Versions Compared

Old Version 3

changes.mady.by.user Michela Giusti

Saved on

compared with

New Version 4

changes.mady.by.user Michela Giusti

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Anchor
Figure2_12
Figure2_12

 


 

Figure 2.12: TA (top) and TAW (bottom) averaged 1980 to 2021 over bidding zones (SZON).

...

Table 2.1: Required spatial aggregation for PECDv4.1.

CodeDescription of the aggregation levelSource
ORIGNot aggregatedGridded data
BIASNot aggregatedGridded data bias adjusted (CDFt method see Section 2.5)
NUT0CountryNUTS0+ADMIN0
NUT2Sub Country/ProvincesNUTS2+ADMIN1
SZONOnshore Bidding Zones Shapefile provided by ENTSO-E 
SZOFOffshore Bidding ZonesShapefile provided by ENTSO-E
PEON

Pan-European Onshore Zones

Shapefile provided by ENTSO-E
PEOFPan-European Offshore Zones Shapefile provided by ENTSO-E


Anchor
Figure2_13
Figure2_13
 

Figure 2.13: Examples of the original polygons used to derive the float masks.

...

Table 2.2: Climate indicators provided in the PECDv4.1 for the historical stream. Files provided at ORIG spatial aggregation are gridded (NetCDF format), while all the other levels of aggregation are provided in CSV format.

VariablePeriodSourceDomain/ spatial resolutionTemporal resolutionSpatial aggregationUnits
2m temperature (TA)1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyORIG, NUT0, NUT2, SZON, SZOF, PEON, PEOF

K (gridded)

°C (aggregated)

Population-weighted temperature (TAW)1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlySZON°C
Total precipitation (TP)1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyORIG, NUT0, NUT2, SZON, SZOF, PEON, PEOFm
Surface solar radiation downwards (GHI)1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyORIG, NUT0, NUT2, SZON, SZOF, PEON, PEOFW m-2
10m wind speed (WS10)1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyORIG, BIAS, NUT0, NUT2, SZON, SZOF, PEON, PEOFm s-1
100m wind speed (WS100)1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyORIG, NUT0, NUT2, SZON, SZOF, PEON, PEOFm s-1

Energy data
Anchor
Section2_7
Section2_7

...

Table 2.3: Description of exclusion areas.

CriteriaDescriptionSourceVariable Name
Protected areasDataset with the constraint for protected areas. Gridded data at 0.25°x0.25° spatial resolution over the globe domain, with a binary format, where 1 represents a restricted pixel under this specific criterion.

World database on protected areas from the United Nations Environment Programme

prot_a
Polar areasDataset with the constraint for polar areas. Gridded data at 0.25°x0.25° spatial resolution over the globe domain, with a binary format, where 1 represents a restricted pixel under this specific criterion.

Land cover classification system from the United Nations Food and Agriculture Organization

polar_a
Urban areasDataset with the constraint for urban areas. Gridded data at 0.25°x0.25° spatial resolution over the globe domain, with a binary format, where 1 represents a restricted grid cell with an urban coverage equal or higher than 45%.

Land cover classification system from the United Nations Food and Agriculture Organization

urban_a
Water and continental waters areaDataset with the constraint for inland water areas. Gridded data at 0.25°x0.25° spatial resolution over the globe domain, with a three-value format, where 0 represents land, 1 represents ocean and 2 corresponds to inland waters.ERA5 land-sea mask from ECMWFwatr_a
High slope areaDataset with the constraint for high slope areas. Gridded data at 0.25°x0.25° spatial resolution over the globe domain, with a binary format, where 1 represents a restricted grid cell with a high slope coverage equal to or higher than 60%.

ETOPO1 Global Relief Model from National Oceanic and Atmospheric Administration

halo_a
High elevation areasDataset with the constraint for high elevation areas. Gridded data at 0.25°x0.25° grid resolution over the globe domain, with a binary format, where 1 represents a restricted pixel under this specific criterion.

ETOPO1 Global Relief Model from National Oceanic and Atmospheric Administration

hele_a
Distance to shore areasDataset with the constraint for the distance to shore for offshore areas. Gridded data at 0.25°x0.25° spatial resolution over the globe domain, with a binary format, where 1 represents a restricted pixel under this specific criterion.

ERA5 land-sea mask from ECMWF

dist_s


Energy Conversion models
Anchor
Section2_9
Section2_9

...

Table 2.4: The parameters defining the generic plant-level power curve, with the range of supported values for the parameters which can be varied.

