Geographical comparison between wind power, solar power and demand for the German regions and data filling concepts. (October 2018)

• Record Type: Journal Article
• Authors: Renken, Volker; Sorg, Michael; Marschner, Volker; Gerdes, Lewin; Gerdes, Gerhard; Fischer, Andreas
• Is Part Of: Renewable energy. Volume 126(2018)
• Page Start: 475
• Page End: 484
• Publication Date: 2018-10
• Subjects: Renewable energy -- Geographical distribution -- Solar -- Wind -- Data-based analysis -- Model-based data filling; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.03.046
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The rising penetration of renewable energies became an important issue in the German electricity sector within the past years. In order to plan the required infrastructure for the energy distribution, a detailed knowledge about the complete geographical and temporal power generation compared to the demand is crucial. However, the available data for the renewable power generation in Germany is insufficient due to the complexity of the energy system. For this reason, a comparison between the renewable power generation and the electricity demand is presented for 95 German zip code regions based on real input data with a sample time of 15 min from renewable energy generators. For enhancing the incomplete data, different model-based data filling methods using the data of neighboured regions or additional meteorological data are introduced and compared. As a result, a number of modelling methods, based either on a heuristic model, a wind speed model or a combination of both, has been investigated, leading to similar correlation coefficients of above 80%. Finally, the obtained data set is applied for an analysis with a high spatiotemporal resolution. For three use cases the resulting optimal flow of the inter-regional power transfers is calculated. Highlights: Analysis of the distribution of fluctuating renewable energy generation. Usage of real measurement wind and solar data with high spatiotemporal resolution. Temporal sampling rate of 15 min within all 2-digit zip code regions. Investigation of data filling methods using heuristic and wind speed models. Comparison with distributed power demand and visualization of optimal power flow.