Abstract. Uncoordinated extension of renewable energy sources (RES) disregarding local demand structures leads to increased loads on the transmission grid and overall economic losses. One approach to solve this problem is to support the local power consumption by local power generation without making use of the transmission network. Therefore, the actual physical coverage of local demand with local supply is to be investigated instead of a yearly net power balance. Rural municipalities are an ideal starting point to establish such self-sufficient power supply systems on the basis of RES as they have a high RES potential in combination with low demand loads.

Fluctuating feed-in of wind and solar power and peaks in demand loads can be balanced by bioenergy as flexible power generation capacity. In contrast to highly resource dependent wind and solar power, biomass can be stored and power generation from biomass can be controlled flexibly. To assess the potential of electricity from biomass, this study analyses the agricultural structure of the rural municipalities. The objective of this study is to assess what kind of agricultural structure might be advantageous for flexible power generation from bioenergy, hence balancing fluctuation RES feed-in and power demand. The results from this structural assessment of rural municipalities can help for analysing further municipalities to identify potentials at first sight without costly individual analysis. Heat and fuel sectors are neglected. A methodology is introduced to model time series of wind, PV and biomass power with a 15-minute resolution. It is evaluated to which degree it is possible to cover the local demand in power supply with bioenergy as flexible power generation capacity in the identified clusters.

The results indicate that bioenergy is generally suitable to cover the gap between local power demand and supply. Waste products from animal farming are far more effective for biomass power production than from agricultural farming. Low population densities raise the potential for self-sufficiency in the power sector because of low demand loads. Further improvement of the model is needed concerning the clustering approach and for the approximation of installed wind and PV power capacities.