implications of limited foresight on wind park …iea-etsap.org/workshop/london_april2016/1...

Implications of Limited Foresight on Wind Park Investments in Norway

wholeSEM (28/04-2016)

Arne Lind & Eva Rosenberg

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Overview

2

Introduction

• Motivation

Methodology

• TIMES-Norway

• NET-Model

Scenarios and assumptions

• Model variations

• Demand variations

• Energy prices

Model results

Concluding remarks

Part 1: Introduction

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Motivation

3

Test the important of the perfect foresight assumption in the following

energy system models:

• TIMES-Norway

• NET-Model

In reality, decision makers do not act with full information about the

future

• In practice: A limited horizon is used for their decision making

• However, the future is predictable to some extent

From a market point of view, it is not reasonable to use a perfect

foresight planning horizon

• More realistic to use a planning horizon equal to the planning horizon of

investment decisions


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Principle

4

Limited

foresight

w/ overlap

For

esig

htIn

form

atio

n

Perfect foresight

Demand, technology costs, energy prices, taxes, etc


Time horizon

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5

Methodology

Part 2: Methodology

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Modelling framework:

TIMES-Norway

6

Bottom-up, techno-economic optimisation model describing the Norwegian energy system

• Optional extension: Hard-linked version including Sweden

High time resolution

Model horizon from 2010 to 2050

Covers five Norwegian (and four Swedish) regions

Exchange of electricity between regions and neighbouring countries

Assumes perfect competition and perfect foresight and is demand driven

Energy demand is exogenous inputMore information: A. Lind et al. / Energy Policy 60 (2013) 364–377

Part 2: Methodology

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Limited foresight – Implementation in

TIMES

7

Variable amount of years optimised in each solution step

The total model horizon will be solved by successive steps

• For each step the decision variables are defined only for a time subset

• In each step the periods to be optimised are advanced further in the future

• All periods before them are fixed to the solution of the previous step

The amount of overlapping years between successive steps can be

controlled by the user

• More realistic to use a planning horizon equal to the planning horizon of

investment decisions

The final (optimal) solution consists of accumulated variables

• Combination of results from each solution step

Part 2: Methodology

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8

Scenarios and assumptions

Part 3: Scenarios and assumptions

v

vMain assumptions

Energy prices constant 2016-2050 (purchased

energy)

Present tax policy

• Energy taxes 2014, constant until 2050

• Biodiesel tax = fossil diesel after 2020

• Zero emission cars exempted from

purchase tax and VAT until 2020

• Purchase tax for vehicles based on CO2-

emissions,

power and weight until 2050

Other:

• Discount rate 4 % (higher for some

demand technologies)

• Enova support programs until 2020

• Common green certificate market

• Direct electric heating restricted as in TEK10

(building regulation)

9

0 100 200 300 400 500

SE2, SE3, FIN, RUS

DK

DE

SE1

NL

UK

Power export price (NOK2005/MWh)

Annaul average power price

2016-2050

2010-2014

Part 3: Scenarios and assumptions

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Demand alternatives

• REF CenSES reference demand

projections

• DEM High industry activity

• PRI Increasing energy prices

Model alternatives

• Perfect foresight = No timestep

• Limited foresight I = 10 years timestep with 5

years overlap

• Limited foresight II = 5 years timestep with 2

years overlap

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ScenariosPart 3: Scenarios and assumptions

Model altern

atives

Demand alternatives

Time step

REF REF REF REF DEM DEM DEM DEM PRI PRI PRI PRI

No

Global rate 4% 10% 4% 4% 4% 10% 4% 4% 4% 10% 4% 4%

Certificate price

NOK/MWh 165 165 200 120 165 165 200 120 165 165 200 120

10 yrs

Global rate 4% 10% 4% 4% 4% 10% 4% 4% 4% 10% 4% 4%

Certificate price

NOK/MWh 165 165 200 120 165 165 200 120 165 165 200 120

5 yrs

Global rate 4% 10% 4% 4% 4% 10% 4% 4% 4% 10% 4% 4%

Certificate price

NOK/MWh 165 165 200 120 165 165 200 120 165 165 200 120

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Analyses

Part 4: Analyses

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Total power production (2030)

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140

145

150

155

160

165

170

175

180

185

190

REF DEM PRI

[TW

h]

PF TS10_5O TS5_2O 4% discount rate

Certificate price = 165

NOK/MWh

Part 4: Analyses

2.7 TWh

5.1 TWh

11 TWh

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140

145

150

155

160

165

170

175

180

185

190

REF DEM PRI

[TW

h]

PF TS10_5O TS5_2O

Total power production (2030)

Part 4: Analyses

10% discount rate

Certificate price = 165

NOK/MWh

0.1 TWh0.2 TWh

6.3 TWh

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Wind power production (2030)

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0

5

10

15

20

25

30

35

REF DEM PRI REF DEM PRI REF DEM PRI REF DEM PRI

165 NOK/MWh 165 NOK/MWh 200 NOK/MWh 120 NOK/MWh

4% rate 10 % rate 4% rate 4% rate

[TW

h]

PF TS10_5O TS5_2O

Part 4: Analyses

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0

5

10

15

20

25

30

35




[TW

h]

PF TS10_5O TS5_2O

Part 4: Analyses

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0

5

10

15

20

25

30

35




[TW

h]

PF TS10_5O TS5_2O

Part 4: Analyses

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0

5

10

15

20

25

30

35




[TW

h]

PF TS10_5O TS5_2O

Part 4: Analyses

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Electricity consumption

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-

20

40

60

80

100

120

140

160

1975 2000 2025 2050

Elec

tric

ity

use

(TW

h/y

ear)

Statistics

REF

DEM

PRI

Part 4: Analyses

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Electricity consumption

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100

105

110

115

120

125

130

135

140

145

150

1975 2000 2025 2050

Elec

tric

ity

use

(TW

h/y

ear)

Statistics

REF

DEM

PRI

Part 4: Analyses

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Net power export (2030)

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Part 4: Analyses

-10

0

10

20

30

40

50




[TW

h]

PF TS10_5O TS5_2O

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Concluding remarks

Part 5: Conclusion

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Concluding remarks

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For predictive scenarios (forecasts):• Small effects of using limited foresight in TIMES-Norway

For explorative (external) or predictive (what if) scenarios:• Occasionally large effects of using limited foresight in TIMES-Norway

The effects of limited foresight is reduced with a higher discount rate

The model results show that limited foresight had larger effect on the production side than on the end-use side of the energy system:• Typical features of the production side:

Large, expensive investments

Long technological lifetime

Examples: Wind power, hydropower and export connections

• Typical features of the end-use side: Small, “inexpensive” investments

Shorter technological lifetime

Constant demand -> Change of technology (and energy carrier) possible

Significant reduced computational time• Over 80% for certain scenarios

Part 5: Conclusion

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Thank you!

Arne Lind, PhD

Senior research scientist

Institute for Energy Technology

2027 Kjeller, Norway

e-mail: [email protected]

Part 5: Conclusion

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