Modelling Work Packageiea-etsap.org/workshop/madrid_nov2016/12-Meinke-Hubeny_ESTMA… · IER...

Energy Storage Mapping And Planning

Modelling Work Package Vito/EnergyVille Frank Meinke-Hubeny Larissa Pupo Nogueira de Oliveira Jan Duerinck IER Stuttgart Markus Blesl Julia Welsch

ETSAP Workshop CIEMAT – Madrid 17.11.2016

Overview and Background ESTMAP project Frank Meinke-Hubeny, Vito/EnergyVille Evaluation of the role of energy storages in Europe with TIMES PanEU Markus Blesl and Julia Welsch, IER University of Stuttgart Analysis of the role of energy storages in Germany with TIMES PanEU Julia Welsch and Markus Blesl, IER University of Stuttgart Analysis of the role of energy storages in Belgium and Netherlands with TIMES Larissa P. N. de Oliveira and Jan Duerinck; Vito/EnergyVille Overview of Powerfys Model results (Ecofys) Frank Meinke-Hubeny, Vito/EnergyVille Discussion

GRAND ENERGY CHALLENGES

• Establish a clean, low carbon energy system based on renewable resources

• Ensure continuation of stable and high quality energy services

• Keep energy generation cost efficient and affordable for all EU citizens

ENERGY STORAGE IS A KEY ENABLER

• Flexibility for an energy system in which electricity generation increases

• Mitigation of consequences from increasing share of intermittent energy sources

• Solutions for declining base load and shift to decentralized generation

Energy Storage Mapping And Planning

Key knowledge and information on Europe’s energy storage potential

Spatial energy storage database for electricity, gas and heat technologies

Case demonstration of European energy systems analysis and planning

Contribute to Energy Storage development

TANKS

LAKES

RESERVOIRS

SALT

HOST ROCK

AQUIFERS

MODULAR

ESTMAP DATA SCOPE

PUMPED HYDRO STORAGE

NATURAL GAS STORAGE HYDROGEN STORAGE

HYDROGEN STORAGE NATURAL GAS STORAGE

COMPRESSED AIR ENERGY STORAGE

NATURAL GAS STORAGE THERMAL ENERGY STORAGE

COMPRESSED AIR ENERGY STORAGE UNDERGROUND PUMPED HYDRO STORAGE

BATTERIES FLYWHEEL

CAPACITATORS LNG HYDROGEN STORAGE

UNDERGROUND THERMAL ENERGY STORAGE

Reservoirs Technologies Subsurface / Above ground Existing / Potential Electricity, Gas, Heat

Subsurface data collection

Above ground data collection (PHS)

Geographical energy storage database > 4200 potential and proven natural energy storage capacities

> 700 planned and developed energy storage facilities Number of potential storage sites

Schematic overview of interrelations in Modelling WP

Flow of information for energy systems analysis and planning

DBase

ESTMAP Geographical

database Salt Formation

depth

height

area

Select potentially suitable reservoirs for analysis input

Storage site and reservoirs database

reservoir characteristics

Salt Formation

Define notional storage facilities per technology

Site characterization Feasibility determination Reservoir properties

Generic technical design parameters Site-specific performance parameters

Analysis input deck

Future potential capacities + Proven capacities in existing facilities

intake discharge efficiency capex opex etc.

GIS, TIMES and PowerFys have been combined to demonstrate potential analysis on ESTMAP database

Database GIS mapping TIMES model PowerFys model

Description

• Compile a database with existing and future potential energy storage

• Integrate contributions from geological and technical institutes and open source information

• EU

• Calculate connection costs for future storage facilities

• Develop storage maps depicting analysis results, after TIMES and PowerFys model runs

TIMES PanEU: • Optimize

configuration of storage sites & power plants

• Time resolution of day, night and peak time slices

• EU-28, NO, CH • 2010 – 2050 TIMES regional: • Time resolution of

280 (GER) and 60 (BE & NL) time slices

• DE, BE & NL

• Optimize operation of energy storage and power generation assets

• Optimize storage use

• Assess cross-border electricity flow & congestion

• Calculate marginal energy costs

• Hourly resolution • DE, BE and NL • 2050

Outcomes

• Storage locations • Storage

specifications

• Storage connection costs

• Optimal configuration of storage sites and power plants

• Hourly storage use • Generation mix • Marginal costs

Scenario Definitions

2030 2050 Combined binding

emissions target (2030)

