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Interdisciplinary approach of material demand analysis and econometrics

EFFECTS OF INCREASING WIND ENERGY

SHARE IN THE GERMAN ELECTRICITY SECTOR

ON THE EUROPEAN STEEL MARKET

Shivenes Shammugam1, Estelle Gervais1,

Andreas Rathgeber2, Thomas Schlegl1

1Fraunhofer-Institute for Solar Energy Systems

ISE

2Institute of Materials Resource Management

MRM, University of Augsburg

15th IAEE European Conference 2017 Vienna,

6th of September 2017

www.ise.fraunhofer.de

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Introduction and Motivation

Increasing share of RE-Technologies

German government set targets for CO2-reduction and RE-share

80% RE-share by 2050, 80-95% CO2-reduction by 2050

0 200 400 600 800

REMax

THG-80

Regio

THG95

1.a

2015

RE

Mod-

80

UB

AB

MU

12

SR

U

Sta

tu

s-

quo

Cumulative installed capacity [GWel]

En

erg

y s

ce

na

rio

s fo

r 2

05

0

Wind Onshore

Wind Offshore

PV

Hydropower

Hard-coal

Lignite

Nuclear

Net Import

Gasturbine

Combined Cycle

Micro CHP

Large CHP

Geothermie

Increasing demand of raw materials due to deployment of RE-Technologies might lead

to supply bottlenecks or price instabilities

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Aim of the study

An interdisciplinary approach

Interdisciplinary approach

Raw material demand analysis + Econometric methods

Estimation of

material demand

in an technology

Vector Auto

Regression

(VAR) Model

Impulse

Response

Functions (IRF)

This approach is exemplarily tested on the steel demand due to deployment of wind

turbines in Germany until 2050

• Annual material

demand

• Price elasticity of

supply and demand

• Short and long term

price changes and

fluctuations

• Impact of demand

shocks

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Methodology I

Estimation of steel demand in wind turbines

As-built material requirements

Composition current WT

• LCA

• Reports and

sheets from

Manufacturers

Composition future WT

• Empirical models

(upscaling methods)

• Techno-

economical

optimized models

Roadmaps

• Installed capacity

• Average rotor diameter

• Average rated power

• Towers market shares

• Foundations market

shares

• Drive trains market

shares

Additional material consumptions

Production losses

• Metal scrap rates

Maintenance

• Statistical

failure rates

• Weibull

distribution

Repowerings

• Weibull distribution

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Methodology I

Scenarios for generator market shares

Upscaling scenario (On- and Offshore)

Continuous trend towards larger

plants

Increased share of PM generators

Conservative scenario (Onshore)

Stagnant trend towards larger

turbines

Low dynamics in technology market

shares

Year

Ma

rke

t sh

are

[%

]

0

20

40

60

80

100

2014 2015 2016 2020 2030 2040 2050

Conservative

0

20

40

60

80

100

Upscaling

EESG-DD DFIG SCIG FC PMSG-MS PMSG-DD PMSG-HS

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Results I

Estimation of steel demand

Largest steel demand in 2050: 5,900 kt in 2050

Steel production in Germany in 2015: 42,000 kt

Bottleneck in steel supply is highly unlikely

0

1.000

2.000

3.000

4.000

5.000

6.000

2015 2020 2025 2030 2035 2040 2045 2050

An

nu

al s

teel

dem

and

[kt

]

REMod_Conservative REMod_Upscaling GP_Conservative GP_Upscaling

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Methodology II

Vector Auto Regression (VAR) Model

Annual data from 1968 to 2013

Unit root tests: ADF-test and KPSS-test

Optimal lag length via Akaike and Bayesian information criteria

𝑌𝑡 = 𝑎0 +

𝑖=1

𝑙𝑎𝑔

𝑏𝑖𝑌𝑡−𝑖 + +𝜀𝑡

𝑌𝑡 vector of length n

𝑎0 [n ×1] vector of constants

𝑏𝑖 [n × n] coefficient matrices

𝑖 order of the model

𝜀𝑡 matrix of residuals

Micro Variables Macro Variables

Steel price Gross domestic product GDP

Apparent consumption Consumer price index CPI

Steel production Yields on debt securities outstanding

Export of iron ore Oil price: West Texas Intermediate (WTI)

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Methodology II

Impulse Response Functions (IRF)

IRF shows how the system reacts to specific isolated shocks

Two cases are tested

Case 1: No additional demand shocks

Case 2: Annual steel demand as annual shocks in in IRF

Confidence bands constructed via Monte Carlo method of 1000 simulation runs for a

period of 17 years until 2030

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Results II

Development of steel price

CAGR of steel price in the past 30a: 2.5% p.a.

In the first case, CAGR of steel price: 2.4 % p.a.

In the second case, CAGR of steel price increases

by 0.4 %

Steel production remains at 0.1 % in both cases

Total steel demand increases at 2.9 % p.a.

Total steel demand increases at a much higher rate than the additional steel demand due to wind turbine

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Conclusions and further works

Conclusions

Approach to combine raw material demand analysis of an energy technology and

econometric methods in order to analyze the effects of an increasing share of

renewable energies on the raw material price is successfully tested

The additional steel demand from wind turbine are absorbed by the model, thus

limiting the impact on the price.

Further works

Expansion of the VAR-Model and selection of variables via Granger-Causality test

Application of method on demand of other materials and technology / energy

system

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References

SRU 1.a - Wege zur 100 % erneuerbaren Stromversorgung,

Sondergutachten; Sachverständigenrat für Umweltfragen; 2011

REMod – Energiesystem Deutschland 2050; Fraunhofer ISE; 2013

BMU12 - Langfristszenarien und Strategien für den Ausbau der erneuerbaren

Energien in Deutschland bei Berücksichtigung der Entwicklung in Europa und

global; DLR, Fraunhofer IWES, ifne; 2012

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Thank you for your attention!

Fraunhofer-Institut für Solare Energiesysteme ISE

Institute of Materials Resource Management MRM, University of Augsburg

Shivenes Shammugam, Estelle Gervais, Andreas Rathgeber, Thomas Schlegl

www.ise.fraunhofer.de

shivenes.shammugam@ise.fraunhofer.de

*this work was supported by the Reiner Lemoine Stiftung

© Fraunhofer ISE

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Methodology I

Component of wind turbines and drive train systems

Rotor

Tower

Steel

Hybrid

Foundation

Jacket

Monopile

Tripod

Floating

Onshore

Nacelle

Drive train system

Drive traintype

Synchronous

Electrical

Direct-Drive

Full scale

Permanent Magnet

Direct-Drive

Full scale

Gearbox

Single-stage

Full scale

Multiple-stage

Full scale

Induction

Electrical

Gearbox

Multiple-stage

Partial scale

Full Scale

EESG-

DD

PMSG

-DD

PMSG

-MS

PMSG

-HSDFIG SCIG