Default parameters (fixed)

air_density

1.225 kg/m3

max_cp

0.49

constant_ct

0.8

gear_loss_const

0.01

gear_loss_var

0.014

generator_loss

0.03

converter_loss

0.03

turbulence_intensity

0.1

Varied parameters, with a supported range

Rotor diameter

10-250 m

Plant installation density

4-10 MW/km2

Specific power

100-650 W/m2

Number of turbines

1-1024


For future onshore wind installations, turbines with specific powers ranging from 198 to 335 W/m2, as shown in Swisher et al. (2022), are used. For future offshore wind installations, turbines with specific powers of 316 and 370 W/m2 are used. The selected specific powers are the same as those used in the PECD 2021 update. An overview of the simulated future wind technologies is given in Table 2.5 and Table 2.6, which also lists the corresponding options found in the widget "Technological specification" in the download form. Each wind technology option is labeled with a number representing a specific combination of hub height (HH) and specific power (SP). For example, "21 (SP316 HH155)" refers to offshore wind power with a specific power of 316 W/m² and a hub height of 155 m. These labels allow users to easily select the desired wind turbine specification from the dataset.

...

Table 2.5: Future technology onshore wind turbines.

Specific Power [W/m2]

Rotor Diameter [m]

Hub Height [m]

Rated Power [MW]

Correspondent codes in the download form on CDS

199

152

100, 150, 200

5

31 (SP199 HH100)
32 (SP199 HH150)
33 (SP199 HH200)

277

129

100, 150, 200

5

34 (SP277 HH100)
35 (SP277 HH150)
36 (SP277 HH200)

335

117

100, 150, 200

5

37 (SP335 HH100)
38 (SP335 HH150)
39 (SP335 HH200)

Anchor
Table2_6
Table2_6

Table 2.6: Future technology offshore wind turbines.

Specific Power [W/m2]

Rotor Diameter [m]

Hub Height [m]

Rated Power [MW]

Correspondent codes in the download form on CDS

316

269

155

18

21 (SP316 HH155)

370

249

155

18

22 (SP370 HH155)


The storm shutdown behavior is modeled as described in Murcia et al. (2021), assuming a direct (non-controlled) shutdown for all existing wind power plants (WPPs), using data from the WindPowerNet WPP installation database for the shutdown wind speeds. For future wind technologies, a 25 m/s cut-off is assumed for onshore wind installations, and the HWS (High Wind Speed) Deep type from Murcia et al. (2021) is used for future offshore wind installations (as in the PECD 2021 update). The shutdown procedure is modeled as a 'hysteresis,' where a restart occurs only after the wind speed has dropped to a sufficiently low value for a restart to take place (see Figure 2.16). The storm shutdown is a dynamic model that captures three aspects:

...

Table 2.7: Wind run types.

Run type

ERA5 simulated years

WPP locations

WPP technology

Losses

Validation (for validation only, not delivered)

2015-2021

Changed every year to match changing WPP installations (based on WindPowerNet data)

Existing WPP parameters based on WindPowerNet data (changed every year), applied in the generic power curve model

Wakes as part of the generic power curve. And 10 % for other losses (incl. unavailability), applied as a simple multiplication by 0.9

Existing

1980-2021

All years with 2020 WPP locations (based on WindPowerNet data)

Existing WPP parameters based on WindPowerNet data (always 2020 fleet), applied in the generic power curve model

Wakes as part of the generic power curve. And 10 % for other losses (incl. unavailability), applied as a simple multiplication by 0.9

Future wind technologies

1980-2021

The best 10-50 % locations of the unmasked points within each PECD region (in terms of mean wind speed in the bias-adjusted ERA5 data, based on ERA5 grid).

Onshore wind: 3 hub heights and 3 turbine types, so in total 9 wind technologies. A plant of 50 MW with ten 5 MW turbines modelled for each technology.

Offshore wind: 1 hub height and 2 turbine types, so in total 2 wind technologies. A plant of 500 MW with 28 18 MW turbines modelled for each technology.

Wakes as part of power curves. And 5 % for other losses (incl. unavailability), applied as a simple multiplication by 0.95


Some notes on Table 2.7:

  1. all wake modelling considers only intra-farm wakes (wakes between plants are not considered).
  2. Literature suggests a range of 5 % to 10 % for the other losses (Mortensen, 2018). The existing installations cover historical installations over tens of years with older technology, whereas the future installations are new installations (no wear-and-tear considered) with modern technology: it was thus considered fair to place them at the opposite sides of the loss range.
  3. A suitable mask is used to find the potential points for the Future wind technologies runs.
  4. Locations of existing wind power plants are not considered in the assessment of the 10-50 % best locations for each region. This is done because the decommissioning of old turbines is expected to free up more space for new installations in the future.
  5. The assumed locations of wind power plant installations within a region significantly impact the expected capacity factor on the aggregate level (Swisher et al., 2022). At this point, only one ‘resource grade’ (i.e., the 10-50 % best locations) is simulated; however, simulations covering also the 10 % best locations and the 50 % worst locations (or in principle any other distribution split between 0 and 100 %) could be provided in a later version of the PECD in consultation with ENTSO-E. However, this would multiply the amount of Future wind technology time series.

...

Anchor
Table2_8
Table2_8

Table 2.8: Solar multiple (SM) as a function of thermal energy storage (TES).

TES (hours)

SM

0

1.5

3

1.75

6

2.0

9

2.5

12

2.9

18 

3.0



Hydro Power conversion model
Anchor
Section2_9_4
Section2_9_4

...

