Positioning of Danish offshore wind farms until 2030- using Levelized Cost of Energy
Gyde Liane Ohlsen
Supervisors:
Niels-Erik Clausen & Asger Bech Abrahamsen
Risø, 7th of February 2019
M.Sc. Thesis:
Agenda
Motivation
Objective
Methodology
Input
Results
Conclusion
Outlook
Motivation
Danish energy agreement signed in June 2018:
→ Increase offshore wind energy capacity.
→ 3 new offshore wind farms until 2030 - each minimum 800 MW:
Grid connection covered by wind farm operator.
Possibly operated without subsidies.
1. Tender in 2019-2020, operating 2024-2027.
2. Tender in 2021, operating latest 2030.
3. Tender in 2023, operating latest 2030.
Identification of potential locations for offshore wind farms
Screening by the Danish Energy Agency (DEA).
Identification of economically attractive areas in the Danish Sea for future offshore wind energy projects.
Motivation MethodologyObjective Input OutlookResults Conclusion
3
Motivation
Pre-selected areas by the DEA1:
Motivation MethodologyObjective Input OutlookResults Conclusion
1 Danish Energy Agency {2018}: https://efkm.dk/aktuelt/nyheder/2018/sep/her-skal-danmarks-naeste-havvindmoellepark-ligge/ 4
Objective
1. Where in the Danish Sea is it most profitable to erect offshore wind farms?
▪ Using Levelized Cost of Energy (LCoE): Cost of one unit of produced energy (€/MWh).
▪ Methodology to compare LCoE of offshore wind energy projects across the Danish Sea, considering water depth, distance to shore and wind resources.
▪ Tool allowing a graphical visualization of LCoE through a colour map.
▪ Ranking of LCoE of the pre-selected areas comparison to COWI’s ranking.
2. How will potential future development of technology and cost affect the LCoE?
▪ Prediction of future cost development and technological improvements (wind turbine capacity, farm capacity and lifetime) until 2030.
3. Will future Danish offshore wind energy projects be able to operate without subsidies?
▪ Comparison of LCoE for offshore wind and electricity price prediction by DEA until 2030.
Motivation MethodologyObjective Input OutlookResults Conclusion
5
MethodologyInput parameters:
Motivation MethodologyObjective Input OutlookResults Conclusion
CAPEXWind turbineSubstructureInstallationElectrical equipment:
Inter-array cablesTransmission cablesOn- & offshore substation
OPEXFixed & variable O&MOTHERDiscount rateLifetime
Wind resourcesWater depthDistance to shoreWind turbine capacityWind farm capacityWind farm losses
Technical aspects Economical aspects
𝐶𝐴𝑃𝐸𝑋: Capital expenditure 𝑂𝑃𝐸𝑋: Operational expenditure𝑟: Discount rate𝐸: Energy production𝐿𝑇: Lifetime𝑡: Time
𝐿𝐶𝑜𝐸 =
σ𝑡=0𝐿𝑇 𝐶𝐴𝑃𝐸𝑋𝑡 + 𝑂𝑃𝐸𝑋𝑡
(1 + 𝑟)𝑡
σ𝑡=0𝐿𝑇 𝐸𝑡
(1 + 𝑟)𝑡
6
▪ Rectangular wind farm layout with turbine spacing of 7 𝑥 rotor diameter around pixel.
▪ Annual energy production (AEP) and water depth mean over wind farm area.
▪ LCoE value assigned to pixel.
Methodology
Motivation MethodologyObjective Input OutlookResults Conclusion
7
Wind resources & annual energy production
Motivation MethodologyObjective Input OutlookResults Conclusion
𝐴: Scale parameter𝑘: Shape parameter𝑣: Wind speed
AEP
𝑓(𝑣) =𝑘
𝐴∙𝑣
𝐴
𝑘−1
∙ 𝑒−
𝑣𝐴
𝑘
𝐴𝐸𝑃 = 𝑓 𝑣 ∙ 𝑃 𝑣 𝑑𝑣 [𝑀𝑊ℎ]
𝐴 and 𝑘 parameters:
▪ Global Wind Atlas data2.
▪ 500 𝑥 500 𝑚 resolution. ▪ Combined for all wind directions.
