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Setting up a Long-Term European Electricity System Model Incorporating Climate Change Effects

EERA Energy System Integration Workshop, DTU Lyngby, Denmark

3rd November 2016

Fabian Gotzens, M.Sc. PhD candidate

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03/11/2016 Fabian Gotzens | IEK-STE, FZ Jülich, Germany 2

Contents

Motivation

Overview of Employed Models

Details of Model Coupling

Scenario Background

Preliminary Results

Outlook

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03/11/2016 Fabian Gotzens | IEK-STE, FZ Jülich, Germany 3

Motivation

Expansion of RES develops differently according to country-specific goals and RES potentials2

Climate change might influence RES potentials + the related political goals

Policy advice is needed for the required fundamental change in energy systems

1 [EU, 2016] 2 [IRENA, 2016]

The EU sets itself ambitious energy and climate targets1 until 2030

≥ - 40% greenhouse gas emissions compared with 1990

≥ 27% of total energy consumption from renewable energy sources (RES)

≥ 27% increase in energy efficiency

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Overview of Employed Models

existing, running existing, work in progress planned

Climate Effects

Integration

Model

European

electricity

system model

German energy

system model

IKARUS

German regional

electricity

system model

Cross-border electricity exchanges

Convergence Criterion: Match of Capacity Expansion

Capacity Expansion Constraints

Aggregated effects at country-specific level

Aggregated effects at regional level

How, when and where might climate change effects

impact the European electricity system?

Main Research

Question:

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European Electricity System Model

TIMES Model Paradigm LP capacity expansion model

Regions European countries

Years 2010–2050

Resolution 4 seasons, 2 weekdays, 24 hrs = 192 time slices per year

Economical Parameters - Fuel Prices

- CO2 Prices

- Technology investment costs

- Fixed + variable O&M costs

Policy Parameters

- CO2 reduction targets

- RES expansion goals

Technical Parameters - Power plant database

(technologies, fuels, efficiencies decommission pathways, …)

- RES geographical potentials

- RES temporal availability factors

- Cross-border transmission capacities

- CO2 emission factors

- Demands per sector

2010

2015

2020

2025

2030

2035

2040

2045

2050

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03/11/2016 Fabian Gotzens | IEK-STE, FZ Jülich, Germany 6

German Spatial Electricity System Model

TIMES Model Paradigm LP capacity expansion model

Regions German Regions

Years 2010–2050

Resolution 4 seasons, 2 weekdays, 24 hrs = 192 time slices per year

Economical Parameters - Fuel Prices

- CO2 Prices

- Technology investment costs

- Fixed + variable O&M costs

Policy Parameters

- CO2 reduction targets

- RES expansion goals

Technical Parameters - Power plant database

(technologies, fuels, efficiencies decommission pathways, …)

- RES geographical potentials

- RES temporal availability factors

- Cross-border transmission capacities

- CO2 emission factors

- Demands per sector

2010

2015

2020

2025

2030

2035

2040

2045

2050

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Climate Effects Integration Model

• Spatial resolution 12-km pattern

• Temporal resolution 3h

• Time Horizon 2100 Solar Irradiation

• Spatial resolution 12-km pattern

• Temporal resolution 3h

• Time Horizon 2100

• Height 10 m

Near-surface windspeeds

EURO-CORDEX

Climate Data Model

- Spatial allocation of cell

pattern to countries

- Temporal disaggregation

into hourly values

- Adjustment of wind

heights via

- surface roughness

- turbine curves

- clustering

Final derivation of full load hours for wind and solar power for each region and year Possible integration of data from [Tobin et al., 2015] Planned cooperation with Institute of Geophysics and Meterology, Univ. Cologne

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050

100150200250300350400450500

0 4 8 121620 0 4 8 121620 0 4 8 121620 0 4 8 121620

R S F W

Sup

ply

[M

W]

Season / Hour of Day

Average Wind Offshore Feed-Ins 2010-2015

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20

R S F W

Season / Hour of Day

Average Wind Onshore Feed-Ins 2010-2015

Ø TransnetBW

Ø Amprion

Ø 50Hertz

Ø TenneT

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Pre-analysis of Historical RES Feed-Ins

Average offshore wind feed-ins do not show a clear intra-day temporal dependence

Interesting: Average onshore wind feed-ins show o midday peaks in spring + summer o rather flat patterns in fall + winter

Spring Summer Fall Winter Spring Summer Fall Winter

Are these patterns subject to climate change? If yes, how is the impact on the electricity supply system?

