a multi-objective optimization framework for the design of offshore wind farms

1 Background

2 Multi-ObjectiveOptimizationFramework

3 Case Study

4 Conclusions

A Multi-Objective OptimizationFramework for the Design of

Offshore Wind Farms

Sılvio Rodrigues

DCE&S GroupDelft University of Technology

Delft, The [email protected]

June 10, 2016

1 Background


3 Case Study

4 Conclusions

Outline

Background

Multi-Objective Optimization Framework

Case Study

Conclusions

1 Background


3 Case Study

4 Conclusions

Background

<33.00 - 3.253.25 - 3.503.50 - 3.753.75 - 4.004.00 - 4.254.25 - 4.504.50 - 4.754.75 - 5.005.00 - 5.255.25 - 5.505.50 - 5.755.75 - 6.006.00 - 6.256.25 - 6.506.50 - 6.756.75 - 7.007.00 - 7.257.25 - 7.507.50 - 7.757.75 - 8.008.00 - 8.258.25 - 8.508.50 - 8.758.75 - 9.009.00 - 9.259.25 - 9.509.50 - 9.759.75 - 10.00> 10.00

Mean annual wind speed

[m/s]

3 / 26

1 Background


3 Case Study

4 Conclusions

Background

3 / 26

1 Background


3 Case Study

4 Conclusions

Background

1990 1995 2000 2005 2010 20150123456789

1011

Commission year

Ca

pa

city [

GW

]

5 2 5 17 3 15 50 160 273 90 90 201 210 149 577

1087

245

1166

2118

1296

3198

Yearly commissioned capacity [MW]

Cumulative commissioned capacity

mk 050 0040030020010100

10.0 ° W 7.5 ° W 5.0 ° W 2.5 ° W 0.0 ° 2.5 ° E 5.0 ° E 7.5° E 10.0° E 12.5° E 15.0° E 17.5° E

50.0 ° N

52.5 ° N

55.0 ° N

57.5 ° N

60.0 ° N

I 2006

I ≈ 1 GW

I Denmark 1st,UK 2nd

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1 Background


3 Case Study

4 Conclusions

Background

1990 1995 2000 2005 2010 20150123456789

1011

Commission year

Ca

pa

city [

GW

]

5 2 5 17 3 15 50 160 273 90 90 201 210 149 577

1087

245

1166

2118

1296

3198

Yearly commissioned capacity [MW]

Cumulative commissioned capacity

mk 050 0040030020010100

10.0 ° W 7.5 ° W 5.0 ° W 2.5 ° W 0.0 ° 2.5 ° E 5.0 ° E 7.5° E 10.0° E 12.5° E 15.0° E 17.5° E

50.0 ° N

52.5 ° N

55.0 ° N

57.5 ° N

60.0 ° N

I 2015

I ≈ 11 GW

I UK 1st,Germany andthen Denmark

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1 Background


3 Case Study

4 Conclusions

Background

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 20500

20

40

60

80

100

120

140

160

180

200

Commission year

Offshore

capacity [G

W]

Commissioned capacityMinimum growth (8.3%)EWEA growth 2030 (19.1%)EWEA growth 2020 (29.6%)Average growth (36.1%)

6 / 26

1 Background


3 Case Study

4 Conclusions

The challenge

TurbinesOnshore substationMeteorological mastArray cable connectionsExport cable routeOnshore cable route

Lolland

Sweden

Norway

Denmark

Germany

Netherlands

0 1 km

Conwy

Llandudno

Colwyn BayLlanddulas

Abergele

Rhyl

Prestatyn

Mostyn

Turbines

Onshore substationMeteorological mastArray cable connectionsExport cable routeOnshore cable routeProject areaTurbine area

O�shore substations

0 10 km

7 / 26

1 Background


3 Case Study

4 Conclusions

The challenge

I Different technologies have to be assessed

CR

Support Structures

Grounded

Gravitybased

Bucket Monopile Tripod Tripile Twistedjacket

Lattice

Jacket IDEOL WindFloat

Buoyancy

WINFLO

Mooringline

BlueH TLP

Floating PelaStar Advanced Hywind

Ballast

Floating

Haliade Spar

I The problem is too complex to be tackled at oncewith current design techniques

8 / 26

1 Background


3 Case Study

4 Conclusions

Current Optimization Frameworks

Single-

Objective

Optimizer

Wind farm

designer

Final wind

farm layout

Economic

assumptions

Optimization and decision phases

Database

High-level

constraints

AED

CAPEX

OPEX

NPV = (AED · pMWh − OPEX ) a− CAPEX

9 / 26

1 Background


3 Case Study

4 Conclusions

Trade-offs

Energy production [MWh]

Investment

cost [€]

NPV = (AED · pMWh − OPEX ) a− CAPEX

10 / 26

1 Background


3 Case Study

4 Conclusions

Objectives

Optimize

Integrate Automate

11 / 26

1 Background


3 Case Study

4 Conclusions

Proposed Multi-Obj. Framework

Optimized trade-offs

Multi-

Objective

Optimizer

AED

OPEX

CAPEXDatabase

Optimized wind

farm layout

Wind farm

designer

End

design?