Wake losses 9 %
Availability losses 3 %
Electrical losses 1.5 %
Total losses 13 %
Considered AEP losses:
2 D. Heathfield {2018} from World in a Box, consultant to DTU Wind Energy on the Global Wind Atlas. 8
Water depth
Motivation MethodologyObjective Input OutlookResults Conclusion
Bathymetry data3 not considered: Water depth > 50 𝑚
Distance to shore < 10 𝑘𝑚3 EMODnet Bathymetry portal {2018}: http://portal.emodnet-bathymetry.eu/ 9
Wind turbine cost
Motivation MethodologyObjective Input OutlookResults Conclusion
Additional:
▪ Turbine installation rate5: 0.6𝑑𝑎𝑦𝑠
𝑀𝑊.
▪ Installation vessel day rate6: 150.000€
𝑑𝑎𝑦.
Assumed wind turbine cost development over time based on:▪ Past data obtained from IRENA study4. ▪ Prediction based on learning rate of
10 % (DEA).
4 IRENA {2017}: Renewable Power Generation Cost in 2017; 5 R. Lacal-Aránteguia, J. M. Yustab, J. A. Domínguez-Navarrob {2017}: Offshore wind installation: Analysingthe evidence behind improvements in installation time; 6 Ballast Nedam Offshore: Optimal integrated combination of foundation concept and installation method 10
Substructure cost
Motivation MethodologyObjective Input OutlookResults Conclusion
Cost function of monopile + transition piece developed by DTU7 in 2015.
Additional:
▪ Substructure installation rate5: 0.5𝑑𝑎𝑦𝑠
𝑀𝑊.
▪ Installation vessel day rate6: 150.000€
𝑑𝑎𝑦.
Assumed monopile cost development over time based on:
▪ Past data obtained from various studies.
▪ Prediction based on learning rate of 10 % (DEA).
7 T. Buhl, A. Natarajan {2015}: Level 0 cost models of offshore substructure. 11
Electrical equipment cost – Inter-array cable
Motivation MethodologyObjective Input OutlookResults Conclusion
𝑉 = 33 𝑘𝑉
𝐼𝑚𝑎𝑥 = 1050 𝐴 → 𝐴𝐶𝑢= 1000 𝑚𝑚2
Cable choice determines the wind farm layout.
Cost8: 615€2017
𝑚
8 A. G. Gonzalez-Rodriguez {2016}: Review of offshore wind farm components. 12
Electrical equipment cost - Transmission
Motivation MethodologyObjective Input OutlookResults Conclusion
Points of common coupling (PCC) 400 𝑘𝑉 9.
Costs:▪ Offshore and onshore substation.▪ Transmission cable:
Substation PCC.▪ No land cable.
HVAC vs. HVDC 10
Shorter distance.High cable cost.Low terminal cost.
𝑉𝐴𝐶 = 220 𝑘𝑉
Longer distance.Low cable cost.High terminal cost.
𝑉𝐷𝐶 = 300 𝑘𝑉
9 Energinet {2017}: https://en.energinet.dk/Electricity/Energy-data/System-data; 10 EX. Xiang, M. M. C. Merlin, T. C. Green {2016}: Cost Analysis and Comparison of HVAC, LFAC and HVDC for Offshore Wind Power Connection
13
Other costs
Motivation MethodologyObjective Input OutlookResults Conclusion
O&M, administration, monitoring etc.
OPEX as cost per MWh based on:
▪ Past data obtained from various studies.
▪ Prediction provided by DEA 11:→ 75% fixed and 25% variable cost.
10% of CAPEX.
Operational expenditures (OPEX)
11 Danish Energy Agency {2016-18}: Technology Data for Energy Plants for Electricity and District heating generation.
▪ Planning and development cost
▪ Engineering and permitting
▪ Insurances
14
Discount rate
Motivation MethodologyObjective Input OutlookResults Conclusion
Discount rate based on:▪ Calibration of discount rate through existing/planned Danish offshore wind farms:
Cost of Energy (CoE) LCoE given by Danish Wind Industry Association12.
▪ Data provided by COWI, DEA and IRENA.
Existing/planned wind farms:
12 DWIA {2016}: https://windpower.org/da/aktuelt/aktuelt_i_vindmoelleindustrien/news_q4_2016/kystnaere_havvindmoeller_fortsat_billigst.html
Wind farm Year of FID
Anholt 2011
Horns Rev 3 2016
Kriegers Flak 2018
Vesterhav Nord & Syd 2017
15
Levelized Cost of Energy (LCoE)
Motivation MethodologyObjective Input OutlookResults Conclusion
Using wind turbine Vestas V164-8.0MW. 16
LCoE distribution across Danish Sea
Motivation MethodologyObjective Input OutlookResults Conclusion
▪ 50% of Danish Sea 47 − 58 €/MWh.▪ < 5% below 50 €/MWh. ▪ 3% above 68 €/MWh.▪ Majority between 55 and 60 €/MWh.