Renewable target 2030 Combined emissions

target (2050) Renewable target

2050*

% vs. 1990 % gross final energy consumption % vs. 1990 % gross final energy

consumption EU -40% 27% -80% 75%*

Sources: (COM, 2013 (169)) and (COM, (2011) 885) Note: * based on 'High Renewable Energy Sources (RES)' scenario, Roadmap 2050

Baseline Scenario

PV Scenario • Predefined PV generation capacity : 50% higher compared to the baseline results in 2050

BattCost Scenario Baseline Scenario BattCost Scenario Investment costs Investment costs 2010 2050 2010 2050

Battery Lithium Ion Input 100 €𝑘𝑘

30 €𝑘𝑘

100 €𝑘𝑘

60 €𝑘𝑘

Battery Lithium Ion Storage 752 €𝑘𝑘𝑘

85 €𝑘𝑘𝑘

752 €𝑘𝑘𝑘

170 €𝑘𝑘𝑘

Battery Lithium Ion Output 100 €𝑘𝑘

30 €𝑘𝑘

100 €𝑘𝑘

60 €𝑘𝑘

General Assumption • Spirit of a true ‘Energy Union’ till 2050 • Guidance from EU policy H2020, Roadmaps 2030 and 2050

Further information about ESTMAP … • Vito, IER • Project Flyer • Website (http://estmap.eu)

Frank Meinke-Hubeny [email protected] Larissa Pupo Nogueira de Oliveira [email protected] Jan Duerinck [email protected]

Overview and Background ESTMAP project Frank Meinke-Hubeny, Vito/EnergyVille Evaluation of the role of energy storages in Europe with TIMES PanEU Markus Blesl and Julia Welsch, IER University of Stuttgart Analysis of the role of energy storages in Germany with TIMES PanEU Julia Welsch and Markus Blesl, IER University of Stuttgart Analysis of the role of energy storages in Belgium and Netherlands with TIMES Larissa P. N. de Oliveira and Jan Duerinck; Vito/EnergyVille Overview of Powerfys Model results (Ecofys) Frank Meinke-Hubeny, Vito/EnergyVille Discussion

Belgium and Netherlands TIMES Model -

Methodology: Temporal resolution

Jan Duerinck

Belgium and Netherlands TIMES Model - Methodology and Results

Larissa Pupo Nogueira de Oliveira

Methodology

Main Structure for Storage Processes

BE & NL: disaggregated approach DE & PanEU: aggregated approach ~FI_ProcessSets Region TechName TechDesc Tact Tcap Tslvl

*Process Set MembershipRegion Name

Technology Name Technology Description

Activity Unit

Capacity Unit

TimeSlice level of Process Activity

.ELE.CEN.TCH.STGTSS. BE STGSPH1L1 FFAC_PHS_1LAKE_001 - Storage PJa PJa DAYNITE

.ELE.CEN.TCH.STGTSS. NL STGSLCSA245 FFAC_LCCAES_SALT_245 - Storage PJa PJa DAYNITE




.ELE.CEN.TCH.STGTSS. NL STGSLCSA249 FFAC_LCCAES_SALT_249 - Storage PJa PJa DAYNITE….PRE.CEN.TCH.STGTSS. NL STGSUGRE307 FFAC_UGS_RES_307 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGRE308 FFAC_UGS_RES_308 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGRE309 FFAC_UGS_RES_309 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGRE310 FFAC_UGS_RES_310 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGRE311 FFAC_UGS_RES_311 - Storage PJa PJa DAYNITE….PRE.CEN.TCH.STGTSS. NL STGSUGSA245 FFAC_UGS_SALT_245 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGSA246 FFAC_UGS_SALT_246 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGSA247 FFAC_UGS_SALT_247 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSUGSA248 FFAC_UGS_SALT_248 - Storage PJa PJa DAYNITE….PRE.CEN.TCH.STGTSS. NL STGSH2SA245 FFAC_H2_SALT_245 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSH2SA246 FFAC_H2_SALT_246 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSH2SA247 FFAC_H2_SALT_247 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSH2SA248 FFAC_H2_SALT_248 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSH2SA249 FFAC_H2_SALT_249 - Storage PJa PJa DAYNITE.PRE.CEN.TCH.STGTSS. NL STGSH2SA250 FFAC_H2_SALT_250 - Storage PJa PJa DAYNITE

~FI_ProcessSets TechName TechDesc Tact Tcap Tslvl.ELE.CEN.TCH.STGTSS. EUSTGPSN01 Pump Storage PJ PJa Daynite.ELE.CEN.TCH.STGTSS. EUSTGPSN01_S Pump Storage Seasonal PJ PJa Season.ELE.CEN.TCH.STGTSS. EUSTGCAESADIA01 CAES adiaba (salt caverns) PJ PJa Daynite.ELE.CEN.TCH.STGTSS. EUSTGCAESDIA01 CAES diabat (salt caverns) PJ PJa Daynite.ELE.CEN.TCH.STGTSS. EUSTGCAESADIA01_O CAES adiabat (tanks) PJ GW Daynite

-Database can be used with both approaches! - In addition to the exercise of evaluating regions with different dimensions and potentials.

