Methodology and data

Under favourable weather conditions the expansion of RES capacities can lead to RES gen- eration exceeding immediate consumption. Instead of being curtailed, this surplus output can be stored. There are several ways to model storage operation. One is to optimize charg- ing and discharging of storage to ensure better integration of RES surpluses. Uncertainty of future RES supply can be considered, although usually the literature uses a deterministic approach (Haas et al., 2017). In contrast to this optimization approach, in this paper I use a myopic storage heuristic. Any current RES output exceeding load is stored as long as the storage capacity permits and is curtailed otherwise. Once residual load – the difference between load and RES generation – is positive, energy is released from storage, displacing

conventional generation.43 _{The level of power in storage is equal to zero at the beginning of}

the year and can be positive at the end.

This paper applies myopic storage heuristic to the European network of 19 countries. In- cluded are Germany, Austria, Belgium, Switzerland, Czech republic, Denmark, Spain, Fin- land, France, Great Britain, Hungary, Italy, the Netherlands, Norway, Poland, Portugal, Swe- den, Slovenia and Slovakia. As a shorthand, further on in this paper they are referred to as “Europe”. While the aim was to cover as many countries as possible, the choice set was ultimately limited by data availability. To apply the storage heuristic for this (or any) region I need to set values of two parameters - RES capacity and energy capacity of storage. The first determines the volume of surplus RES output produced in the system, the second - the

ability of the system to shift consumption of surplus RES in time. Both variable parameters enter the myopic storage heuristic at total European levels. They are then distributed among countries according to electricity demand: countries with high demand are assumed to have more installed RES and larger volume of storage. Each country is represented as a single node. To study the effect of cross border transmission I consider two cases. The first one assumes that there is no cross border trade, and each country has to perform the storage heuristic on its own. In the second case markets in the network are fully integrated, with cross border lines able to accommodate whatever flows necessary, and the storage heuristic is implemented for the whole region. Further in this paper these two cases are referred to as autarky and copper plate, respectively.

Now let’s consider the parameters of storage heuristic in more detail. Installed RES is the first of them. To apply the myopic storage heuristic, I need to determine the volume of sur- plus RES output produced in the system. To calculate it I combine historical load data from ENTSO-E (2018a) with RES generation. To get the latter, I use the results of reanalysis done by Staffell and Pfenninger (2016c) and Pfenninger and Staffell (2016), available online

as a database44_{. They provide hourly historical capacity factors for on- and offshore wind}

and PV for the EU-28 plus Norway and Switzerland, based on NASA’s MERRA-2 dataset.45

Therefore to get RES generation I need to choose a year, that will give me load and weather patterns, and to make an assumption on the level of RES generation installed. In the follow- ing sections most of the results refer to the year 2014, with additional calculations provided for 2012-2014. In this paper I consider a range of installed RES capacities. As a starting point, in 2016 there was 242.3 GW of installed wind and PV in the studied countries (Euro- pean Commission, 2018c; ENTSO-E, 2018b). To scale up RES capacities I need to make an assumption on how they are distributed between technologies – onshore, offshore wind and PV – within each country. For this purpose I use weights based on the analysis by Fraunhofer IWES (2015), performed on behalf of Agora Energiewende. They study the flexibility require- ments arising from the 2030 target of about 50% of RES in the power supply. In particular, they assume that by 2030 the sum of wind onshore, wind offshore and PV in the studied countries will increase more than twofold, up to 522 GW. Capacity levels for different tech-

44_{Renewables.ninja (2018), https://www.renewables.ninja/downloads, see also Staffell and Pfenninger}

(2016b) and Staffell and Pfenninger (2016a).

45_{Staffell and Pfenninger (2016c) provide several versions of wind capacity factors. I use the one based on}

what they call “near-term” future wind fleet (current wind fleet plus under construction or with planning approval as of December 2016, see Renewables.ninja (2018)). Renewables.ninja (2018) provides two PV capacity fac- tors datasets, based on two different meteorological sources: NASA’s MERRA-2 and Meteosat-based CM-SAF SARAH satellite. However, Pfenninger and Staffell (2016) state that MEERA-2 version is more consistent on a long-term basis, therefore I use it.

nologies in Fraunhofer IWES (2015) come from national grid development plans and national energy strategy documents (for Austria, Germany, France and partially the Netherlands), and

from the “green transition” vision of ENTSO-E (2014) for the rest.46

The second variable parameter in the storage heuristic is the energy capacity of storage. I scale up the energy capacity of storage to demonstrate what effects it can have on the network. While power capacity refers to the instantaneous electricity flow, the energy ca- pacity of storage deals with power integrated over time. Essentially, this is the volume of storage. Since in the future we would like to use storage to shift surplus RES generation form periods with favourable weather conditions to prolonged periods of lull and dark hours, the energy capacity of storage will be the binding constraint. Currently, energy capacity of pumped storage in Europe is no more than 327 GWh (Sinn, 2017; European Commission, 2016). In this paper I set the upper limit on the range of possible storage volume values at 2.6 TWh. This is approximately equal to the sum of existing pumped storage capacity plus 2291 GWh of realizable potential for pumped storage in EU-15, Switzerland and Norway (van de

Vegte, 2015).47 _{This number is also comparable to 3 TWh of maximum estimates for the op-}

timal energy capacity of storage needed in Europe under different scenarios (Cebulla et al.,

2018).48

Given the values of two variable parameters – RES capacity and energy capacity of storage – the storage heuristic produces an output in form of a time series of conventional dispatch and RES curtailment. Finally, using this output, I calculate several indicators. The first is the curtailment rate - curtailed RES generation divided by total RES generation. It estimates the percentage of the RES generation that is ultimately wasted - i.e., neither consumer nor

stored.49 _{The second system indicator is the share of consumed RES generation in the}

total annual electricity demand, or RES penetration. It estimates the percentage of the load that is covered with either simultaneous RES generation or with RES energy released from

storage.50 _{The third indicator is the maximum hourly mismatch between the load on one hand}

46_{See table 6, page 81, in Fraunhofer IWES (2015).}

47_{EU-15 in van de Vegte (2015) includes Austria, Belgium, Denmark, Finland, France, Germany, Greece,}

Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United Kingdom.

48_{See Cebulla et al. (2018), excluding extreme outliers with high RES shares and CO2 certificate price of 400}

e/t.

49_{Curtailment rate in copper plate is equal to the RES generation curtailed, assuming no transmission con-}

straints between the countries, divided by the total RES generation. Curtailment rate in autarky is equal to the sum of RES generation curtailed in each country, assuming no transmission between the countries, divided by the total RES generation.

50_{Given installed RES capacity, the storage heuristic allows me to calculate the total conventional dispatch di-}

and RES generation and possible storage discharge on the other - the minimal conventional capacity that should be installed to avoid a blackout. Appendix C.1 provides calculation results for Germany, while appendix C.2 provides extra tables with calculation results.