Table 2.9: Schematic of the predictors (columns) used as input by the Random Forest model for the simulation of hydropower generation or inflow for the first weeks of January 2015 (generic dates). TA and TP stand respectively for 2-m temperature and total precipitation, while W followed by a number indicates the number of past weeks over which the variable has been averaged (for TA[K]) or aggregated (for TP[m]).

Date

TA_W1

TP_W1

TA_W2

TP_W2

TA_W3

TP_W3

…

2015-01-05

276

0.007

276

0.025

276

0.027

…

2015-01-12

278

0.009

277

0.016

276.7

0.034

…

…

…

…

…

…

…

…


The climate data used for both the training/validation, over the period when observations are available, and the subsequent reconstruction of the historical time series, extending it to 1979, comes from the ERA5 Reanalysis model.

...

Table 2.10: Random Forest (RF) parameters involved in the optimization procedure, with a short description and range of possible values sampled by the Latin Hypercube Sampling algorithm.

RF parameter

Short description

Range

n_estimators

number of trees in the forest.

100-500

max_features

maximum number of features (predictors) considered for splitting a tree node.

0.1-1 (1 meaning all available features)

max_depth

maximum number of levels in each decision tree.

1-100

min_samples_split

minimum number of data points placed in a node before the node is split.

2-30

min_samples_leaf

minimum number of data points allowed in a leaf node (terminal node of a tree).

2-30

bootstrap

method for sampling data points (with or without replacement).

True/False

The parameters optimization has been tested with two different metrics: the Nash-Sutcliffe Efficiency (NSE), and a combined metric. In particular, the latter includes the normalized NSE (NNSE), which indicates a general goodness of fit to the observations, and the Normalized Mean Absolute Error (MAE) of Annual Maxima, which quantifies the ability of the model to reproduce high extremes of generation

...

Anchor
Figure2_32
Figure2_32

Image Modified


Image Modified

Image Modified

Image Modified

Image Modified

Figure 2.32: maps of the LOYO validation results obtained in terms of NSE over the period of available data which depends on the source (TSO: 2010-2022, TP: 2015-2022, PECDv3.1: 2010-2017). The four panels each refer to a different inflow (or generation) indicator, as reported in the panels’ titles.

...

Table 2.11: Multiplicative Correction Factors applied to inflows model output.

Region

Technology

Correction Factor

Source

AT00

HRI – inflows to reservoirs

2404/5507

Comparison of mean maximum generation with an internal APG data source with strict sharing limitations.

HRR – inflows to run of river

23082/17760

HPI – inflows to pondage

5607/4506

CH00

HOL – inflows to open-loop pumped storage

0.825

Comparison of mean annual cumulated inflows with a reference monthly dataset derived from Swiss Federal Office of Energy (SFOE) data.

HRR – inflows to run of river

1.39

Comparison of mean annual cumulated inflows with a reference monthly dataset (SFOE). Mind: this factor was applied directly to the model input TSO data in accordance with the Swiss TSO.

TR00

HRR – inflows to run of river
(and relative IC series)

2.502

Comparison of mean annual cumulated inflow with an internal series of annual cumulated generation for period 2019-2023 including all country plants.

HRI – inflows to reservoirs
(and relative IC series)

1.850

Comparison of mean annual cumulated inflow with an internal series of annual cumulated generation for period 2019-2023 including all country plants.

Summary Table

The following table (Table 2.12) includes all addressed bidding zones and technologies (except for generation from run-of-river and pondage which would be a repetition of the respective reported inflow columns) and can be used to check the availability of data, source of data used for the modelling, and comments on the results mainly addressing inconsistencies found or considerations made for the source/modelling choices. As mentioned, the TSO generation data have always been given priority when available, followed by TP data and PECDv3.1 estimates. Given the different data sources and methodology used, the results can significantly differ from the ones of the previous PECD, therefore we strongly recommend checking with TSOs about the reliability of mean generation/inflow historical values.

...

Table 2.12: Summary table of used data sources and comments/considerations on the model outputs results.


Reservoirs Generation

Inflow to Reservoirs

run-of-river Inflow

Inflow to Open Loop PS

Pondage Inflow

Bidding zone / Tech.

HRG

HRI

HRO

HOL

HPO

AL00

TSO rescaled using monthly IC

TSO rescaled using monthly IC

TSO rescaled using monthly IC



AT00

TSO

TP – the mean using PECDv3.1 data is too low with respect to TSO data, hence using TP data although SE is surely affected by HPS (Hydro Pumped Storage)

TSO

PECDv3.1

TSO

BA00

TSO

PECDv3.1

PECDv3.1- TSO run-of-river data not provided – might be already accounted for in TSO pondage data

PECDv3.1

TSO

BE00



TSO



BG00

TSO

TSO

TSO

TSO


CH00

TSO

TSO – rescaled using monthly IC

TSO - rescaled using monthly IC – multiplication factor of 1.39 applied to generation input data in accordance with CH00 TSO