Methodology

Storage Potential Database Pump Storage – one reservoir Pump Storage – two reservoir

Country Potential [GWh]

Connecting Cost � €

𝒌𝒌𝒌𝒌� Country Potential

[GWh] Connecting

Cost � €𝒌𝒌𝒌𝒌�

AT 409 6,2 IE 30 5,9 BE 0 - IT 1.626 6,7 BG 378 6,9 LT 0 - CH 0 - LU 0 - CY 51 5,7 LV 0 - CZ 183 6,5 MT 0 - DE 297 6 NL 0 - DK 0 - NO 6.616 23,5 EE 0 - PL 47 5 ES 0 - PT 1.229 4,4 FI 104 8 RO 0 - FR 1.913 5,2 SE 1.098 11,6 GR 288 11,6 SI 18 5,6 HR 291 7,5 SK 0 - HU 3 5,5 UK 1.702 8,4




[GWh] Connecting


AT 16 6,2 IE 0 - BE 0 - IT 86 7,7 BG 0 - LT 0 - CH 0 - LU 0 - CY 0 - LV 0 - CZ 3 6 MT 0 - DE 5 15,2 NL 0 - DK 0 - NO 212 11,7 EE 0 - PL 0 - ES 0 - PT 28 5,6 FI 0 - RO 0 - FR 49 2,9 SE 0 - GR 0 - SI 0 - HR 0 - SK 0 - HU 0 - UK 85 4

Methodology

Storage Potential Database Compressed Air Natural Gas




[GWh] Connecting


BG 5,4 6 NL 30 7,7 DE 575 7,9 PL 3 16,6 DK 32 8,6 GR 19 21,7 RO 22 8,4

Country Potential reservoir

[million m3]

Potential cavern

[million m3] Country

Potential reservoir

[million m3]

Potential cavern [million

m3] AT 9.264 0 GR 0,002 4.000 BG 0 1.460 IT 8.863 0 DE 172.000 221.000 HU 29.011 0 DK 0 12.000 NL 65.250 12.000 DE 0 0 PL 2.000 4.000 DK 0 0 RO 3.000 8.000 HR 25 0 SI 306 0 UK 34 0,004

Optimized electricity output of power plants in Belgium

-20

0

20

40

60

80

100

120

Stat

istic

s

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

TWh

Net Electricity Electricity Storage (excl. Pump Hydro) Net imports Others / Waste non-ren. Hydrogen Other Renewables Biomass / Waste ren. Solar Wind offshore Wind Onshore Hydro (incl. Pump Storage) Nuclear Gas CCS Gas w/o CCS Oil Lignite CCS Lignite w/o CCS Coal CCS Coal w/o CCS

• Major transition in the years from 2020 to 2030, during phase out of nuclear generation • Nuclear phase out is compensated mainly through

• Increase in gas generation plant output, • Decline in energy demand and • Massive increase in net energy import from neighbouring countries

Optimized electricity capacity of power plants in Belgium

0

5

10

15

20

25

30

35

40

45

50

Stat

istic

s

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

GW

Capacity Electricity Storage (excl. Pump Storage)

Others / Waste non-ren.

Other Renewables

Biomass / Waste Ren.

Solar

Wind

Hydro (incl. Pump Storage)

Nuclear

Natural Gas

Oil

Lignite

Coal

• Overall capacity increases from 2030 to 2050 • Most growth can be attributed to the increasing wind generation capacity • ‘Imposed’ higher PV generation capacity (MorePV scenario) results in a reduction of 2 GW of

wind capacity (14 GW in baseline scenario to 12 GW in MorePV scenario) • Majority of additional PV capacity does not replace other RES capacity, but is added to

overall capacity

0123456789

10St

atist

ics

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

GWh

Storage Content

Pump Storage

Optimized amount and types of storage sites in Belgium

• New capacity in electrical storage output does not play a role in Belgium in the model results • Storage is limited to the existing pumped hydro storage with a steady 6.5 GWh of storage content

