How much electrical energy storage do we need? A synthesis for the U.S., Europe, and Germany
Graphical abstract
Introduction
Renewable energy sources are variable, uncertain, and location-specific. Thus, their integration into power systems requires flexibility. Flexibility can be understood as the ability to balance the residual load (electricity load minus variable renewable energy, VRE) (Huber et al., 2014). It can be provided by transmission and distributions grids, by the supply side (flexible power plants or curtailment of VRE), by demand-side management (DSM, including new loads as part of the electrification of demand such as electro-heating and cooling, e-mobility, and power-to-gas), and by electrical energy storage (EES) (Kondziella and Bruckner, 2015, Lund et al., 2015, Pamparana et al., 2017, Ren and Ren, 2018, Haaset al, 2018, Rahmann et al., 2016). This study will focus on EES requirements.
During the last 30 years, much research on different EES technologies has been produced. These frequently include a varied spectrum of batteries (Poullikkas, 2013, Longo et al., 2014), pumped-hydro plants (PHS) (Rehman et al., 2015, Deane et al., 2010), compressed air energy storage (CAES) (Budt et al., 2016), and hydrogen with the option for reconversion to electricity (H2) (Götzet al, 2016, Barthelemy et al., 2017), among others (Wicki and Hansen, 2017). Several recent studies (Lund et al., 2015, Luo et al., 2015, Amirante et al., 2017, Chen et al., 2009, Aneke and Wang, 2016, Ferreira et al., 2013), provide comprehensive reviews of these technologies. A widely accepted conclusion is that there is no storage option that outperforms all others (Chen et al., 2009). Hence, planning with a combination of storage options is a direct consequence.
Examples of studies that plan the required storage capacity for power systems with large shares of renewable energy (RE) are (Ueckerdt et al., 2017, Inage, 2009, Mileva et al., 2016, Frew et al., 2016, Handet al, 2012, Frew, 2014) for the U.S. or (Ueckerdt et al., 2017, Inage, 2009, Bertsch et al., 2016, Bussaret al, 2016, Zerrahn and Schill, 2015, Scholz et al., 2017, Bussaret al, 2015, Brouwer et al., 2016) for Europe. However, these studies result in a wide range of storage requirements, which makes it difficult for the policy maker to identify clear recommendations. Many methods, assumptions, and modeling approaches in storage expansion planning exist, as systemized in ref. (Haas et al., 2017), which may help to explain the variances in the results.
To date, there are a few initial efforts in systemizing the flexibility requirements. One example is the book from Droste-Franke (2015) which, based on studies from around 2010, comprehensively explains the flexibility requirements for Europe and Germany for different shares of renewables. Kondziella and Bruckner (2015) follow that line and provide an updated review of flexibility demand. Koskinen and Breyer (2016) provide a summary of global and trans-continental storage demand. Finally, Doetsch et al. (2014) review different reports, which analyze the need for EES in the German and European energy system. Most recently, Zerrahn and Schill (2017) provide a comprehensive review of storage planning with a focus on the modeling approach. Unexplained differences in the prognosed EES requirements remain, calling for a systematization of the many available storage expansion studies, particularly in the light of their derived storage capacity.
On the above premises, we analyzed and systemized recent EES expansion studies for three regions with strong renewable targets (U.S., Europe, and Germany), including 17 studies and over 400 scenarios. Our study makes three fundamental contributions to the literature:
- i)
for each region, we compare the obtained storage energy and power capacity requirements for VRE shares;
- ii)
as these studies result in a very broad range of storage sizes, we further narrow down the range of storage requirements by analyzing the main drivers, including the impact of different power mixes (photovoltaic- or wind-dominated);
- iii)
we discuss the impact of the electrical network modeling on the storage requirements.
Altogether, our findings provide direction to energy modelers regarding where to put effort when modeling future energy systems, as well as to policymakers towards a more precise understanding of the storage requirements.
Section 2, below, describes the analyzed studies. Section 3 presents the ranges of storage requirements found and discusses the main drivers. Finally, Section 4 draws the conclusions.
Section snippets
Methods
Our approach consists of three steps. First, we collect and systemize data from recent studies about storage expansion planning (Section 2.1). Second, we analyze and describe the models of the selected studies (Section 2.2) to then synthesize the storage requirements and filter unfit scenarios in our third step (Section 2.3). More detail on these steps will follow.
Results and discussion
This section systemizes and analyzes the need for storage capacities in the different regions recommended in the studies listed above. From the admissible scenarios, a broad spectrum of recommendations was found. For example, for VRE shares over 80%, the ranges of EES requirements are 15–530 GW (0.2–6 TWh) for the U.S., 10–350 GW (0.2–22 TWh) for Europe, and 8–140 GW (0.05–83 TWh) for Germany. These variances make it challenging for policymakers to quantify the real need of EES, which motivates
Conclusions
In this study, we analyzed the storage requirements arising from 17 recent storage expansion studies involving over 400 scenarios. For the U.S., Europe, and Germany, we first systemized their recommendations in terms of storage needs per share of VRE and discussed the outliers. Second, we studied how the dominance of a given generation technology (i.e. PV or wind) can help explain the storage needs. And third, we discussed the relevance of the detail of grid modeling. This synthesis helps to
Acknowledgments
We thank all authors of the cited studies that provided additional information to their publications. Further, we acknowledge financial support from the German Academic Exchange Service (DAAD), the Helmholtz Research School on Energy Scenarios, and the German Research Foundation through the grant DFG-NO 805/11-1. P. Mancarella also acknowledges the partial support of the UK EPSRC through the MY-STORE project (EP/N001974/1).
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