TSO - rescaled using monthly IC


CZ00

TSO

PECDv3.1

TP (since there’s no pondage) – can reproduce mean signal, can’t well reproduce the peaks – suspected anthropic factors influencing the production after 2019

PECDv3.1


DE00

TSO

PECDv3.1 – mean too low with respect to TSO generation, should be ca three times higher

TSO

PECDv3.1


ES00

TSO

TSO

TSO

TSO


FI00

TSO

TSO

TP (no TSO pondage data, no PECDv3.1 pondage data)



FR00

TP

TP – HPS (pumped storage) IC about 60% of HRE (reservoirs) IC in past 8 years (from TP data) + time series very close to PECDv3.1 inflow

TP (no TSO data for FR, no pondage in PECDv3.1 data)

GPU (Generation Per Unit) - (no PECDv3.1 data for FR) - low reliability: no HOL storage energy available (approximated inflow assuming negligible storage from one week to the other) + few production and pumping data (3 years)


GR00

TSO

TSO

TSO – model training on last 4 years (missing monthly IC data to rescale) – significant difference with PECDv3.1 inflow

TSO

PECDv3.1 – even though no pondage data from TSO nor TP

HR00

TSO – very close to TP generation

TP – HPS IC about 20% of HRE IC in the past 9 years (TP data)

TSO – could contain pondage

PECDv3.1

PECDv3.1 – even though no pondage data from TSO.

HU00



TSO rescaled using monthly IC



IE00



TSO



ITCA

TSO

PECDv3.1 – reasonable values with respect to TSO generation

TSO



ITCN

TSO

PECDv3.1 – inflow sometimes lower than TSO generation

TSO



ITCS

TSO

PECDv3.1 – inflow very close to TSO generation

TSO

PECDv3.1


ITN1

TSO

PECDv3.1 – inflow very close to TSO generation

TSO

 PECDv3.1


ITS1

TSO

PECDv3.1 – inflow close to generation (would expect it a bit higher)




ITSA

TSO

PECDv3.1 – high with respect to TSO generation

TSO



ITSI

TSO

PECDv3.1 – low peaks with respect to TSO generation

TSO

PECDv3.1


LT00



TSO – generation values exceptionally high for the year 2015 (something wrong in the data) -> left out of training



LV00





TSO

LU00



TSO



ME00

TSO – close to tp generation data, higher peaks

TP – no HPS IC

PECDv3.1



MK00

TSO

TSO




NL00



 PECDv3.1



NOM1

TSO

TP – small HPS production compared to HRE

TSO

PECDv3.1


NON1

TSO

TP - no HPS

TSO



NOS1

TSO

TP – no HPS

TSO

-


NOS2

TSO

TP – trying splitting PECDv3.1 NOS0 data obtained similar result + small HPS production

TSO

PECDv3.1 (splitting PECDv3.1 NOS0 data according to mean TSO generation data for NOS2)


NOS3

TSO

TP - trying splitting PECDv3.1 NOS0 data obtained similar result + small HPS production

TSO

PECDv3.1 (splitting PECDv3.1 NOS0 data according to mean TSO generation data for NOS3)


PL00

TSO

PECDv3.1 – mean inflow value is 3-4 times higher than TSO generation (also TP-calculated mean is 3-4 times higher)

TSO - rescaled using monthly IC

PECDv3.1 – inflow seems to be too low considering TSO generation and pumping series: ca 200 MWh of inflow against 1200 MWh of generation (mean weekly values)


PT00

TSO

TSO

TSO – values seem low, tp and PECDv3.1 data ca 10 times higher than TSO data of run-of-river and HPO together

TSO - rescaled using monthly IC

TSO

RO00

TSO

PECDv3.1

TSO

PECDv3.1


RS00

TSO

PECDv3.1 – TP data significantly impacted by HPS

TSO



SE01

TSO

PECDv3.1




SE02

TSO

PECDv3.1




SE03

TSO

PECDv3.1




SE04

TSO

PECDv3.1




SI00

TSO

-

TSO – could contain pondage


PECDv3.1 – no pondage generation data from TSO: keeping PECDv3.1 trained estimates. Pondage could be included in run-of-river TSO data? In this case PECDv3.1 estimates are off.

SK00

TSO

PECDv3.1 – although mean is considerably higher than TSO generation

TSO

PECDv3.1

TSO

TR00






UK00



TP – (no TSO data for GB, no pondage in PECDv3.1 data)





Energy indicators
Anchor
Section2_10
Section2_10

...

Table 2.13: Energy indicators provided in the PECDv4.1 for the historical stream. Files provided at ORIG spatial aggregation are gridded (NetCDF format), while all the other levels of aggregation are provided in CSV format.