Optimized electricity output of power plants in Netherlands

-20

30

80

130

180

230

Stat

istic

s

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

TWh

Net Electricity Electricity Storage (excl. Pump Hydro) Net imports Others / Waste non-ren. Hydrogen Other Renewables Biomass / Waste ren. Solar Wind offshore Wind Onshore Hydro (incl. Pump Storage) Nuclear Gas CCS Gas w/o CCS Oil Lignite CCS Lignite w/o CCS Coal CCS Coal w/o CCS

• Netherlands largely differs from the Belgium - only 4 TWh originate from nuclear generation • Majority of generated by gas and coal plants in base year • Transition to a more RES based energy system starts with offshore wind energy, in later years

onshore wind and solar generation • Net transfer capacity from the year 2035 onwards (approx. 12 TWh in 2035,

increasing to 19-20 TWh in 2040, 21 TWh in 2045 approximately)

Optimized electricity capacity of power plants in Netherlands

0

10

20

30

40

50

60

70

80

90

100

Stat

istic

s

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

GW

Capacity

Electricity Storage (excl. Pump Storage)

Others / Waste non-ren.

Other Renewables

Biomass / Waste Ren.

Solar

Wind

Hydro (incl. Pump Storage)

Nuclear

Natural Gas

Oil

Lignite

Coal

• Transition to a more RES based energy system starts with • Growing share of offshore wind energy, • Onshore wind and solar generation in later years

Optimized amount and types of storage sites in Netherlands

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4St

atist

ics

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

GWh

Storage Content

Battery Redox Flow

Battery Lead Acid

Battery Lithium Ion

CAES Adiabatic

CAES Diabatic

Pump Storage

• Reliance on import goes hand in hand with no investments in storage capacities • Exception being results in the MorePV scenario

• Lithium-ion storage content in the year 2050 of 0.28 GWh

Optimized amount and types of storage sites in Netherlands

• Hydrogen storage is chosen in the Dutch context • For 2050 H2 storage of approximately 13 GWh in the Base and BattCost scenario • 18 GWh in the MorePV scenario • Potential sites for hydrogen storage are based on the ESTMAP database.

0

2

4

6

8

10

12

14

16

18

20

Stat

istic

s

Base

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

Base

Mor

ePV

Batt

eryC

ost

2010 2015 2020 2025 2030 2035 2040 2045 2050

GWh

H2 Storage Content

H2 Storage

Germany, Belgium & Netherlands Model - PowerFys dispatch model (Ecofys)

Frank Meinke-Hubeny

Schematic representation of the inputs and outputs of the PowerFys model

PowerFys – Adaptation of the load variation curve

Snapshot of the first week of January 2050, Germany

date

05-Jun 06-Jun 07-Jun 08-Jun

MW

10 4

-2

0

2

4

6

8

10

1220160720_Baseline_update_v2 - DE+NL+BE elektra

Storage releaseRE

Oil

OilCC

Coal

Lignite

Gas

GasCC

curt. RE

Storage fillingCurtailment

Load

PowerFys

Example of power dispatch for a three-day period in June

PowerFys

Destination of surplus renewables

% of total renewable generation

0 2 4 6 8 10 12 14 16 18 20

TOTAL

BE

NL

DE

20160720_Baseline_update_v2 - Surplus Renewables

avoided curtailment - to export

avoided curtailment - to storage

curtailment

TIMES analysis in the context of large scale energy storage Challenges / Critical self-reflection • Underestimation of LSES demand due to low time slice

resolution and ‘last moment investments (e.g. >2040) • Avoidance of ‘marginally expensive’ technologies, like

storage, due to ‘perfect foresight’ • Secondary business cases or ‘irrational behaviour’ only

implemented as exogenous input • Interaction with electricity market price difficult to

model • Transmission capacity investments are used in multi-

region models and compete (or replace?) storage in small countries (like Belgium)

TIMES analysis in the context of large scale energy storage Lessons learned • Various storage technologies play a role in the outcomes

– the mix is important and country-specific • Not a ‘one solution fits all’ result • ‘Competition’ among technologies play a key role

- see GER example • Need for a better understanding of the energy systems:

Power - Heat – Storage - Flexibility/DSM • Transmission capacity and willingness for an Energy

Union have a significant impact on small countries (BE) • Knowledge of current and future technologies is key

(solar, wind, storage, …): Technical, Economic, Potential aspects

Modelling Work Packageiea-etsap.org/workshop/madrid_nov2016/12-Meinke-Hubeny_ESTMA… · IER...

Documents

Transcript of Modelling Work Packageiea-etsap.org/workshop/madrid_nov2016/12-Meinke-Hubeny_ESTMA… · IER...