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, the 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 strategies of German homeowners based on an online experimental study. We 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.

All figures were done using the scripts final_plots_main.R and final_plots_SI.R available in https://github.com/alefunxo/P2P-communities-PV-Battery Main fig 1 can be recreated using final_dataset.csv Main fig 2 can be recreated using final_week.csv and final_peak.csv Main fig 3 can be recreated using hist_selling.csv, optimal_trading_individual.csv, and optimal_trading_community.csv SI fig 1 uses df_core.csv SI fig 2 use optimal_trading_individual.csv and optimal_trading_community.csv SI fig 5 uses not_participating.csv SI figs 14 and 15 use sc_vs_p2p.csv SI figs 17 and 18 use sensitivity.csv and sensitivity_means.csv