Modeling Grid-Friendly Clean Energy Communities and Induced Intra-Community Cash Flows

In the context of decarbonizing the energy sector, energy communities play an increasingly important role. However, the German energy transition has reached a point where it is now necessary to look at electricity generation and consumption in a holistic, systemic manner. Building on the qualitative work of Gui and MacGill (2018), the present paper quantifies how the inception of different types of clean energy communities (CECs) changes the grid-friendliness of a community and how it induces intra-community cash flows. In detail, the paper creates three residential reference networks, representing a countryside, village, and suburb setting. To simulate the residential load, the model employs a behavior-based load profile generator. Moreover, it uses meteorological data to model the electricity generation via photovoltaic (PV) systems and wind energy converters (WECs). In the model, CECs can conduct four measures: improve the energy efficiency, buy PV systems in bulk, introduce peer-to-peer (P2P) trading, and install a WEC. The results indicate that by conducting only a single measure, CECs often create a tradeoff in the community’s grid-friendliness. In contrast, combining a measure with a second measure nearly always improves the community’s gridfriendliness. This shows that the measures complement each other well from a grid-friendliness perspective. With regard to intra-community cash flows, the paper estimates a realistic volume for local P2P trading as well as its upper bound. In this regard, the extent of the realistic cash flow alone may not be enough to justify the purchase of P2P-enabling infrastructure. From a grid perspective, the paper highlights the importance of exploiting the complementarity of CEC measures as well as the necessity to provide CECs with access to sufficient funding.