VariableTypeTime periodSourceDomain/ spatial resolutionTemporal resolutionSpatial aggregationTechnologyUnits
Wind power onshore (WON)Capacity factor1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyPEONOnshore Existing technologies, Onshore SP199_HH100, Onshore SP199_HH150, Onshore SP199_HH200, Onshore SP277_HH100, Onshore SP277_HH150, Onshore SP277_HH200, Onshore SP335_HH100, Onshore SP335_HH150, Onshore SP335_HH200

MW/MW

Wind power offshore (WOF)Capacity factor1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyPEOFOffshore Existing technologies, Offshore SP316_HH155, Offshore SP370_HH155


MW/MW
Solar generation (SPV)Capacity factor1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyORIG, NUT0, NUT2, SZON, PEON---MW/MWp
Concentrated solar generation (CSP)Capacity factor1980 - 2021ERA5 reanalysisPECD/0.25° x 0.25°hourlyPEONstorage_0_hours_preDispatch, storage_0_hours_storageDispatched, storage_7p5_hours_preDispatch, storage_7p5_hours_storageDispatchedMW/MW
Hydropower reservoirs generation energy (HRG)

Energy

1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower reservoirs inflow energy (HRI)Energy1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river generation energy (HRO)Energy1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river inflow energy (HRR)Energy1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river with pondage generation energy (HPO)Energy1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river with pondage inflow energy (HPI)Energy1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower open-loop pumped storage inflow energy (HOL)Energy1980 - 2021

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh

*Energy data from ENTSO-E Transparency Platform

...

Table 3.1: CMIP6 climate models. Models are categorized as follows: models that do not provide all scenarios (highlighted in dark red), models with ECS outside the observed climate sensitivity range estimated in AR6 (highlighted in orange), and models that share components with other models (highlighted in yellow). The selection is made from the non-highlighted models.


Model 

ECS (°C) 

Pre-industrial 

Historical   

SSP1-1.9 

SSP1-2.6 

SSP2-4.5 

SSP3-7.0 

SSP5-8.5 

1 

ACCESS-CM2 

4.72 

500 

3 


1 

1 

1 

3 

2 

ACCESS-ESM1-5 

3.87 

900 

10 


3 

3 

3 

10 

3 

AWI-CM-1-1-MR 

3.16 

500 

5 


1 

1 

5 

1 

4 

BCC-CSM2-MR 

3.04 

600 

3 


1 

1 

1 

1 

  

BCC-ESM1 

3.26 


3 






5 

CAMS-CSM1-0 

2.29 

500 

2 

x 

2 

2 

2 

2 

6 

CanESM5 

5.62 

1000 

40 

x 

40 

40 

40 

40 

7 

CanESM5-CanOE 


501 

3 


3 

3 

3 

3 

8 

CESM2 

5.16 

1200 

11 


3 

3 

3 

3 

9 

CESM2-FV2 

5.14 

500 

3 


XXXX 

XXXX 

XXXX 

XXXX 

10 

CESM2-WACCM 

4.75 

499 

3 


1 

3 

1 

3 

11 

CESM2-WACCM-FV2 

4.79 

500 

3 


XXXX 

XXXX 

XXXX 

XXXX 

12 

CMCC-CM2-SR5 

3.52 

500 

1 


1 

1 

1 

1 

  

CMCC-ESM2 


500 

1 


1 

1 

1 

1 

13 

CNRM-CM6-1 

4.83 

500 

30 


6 

6 

6 

6 

  

CNRM-CM6-1-HR 

4.28 

XXXX 

1 


1 

1 

1 

1 

14 

CNRM-ESM2-1 

4.76 

500 

9 

x 

5 

5 

5 

5 

15 

E3SM-1-0 

5.32 

500 

5 


XXXX 

XXXX 

XXXX 

XXXX 

  

E3SM-1-1-ECA 


XXXX 

1 


XXXX 

XXXX 

XXXX 

XXXX 

  

E3SM-1-1 


XXXX 

1 


XXXX 

XXXX 

XXXX 

1 

  

EC-Earth3 

4.10 

XXXX 

23 

x 

7 

22 

7 

7 

16 

EC-Earth3-Veg 

4.31 

500 

5 

x 

4 

5 

4 

4 

  

EC-Earth3-Veg-LR 


XXXX 

1 

x 

XXXX 

XXXX 

XXXX 

1 

17 

FGOALS-f3-L 

3.00 

561 

3 


1 

1 

1 

1 

18 

FGOALS-g3 

2.88 

700 

6 

x 

1 

1 

1 

4 

  

FIO-ESM-2-0 


XXXX 

3 


3 

3 

XXXX 

3 

19 

GFDL-CM4 

3.89 

500 

1 


XXXX 

1 

XXXX 

1 

20 

GFDL-ESM4 

2.60 

500 

3 

x 

1 

3 

1 

1 

21 

GISS-E2-1-G 

2.72 

851 

39 

x 

2 

15 

2 

7 

  

GISS-E2-1-G-CC 


XXXX 

1 


XXXX 

XXXX 

XXXX 

XXXX 

  