Final wind

farm layout

Economic

assumptions

High-level

constraints

Yes

No

Optimization phase

Decision phase

12 / 26

1 Background


3 Case Study

4 Conclusions

Wind farm components

a

b

cd

e

f

g

13 / 26

1 Background


3 Case Study

4 Conclusions

Dutch Offshore Wind Farms

I

IIIII

IV "

"

h h

e

f

g

g

i

i

10-mile zone≈ 18.5 km

12-mile zone≈ 22 km

Zones:a. Borssele I-II: 2015, 700 MW III-IV: 2016, 700 MWb. Hollandse kust: Zuid-Holland Tender 1: 2017, 700 MW Tender 2: 2018, 700 MWc. Hollandse kust: Noord-Holland Tender 1: 2019, 700 MWd. Egmond aan Zee (2006, 108 MW)e. Prinses Amalia (2008, 120 MW)f. Luchterduinen (2015, 129 MW)g. Gemini (under construction, 600 MW)h. Reserved areas for future plansi. Areas within 10 NM

h

h

h

h

h

a

b

cd

CommissionedUnder constructionUpcoming tendersFuture projects

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1 Background


3 Case Study

4 Conclusions

Borssele Offshore Wind Farm

I

IIIII

IV

E

N-E

N

N-W

W

S-W

S

S-E

4.0

7.8

11.7

15.5

19.4

[2.0 : 4.0[[4.0 : 6.0[[6.0 : 9.0[[9.0 : 12.0[[12.0 : 15.0[[15.0 : 18.0[[18.0 : inf[

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1 Background


3 Case Study

4 Conclusions

Standard layout - 8MW turbines

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

NPV = 1 bn eHH = 858 000

LCOE = 90.2 e/MWhDPT = 13.75 years

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1 Background


3 Case Study

4 Conclusions

Optimized Trade-off

0 1000 2000 3000 4000 5000 6000 7000 8000AED [GWh]

0

2

4

6

8

10

12

CAPE

X [b

n EU

R]

MVac 66 kVHVdc 320 kVHVac 220 kVStd 8 MWStd 5 MW

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1 Background


3 Case Study

4 Conclusions

Net Present Value

0 1000 2000 3000 4000 5000 6000 7000 8000AED [GWh]

3

2

1

0

1

2

NPV

[bn

EUR]

Standard layouts do not present the best economic values!

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1 Background


3 Case Study

4 Conclusions

Net Present Value

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

NPV = 1.73 bn eHH = 1 780 000


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1 Background


3 Case Study

4 Conclusions

Sensitivity of the NPV witheconomic factorsNPV = (AED · pMWh − OPEX ) a− CAPEX

Scenarionr.

Lifetime[years]

Interestrate [%]

Energy price[EUR/MWh]

NPV[bn EUR]

HH

1 20 7 124 1.73 1 780 000

0 1000 2000 3000 4000 5000 6000 7000 8000AED [GWh]

6

5

4

3

2

1

0

1

2

3

NPV

[bn

EUR]

Scenario 1Scenario 2Scenario 3Scenario 4Best layouts

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1 Background


3 Case Study

4 Conclusions

Sensitivity of the NPV witheconomic factors

Scenarionr.

Lifetime[years]

Interestrate [%]


NPV[bn EUR]

HH

1 20 7 124 1.73 1 780 0002 20 7 100 0.29 911 000

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

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1 Background


3 Case Study

4 Conclusions


Scenarionr.

Lifetime[years]

Interestrate [%]


NPV[bn EUR]

HH

1 20 7 124 1.73 1 780 0002 20 7 100 0.29 911 0003 20 12 124 0.097 491 000

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

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1 Background


3 Case Study

4 Conclusions


Scenarionr.

Lifetime[years]

Interestrate [%]


NPV[bn EUR]

HH

1 20 7 124 1.73 1 780 0002 20 7 100 0.29 911 0003 20 12 124 0.097 491 0004 25 7 124 2.47 2 085 000

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

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1 Background


3 Case Study

4 Conclusions

LCOE, COP, IRR, DPT, ROI

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

NPV = 0.28 bn eHH = 205 000


24 / 26

1 Background


3 Case Study

4 Conclusions

Highest Energy Delivered

480 485 490 495 500 505 510 515 520x [km]

5710

5715

5720

5725

5730

5735

5740

y [k

m]

40.0

37.5

35.0

32.5

30.0

27.5

25.0

22.5

20.0

NPV = -2.86 bn eHH = 2 200 000


25 / 26

1 Background


3 Case Study

4 Conclusions

Conclusions

I Review of the existing optimization approaches

I Only single-objective optimizationI Do not cover all revelant design aspects

I Multi-Objective Optimization FrameworkI Integrated, automated and optimized designs

I Models tailored for this problem

I MMC, cables, transformers, collection system design

I Choosing the final layout is a difficult task!

I Presenting the trade-offs to the designers

I Cost savings during planning and operation phases!

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a multi-objective optimization framework for the design of offshore wind farms

Engineering