Abstract
In the context of 100% renewable electricity systems, prolonged periods with persistently scarce supply from wind and solar resources have received increasing academic and political attention. This article explores how such scarcity periods relate to energy storage requirements. To this end, we contrast results from a time series analysis with those from a system cost optimization model, based on a German 100% renewable case study using 35 years of hourly time series data. While our time series analysis supports previous findings that periods with persistently scarce supply last no longer than two weeks, we find that the maximum energy deficit occurs over a much longer period of nine weeks. This is because multiple scarce periods can closely follow each other. When considering storage losses and charging limitations, the period defining storage requirements extends over as much as 12 weeks. For this longer period, the cost-optimal storage needs to be large enough to supply 36 TWh of electricity, which is about three times larger than the energy deficit of the scarcest two weeks. Most of this storage is provided via hydrogen storage in salt caverns, of which the capacity is even larger due to electricity reconversion losses (55 TWh). Adding other sources of flexibility, for example with bioenergy, the duration of the period that defines storage requirements lengthens to more than one year. When optimizing system costs based on a single year rather than a multi-year time series, we find substantial inter-annual variation in the overall storage requirements, with the average year needing less than half as much storage as calculated for all 35 years together. We conclude that focusing on short-duration extreme events or single years can lead to an underestimation of storage requirements and costs of a 100% renewable system.
Highlights
Our findings suggest that both time series analyses and optimization models often come with simplifications that may lead to an underestimation of storage requirements
We present the output from the time series analysis (Subsection 3.2) and compare it with the cost-optimal storage requirements (Subsection 3.3)
The investment in variable renewables varies by a lot (Figure 8a). This can be linked to the inter-annual variability of wind energy: for years with relatively high wind yields, the optimization model decides to build more wind power and less solar, and vice versa
Summary
Previous studies on renewable scarcity periods mostly focused on wind power (Cannon et al, 2015; Patlakas et al, 2017; Ohlendorf and Schill, 2020). These studies are similar in their approaches and results (Table 1). They define a threshold below which wind power or wind speed is considered “low”. On this basis, they characterize the frequency and duration of low-wind periods based on decades-long, national time series. The maximum duration of low-wind events identified in these studies is 4–10 days
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