GISS-E2-1-H 

3.11 

XXXX 

1 

x 

XXXX 

XXXX 

XXXX 

XXXX 

22 

HadGEM3-GC31-LL 

5.55 

500 

4 


1 

1 

XXXX 

3 

23 

HadGEM3-GC31-MM 

5.42 

500 

2 


1 

XXXX 

XXXX 

3 

24 

INM-CM4-8 

1.83 

531 

1 


1 

1 

1 

1 

25 

INM-CM5-0 

1.92 

1201 

10 


1 

1 

5 

1 

26 

IPSL-CM6A-LR 

4.56 

1200 

32 

x 

6 

11 

11 

6 

  

KACE-1-0-G 

4.48 

XXXX 

3 


2 

3 

3 

3 

27 

MCM-UA-1-0 

3.65 

500 

2 


1 

1 

1 

1 

28 

MIROC-ES2L 

2.68 

500 

10 

x 

2 

1 

1 

10 

29 

MIROC6 

2.61 

800 

10 

x 

3 

3 

3 

3 

30 

MPI-ESM-1-2-HAM 

2.96 

780 

2 


XXXX 

XXXX 

XXXX 

XXXX 

31 

MPI-ESM1-2-HR 

2.98 

500 

10 


2 

2 

10 

2 

32 

MPI-ESM1-2-LR 

3.00 

1000 

10 


10 

10 

10 

10 

33 

MRI-ESM2-0 

3.15 

701 

5 

x 

1 

1 

5 

2 

34 

NESM3 

4.72 

500 

5 


2 

2 

XXXX 

2 

35 

NorCPM1 

3.05 

500 

30 


XXXX 

XXXX 

XXXX 

XXXX 

  

NorESM2-LM 

2.54 

XXXX 

3 


1 

3 

1 

1 

36 

NorESM2-MM 

2.50 

500 

1 


1 

1 

1 

1 

37 

SAM0-UNICON 

3.72 

700 

1 


XXXX 

XXXX 

XXXX 

XXXX 

38 

TaiESM1 

4.31 

500 

1 


1 

1 

1 

1 

39 

UKESM1-0-LL 

5.34 

1100 

17 

x 

5 

5 

5 

5 


This list was further refined by considering additional criteria, specifically the availability of sufficient temporal resolution, with a minimum requirement of 3-hourly data, and horizontal spatial resolution, with a minimum requirement of 100 km. These criteria ensure the data is sufficiently detailed for further processing and analysis.

...

Table 3.2: CMIP6 models considered for PECDv4.1 under the projections stream.

Model

Time resolution

Spatial resolution

Simulations

Variant label

Calendar

CMCC-CM2-SR5

3 hours

100 km

historical, ssp245

r1i1p1f1

365_day

EC-Earth3

3 hours

100 km

historical, ssp245

r1i1p1f1

proleptic_gregorian

MPI-ESM1-2-HR

3 hours

100 km

historical, ssp245

r1i1p1f1

proleptic_gregorian

Note that the historical simulation period is chosen to ensure overlap between ERA5 and the CMIP6 models, enabling the computation of bias adjustment.

...

Table 3.3: CMIP6 models used in the projections stream and their corresponding nodes for downloading.

Model

Originator

Model code

node URL

CMCC-CM2-SR5

CMCC (Centro Euro-Mediterraneo sui Cambiamenti Climatici)

CMR5

https://esgf-data.dkrz.de/esg-search

EC-Earth3

ECEC (European community Earth System Model)

ECE3

https://esg-dn1.nsc.liu.se/esg-search

MPI-ESM1-2-HR

MPI- (Max Planck Institute)

MEHR

https://esgf-data.dkrz.de/esg-search

Footnotes Display


Spatial interpolation
Anchor
Section3_3
Section3_3

...

Table 3.4: Temporal interpolation methodologies.

VariableInterpolation method
Temperature (TA)(1) Cubic spline with moving window (window width 3 days)
Precipitation (TP)(3) Cumulating over the days
Solar Radiation at Surface (GHI)(2) Method ad hoc for taking into account the position of the sun
Wind speed at 10 m (WS10)(1) Cubic spline with moving window (window width 3 days) apply separately at the 10 m horizontal components of wind)

The cubic spline interpolation is implemented in a Python script that uses the xarray library. The set of files is opened in an xarray.mfdataset (multi-file dataset), and an iterator runs along the "time" coordinates of the 3-hourly file on a daily step starting from 00:00 hours. In each step, a window with a width of 3 days is created, and the data within the window are interpolated to an hourly resolution for each grid point by combining the xarray methods resample("1h") and interpolate("cubic"). The interpolated data for the central day (from 00:00 to 23:00) are then stored in a new dataset and saved as a NetCDF file.

...

Anchor
Table3_5
Table3_5

Table 3.5: Climate indicators provided in the PECDv4.1 for the projection stream. Files provided at BIAS spatial aggregation are gridded (NetCDF format), while all the other levels of aggregation are provided in CSV format.

