Power transport needs for the German power grid for a major demand coverage by wind and solar power

• Published in: Energy strategy reviews 2021-03, Vol.34, p.100626, Article 100626
• Main Authors: Schädler, Yannik, Renken, Volker, Sorg, Michael, Gerdes, Lewin, Gerdes, Gerhard, Fischer, Andreas
• Format: Article
• Language: English
• Subjects: Load coverage; Load management; Optimization; Renewable energy; Solar energy; Transformation of energy system; Transport needs; Wind energy
• ISSN: 2211-467X; 2211-467X
• DOI: 10.1016/j.esr.2021.100626

The contribution of fluctuating wind and solar energy sources to the German electrical energy demand has increased from 12% up to 40% within the last 10 years and leads to higher infrastructure requirements due to the different spatiotemporal characteristics of wind and solar energy generation. However, common investigations of the energy system exhibit a limited spatiotemporal resolution of power or are based only on simulation models. Therefore, real measurement data regarding the energy demand and the generated energy from 95 German ZIP code regions are investigated to support current infrastructure decisions for the power system. In particular, the residual regional power generation with different wind and solar shares and the resulting power flows are calculated as the solution of a cost optimization problem without energy storage. As a result, a heterogeneous power distribution is identified with a medium surplus in northern and eastern regions and deficits in western and southern Germany. Furthermore, maximum regional coverage of the energy demand by solar and wind energy exists with respect to the share of wind and solar energy generation. For an extrapolation scenario of the average renewable energy to 100% of the average demand, the optimal share is 53% solar and 47% wind power. As a conclusion, a balance of the spatiotemporal fluctuations between renewable energy generation and demand is feasible with appropriate changes in the power grid system, e.g., load management. •Spatiotemporal wind, solar and demand data is used for different investigations.•Temporal fitting of renewables to demand is bad and motivates the usage of storage.•In the 100%-Scenario the best local coverage is reached with 53% solar share.•Transport costs are lower for scenarios with higher solar share.