian Research Institute for Digital Transformation (BIDT)). The funding sources had no involvement in the preparation of the article, in the study design, the collection, analysis and interpretation of data, nor in the writing of the manuscript. Furthermore, we thank P. Timoner for his insights in statistical analyses and E. Hartvigsson for his insights on grid modelling.

Author information

Authors and Affiliations

  1. Energy Efficiency Group, Institute for Environmental Sciences and Forel Institute, University of Geneva, Geneva, Switzerland

    Alejandro Pena-Bello, David Parra & Martin K. Patel

  2. Department of Psychology and Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland

    Mario Herberz & Ulf J. J. Hahnel

  3. School of Business, Economics and Society, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Nuremberg, Germany

    Verena Tiefenbeck

Authors
  1. Alejandro Pena-Bello
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  6. Ulf J. J. Hahnel
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Contributions

A.P.-B., D.P. and U.J.J.H. provided conceptualization of the project, carried out the investigations and wrote the original draft of the paper. A.P.-B., D.P., U.J.J.H. and V.T. designed the methodology. A.P.-B. managed the software and validation. Formal analysis was undertaken by A.P.-B., D.P., U.J.J.H. and M.H. U.J.J.H. and M.H. curated the data. M.K.P., D.P. and U.J.J.H. supervised the project and acquired funding. V.T., M.H., D.P., U.J.J.H. and M.K.P. reviewed and edited the manuscript. All authors gave their final approval for publication.

Corresponding authors

Correspondence to David Parra or Ulf J. J. Hahnel.

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The authors declare no competing interests.

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Peer review information Nature Energy thanks Binod Koirala and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Notes 1–7, Figs. 1–18 and Tables 1–8.

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Pena-Bello, A., Parra, D., Herberz, M. et al. Integration of prosumer peer-to-peer trading decisions into energy community modelling. Nat Energy 7, 74–82 (2022). https://doi.org/10.1038/s41560-021-00950-2

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