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German energy system model IKARUS*

Primary energy Energy conversion and transport Final consumption

Decentral CHP

Renewable energies

Nuclear fuel

Gas

Electricity

District heat

Coal

Crude oil

Fuel oils

Gasoline

Diesel & kerosene

Central CHP

Production

Housing space

Number of employees

Freight and passenger transport

Demand for raw

materials

Demand

Power plants

Transport/ distribution/

storage

Transport/ distribution/

storage

Coal import

Coal extraction

Gas import

Gas extraction

Electricity import

Nuclear fuel import

Renewable energy sources

Crude oil import

Import of other oils

Refinery

Industry

Non-energy

consump-

tion

Households

Transport

sector

Small

consumer

Transport/ distribution/

storage

Model type: Techno-economic bottom-up optimization model of the German energy system Objective function: minimizing of total system costs Model philosophy: myopic (no perfect forsight) Time horizon: until 2050 (in 5 years intervalls)

* IK

AR

US:

Inst

rum

ents

for

gree

nh

ou

se g

as r

edu

ctio

n s

trat

egie

s

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IKARUS German Energy System Model

Inputs Outputs

Technologies (Capacities, Costs, Parameters)

Additional inst. electric capacities

Residential sector demands (bn. m² to be heated)

Levelized Cost of Electricity (LCOE)

Transport sector demands (pkm / tkm)

Electricity Demand DE

Industrial gross value added (bn. €)

etc.

Small consumer demands (mio. employees)

Energy carrier prices

Bounds: RE potentials

Bounds: Inst. electric capacities

Bounds: Im- and Exports

Detailled View into Model Coupling

10

TIMES European Electricity System Model

Inputs Outputs

Technologies DE Technologies Others

Additional electric capacities DE Additional electric capacities Oth.

Electricity Demand DE Levelized Cost of Electricity DE Levelized Cost of Electricity Oth.

Electricity Demand Others Electricity: Im- and Exports DE Electricity: Im- and Exports Oth.

Energy carrier prices

Bounds: RE Potentials DE Bounds: RE Potentials Others

etc.

Bounds: Inst. electric capacities

Bounds: Im and Exports (initially free - not bounded)

Convergence criterion:

Δ ≤ ±5%

by 2nd iteration

If violated

Fixed model inputs Data exchange Convergence Criterion

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Scenario Background

• Approach: Comparison with reference scenario

Consistent scenarios for European and German model needed

• Sensitivity case study: Autarky aspects

• Which countries will be able to supply energy self-sufficiently?

• For those how could, what would be the additional costs per country?

• Which countries will –due to limited potentials– still rely on energy imports?

2010

2015

2020

2025

2030

2035

2040

2045

2050

Reference Case Usage and capacity expansion only subject to national constraints

Climate Change Cases Usage and capacity expansion additionally restricted by climate change impacts

Comparison

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Prelimary Exemplary Modeling Results

0

1

2

3

4

5

6

7

8

1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721

FE FI RE RI SE SI WE WI

Ele

ctri

city

Ge

ne

rati

on

[TW

h]

Biogas CHP Coal CHP Waste CHP Biogas Run-off-River Hydro Storage

Lignite Combined Cycle Syn. Gases Wind offshore Wind onshore Waste

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03/11/2016 Fabian Gotzens | IEK-STE, FZ Jülich, Germany 13

Finishing Model Approach and its Application

Outlook and Next Steps

Development of the climate data integration model

Modeling of further European countries

Integration of storage devices

Integration of grid features

Open Questions for Discussion

Adequate modeling of the future role of biogas?

...optimal temporal resolution across scales?

...decrease calculation time of double soft-coupling?

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References Slide Source

3 EU 2030 Energy & Climate Framework http://ec.europa.eu/clima/policies/strategies/2030/index_en.htm Renewable Expansion Data IRENA (2016), Renewable Energy Statistics 2016, The International Renewable Energy Agency, Abu Dhabi

5,6 ETSAP TIMES Loulou et al. (2005), Documentation for the TIMES Model

7 Tobin et al. (2016), Climate change impacts on the power generation potential of a European mid-century wind farms scenario

8 Wind Feed-In Data Germany http://www.50hertz.com/de/Kennzahlen/Windenergie/Archiv-Windenergie http://www.amprion.net/windenergieeinspeisung http://www.tennettso.de/site/Transparenz/veroeffentlichungen/netzkennzahlen/tatsaechliche-und-prognostizierte-windenergieeinspeisung https://www.transnetbw.de/de/kennzahlen/erneuerbare-energien/windenergie Picture TSOs https://upload.wikimedia.org/wikipedia/commons/thumb/1/17/Regelzonen_deutscher_%C3%9Cbertragungsnetzbetreiber_neu.png/200px-Regelzonen_deutscher_%C3%9Cbertragungsnetzbetreiber_neu.png

9 Heinrichs et al. (2015), IKARUS – a German energy system model, IEK-STE, FZ Jülich

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Setting up a Long-Term European Electricity System Model Incorporating Climate Change Effects

EERA Energy System Integration Workshop, DTU Lyngby, Denmark

3rd November 2016

Fabian Gotzens, M.Sc. PhD candidate