VariablePeriodSourceModelsScenarioDomain/ spatial resolutionTemporal resolutionSpatial aggregationUnits
2m temperature (TA)2015-2065CMIP6 projectionsCMR5, ECE3, MEHRSP245, SP370PECD/0.25° x 0.25°hourlyBIAS, NUT0, NUT2, SZON, SZOF, PEON, PEOF

K (gridded)

°C (aggregated)

Population-weighted temperature (TAW)2015-2065CMIP6 projectionsCMR5, ECE3, MEHRSP245, SP370PECD/0.25° x 0.25°hourlySZON°C
Total precipitation (TP)2015-2065CMIP6 projectionsCMR5, ECE3, MEHRSP245, SP370PECD/0.25° x 0.25°hourlyBIAS, NUT0, NUT2, SZON, SZOF, PEON, PEOFm
Surface solar radiation downwards (GHI)2015-2065CMIP6 projectionsCMR5, ECE3, MEHRSP245, SP370PECD/0.25° x 0.25°hourlyBIAS, NUT0, NUT2, SZON, SZOF, PEON, PEOFW m-2
10m wind speed (WS10)2015-2065CMIP6 projectionsCMR5, ECE3, MEHRSP245, SP370PECD/0.25° x 0.25°hourlyBIAS, BIAS, NUT0, NUT2, SZON, SZOF, PEON, PEOFm s-1
100m wind speed (WS100)2015-2065CMIP6 projectionsCMR5, ECE3, MEHRSP245, SP370PECD/0.25° x 0.25°hourlyBIAS, NUT0, NUT2, SZON, SZOF, PEON, PEOFm s-1

  

Energy data

The same data illustrated in Section 2.7 are also used for the projection stream. 

...

Anchor
Table3_6
Table3_6

Table 3.6: Wind run types for projection stream.

Run type

Climate projection simulated years

WPP locations

WPP technology

Losses

Existing

2015-2065

All years with 2020 WPP locations (based on WindPowerNet data)

Existing WPP parameters based on WindPowerNet data (always 2020 fleet), applied in the generic power curve model

Wakes as part of the generic power curve. And 10 % for other losses (incl. unavailability), applied as a simple multiplication by 0.9

Future wind technologies

2015-2065

The best 10-50 % locations of the unmasked points within each PECD region (in terms of mean wind speed in the bias-adjusted ERA5 data, based on ERA5 grid).

Onshore wind: 3 hub heights and 3 turbine types, so in total 9 wind technologies. A plant of 50 MW with ten 5 MW turbines modelled for each technology.

Offshore wind: 1 hub height and 2 turbine types, so in total 2 wind technologies. A plant of 500 MW with 28 18 MW turbines modelled for each technology.

Wakes as part of power curves. And 5 % for other losses (incl. unavailability), applied as a simple multiplication by 0.95


Photovoltaic Solar Power conversion model

...

Anchor
Table3_7
Table3_7

Table 3.7: Energy indicators provided in the PECDv4.1 for the projection stream. Files provided at ORIG spatial aggregation are gridded (NetCDF format), while all the other levels of aggregation are provided in CSV format.

VariableTypePeriodSourceDomain/ spatial resolutionTemporal resolutionSpatial aggregationTechnologyUnits
Wind power onshore (WON)Capacity factor2015 - 2065CMIP6 projectionPECD/0.25° x 0.25°hourlyPEONOnshore Existing technologies, Onshore SP199_HH100, Onshore SP199_HH150, Onshore SP199_HH200, Onshore SP277_HH100, Onshore SP277_HH150, Onshore SP277_HH200, Onshore SP335_HH100, Onshore SP335_HH150, Onshore SP335_HH200

MW/MW

Wind power offshore (WOF)Capacity factor2015 - 2065CMIP6 projectionPECD/0.25° x 0.25°hourlyPEOFOffshore Existing technologies, Offshore SP316_HH155, Offshore SP370_HH155


MW/MW
Solar generation (SPV)Capacity factor2015 - 2065CMIP6 projectionPECD/0.25° x 0.25°hourlyORIG, NUT0, NUT2, SZON, PEON---MW/MWp
Concentrated solar generation (CSP)Capacity factor2015 - 2065CMIP6 projectionPECD/0.25° x 0.25°hourlyPEONstorage_0_hours_preDispatch, storage_0_hours_storageDispatched, storage_7p5_hours_preDispatch, storage_7p5_hours_storageDispatchedMW/MW
Hydropower reservoirs generation energy (HRG)

Energy

2015 - 2065

CMIP6 projection

ENTSO-E TP*

TSO**

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower reservoirs inflow energy (HRI)Energy2015 - 2065

CMIP6 projection

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river generation energy (HRO)Energy2015 - 2065

CMIP6 projection

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river inflow energy (HRR)Energy2015 - 2065

ERA5 reanalysis

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river with pondage generation energy (HPO)Energy2015 - 2065

CMIP6 projection

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower run-of-river with pondage inflow energy (HPI)Energy2015 - 2065

CMIP6 projection

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh
Hydropower open-loop pumped storage inflow energy (HOL)Energy2015 - 2065

CMIP6 projection

ENTSO-E TP*

TSO**

PECDv3.1***

PECD/0.25° x 0.25°weeklySZON---MWh

*Energy data from ENTSO-E Transparency Platform

...

Anchor
Table4_1
Table4_1

Table 4.1: Filename convention used in the PECDv4.1.

Position in the filename

Possible substrings for each position in the filename

Description

Option in the CDS download form

0

H (historical), P (projection)

Data streams

Stream

1

ERA5 (ERA5 reanalysis), CMI6 (CMIP6 Projection)

Model

Origin (Reanalysis or Climate models)

2

ECMW (ECMWF), CMCC (Centro Euro-Mediterraneo sui Cambiamenti Climatici), ECEC (European community Earth System Model), MPI- (Max Planck Institute)

Model

Origin (Reanalysis or Climate models)

3

T639 (ERA5 data), CMR5 (CMCC-CM2-SR5 r1i1p1f1), ECE3 (EC-Earth3 r1i1p1f1), MEHR (MPI-ESM1-2-HR r1i1p1f1)

Model

Origin (Reanalysis or Climate models)

4


TA- (2m temperature), TAW (Population-weighted temperature), TP- (Total precipitation), GHI (Surface solar radiation downwards), WS- (10m wind speed and 100m wind speed)

Variable

Variable (Climate)

SPV (Solar generation capacity factor), CSP (Concentrated solar generation capacity factor), WON (Wind power onshores capacity factor), WOF (Wind power offshores capacity factor), HOL (Hydropower open-loop pumped storage inflow energy), HPI (Hydropower run-of-river with pondage inflow energy), HPO (Hydropower run-of-river with pondage generation energy), HRG (Hydropower reservoirs generation energy), HRI (Hydropower reservoirs inflow energy), HRO (Hydropower run-of-river generation energy), HRR (Hydropower run-of-river inflow energy)

Variable

Variable (Energy)

5

0000m, 0002m, 0010m, 0100m

Level (meters above sea level)

Not applicable

6

Pecd (ENTSO-E PECD domain)

Region

Not applicable

7

025d (0.25°), NUT0 (NUTS 0), NUT2 (NUTS 2), PEOF (Pan-European Offshore Zones), PEON (Pan-European Onshore Zones), SZOF (Offshore Bidding Zones), SZON (Onshore Bidding Zones)

Spatial resolution

Gridded

Regional aggregated timeseries

8

SYYYYMMDDhhmm (starting year, month, day, hour, minute)

Start date

Year

Month

9

EYYYYMMDDhhmm (ending year, month, day, hour, minute)

End date

Year

Month

10

ACC (accumulated), INS (Instantaneous), CFR (Capacity factor), NRG (Energy)

Type

Not applicable

11

MAP (gridded data), TIM (time series)


Not applicable

12

01h (1 hour), 01d (1 day), 07d (7 days)

Temporal resolution

Not applicable

13

NA-

Lead time

Not applicable

14

noc (no correction), cdf (Cumulative distribution fn), mbc (mean bias correction)

Bias adjustment method

Not applicable

15

NA-, org (original data), avg (mean)

Statistics

Not applicable

16

NA, 20 (Offshore wind turbine: Existing technologies), 21 (Offshore wind turbine: SP316 HH155), 22 (Offshore wind turbine: SP370 HH155), 30 (Onshore wind turbine: Existing technologies), 31 (Onshore wind turbine: SP199 HH100), 32 (Onshore wind turbine: SP199 HH150), 33 (Onshore wind turbine: SP199 HH200), 34 (Onshore wind turbine: SP277 HH100), 35 (Onshore wind turbine: SP277 HH150), 36 (Onshore wind turbine: SP277 HH200), 37 (Onshore wind turbine: SP335 HH100), 38 (Onshore wind turbine: SP335 HH150), 39 (Onshore wind turbine: SP335 HH200), 40 (Concentrated solar power: Pre-dispatch, no storage), 41 (Concentrated solar power: Dispatched, no storage), 42 (Concentrated solar power: Pre-dispatch, 7-hours of storage), 43 (Concentrated solar power: Dispatched, 7-hours of storage)

Technological specification

Technological specification (Offshore wind turbine, Onshore wind turbine, Concentrated solar power)

17

NA---, SP245 (ssp 245)

Emission scenario

Emissions

18

NA---

Energy scenario

Not applicable

19

NA---, StRnF (Statistical model/Random Forests), PhM01 (Physical Model/method1), PhM02 (Physical Model/method2), PhM03 (Physical Model/method3)

Transfer function

Not applicable

20

PECD4.1

Version of PECD database

Not applicable

21

fv1

File version

Not applicable

22

.nc (NetCDF)

.csv (comma-separated values)

File formats

Not applicable

Example of filename: H_ERA5_ECMW_T639_TP-_0000m_Pecd_025d_S198501010000_E198501310000_ACC_MAP_01d_NA-_noc_org_NA_NA---_NA---_NA—_PECD4.1_fv1.nc

...