Total Danish Sea (~51.300 km2)
17
LCoE development
Motivation MethodologyObjective Input OutlookResults Conclusion
LCoE reduction induced by:▪ CAPEX, OPEX and discount rate reduction.▪ Increase in wind farm and turbine capacity.▪ Lifetime improvement.
Wind turbines:▪ Siemens SWT-3.6-120▪ Vestas V164-8.0MW▪ DTU RWT-10MW▪ 12 MW (upscaled
DTU RWT-10MW)
18
LCoE ranking of pre-selected sites
Motivation MethodologyObjective Input OutlookResults Conclusion
Site LCoE [€/MWh](𝒓 = 𝟔. 𝟕𝟔%)
LCoE [€/MWh](𝒓 = 𝟖%)
LCoE COWI 13 [€/MWh](𝒓 = 𝟖%)
Jammerbugt 53 58 64
Nordsøen 53 58 60
Kriegers Flak area 54 59 64
Hesselø 58 63 62
▪ Difference to COWI’s raking caused mainly by inclusion of detailed onshore grid connection cost by COWI.
▪ LCoE reduction not necessarily related to site selection, rather has to result from:▪ Cost reductions.▪ Technology improvements.▪ Supply chain and installation campaign improvements.
13 COWI {2018}: Finscreening af havarealer til etablering af nye havmølleparker - Hovedrapport.
▪ Wind resource and AEP assessment WAsP/Fuga with wind turbine V164-8.0MW.▪ Cost estimates Input data of this study for FID 2021 and
lifetime of 30 years.
19
Electricity price comparison
Motivation MethodologyObjective Input OutlookResults Conclusion
Electricity price mainly affected by 14:▪ Fossil fuel price.▪ Carbon credit price.▪ Interconnection between Denmark and neighbouring countries.
14 Danish Energy Agency {2017}: Fremskrivning af elprisen. 20
Conclusion
Motivation MethodologyObjective Input OutlookResults Conclusion
▪ Most profitable areas close to the west and northeast coast of Jutland with around 50 €/MWh in FID 2021, due to high and stable wind speeds and relatively shallow water.
▪ Significant drop in LCoE from 2008-2030 caused by:
▪ Reduction in CAPEX, OPEX and discount rate.
▪ Increase in turbine and farm capacity.
▪ Lifetime improvement.
▪ Ranking of the pre-selected sites placed the areas in the North Sea first (Jammerbugt and Nordsøen) with 53 €/MWh, followed by Kriegers Flak area with 54 €/MWh and Hesselø with 58 €/MWh.
▪ Subisidy-free future for offshore wind projects might be possible if electricity prices will increase in the future and LCoE of offshore wind will decrease.
▪ If electricity prices will follow the low price scenario of around 25 €/MWh, a subsidy-free future might not be possible - not even considering the low LCoE scenario.
21
Outlook
Motivation MethodologyObjective Input OutlookResults Conclusion
▪ More precise input data more precise LCoE estimation.
▪ Minimize uncertainty related to methodology and cost estimates:
▪ Inclusion of ports and weather conditions (significant wave height, wind speed) when estimating installation costs and O&M.
▪ Update of substructure cost function and implementation of other substructure types (Jacket, floating).
▪ Inclusion of design optimization and more accurate wake loss estimation.
▪ Implementation of more detailed onshore grid connection costs, including land cable cost.
▪ Application of the tool in other countries.
▪ Inclusion of LCoE computations for onshore regions.
▪ Implementation of the methodology in the Global Wind Atlas (requires local data).
22
Questions?
23
Levelized Cost of Energy (LCoE)
Additional slides
▪ 𝐿𝐶𝑜𝐸 =σ𝑡=0𝐿𝑇 𝐶𝐴𝑃𝐸𝑋𝑡+ 𝑂𝑃𝐸𝑋𝑡
(1 + 𝑟)𝑡
σ𝑡=0𝐿𝑇 𝐸𝑡
(1 + 𝑟)𝑡
= 𝐿𝐶𝑜𝐸𝐶𝐴𝑃𝐸𝑋 + 𝐿𝐶𝑜𝐸𝑂𝑃𝐸𝑋
▪ 𝐿𝐶𝑜𝐸𝐶𝐴𝑃𝐸𝑋 =𝐶0
𝑎 ∙𝐴𝐸𝑃 ∙𝐿𝑇with a =
1
𝐿𝑇∙1+𝑟
𝑟∙ 1 −
1
1+𝑟
𝐿𝑇+1
▪ 𝐿𝐶𝑜𝐸𝑂𝑃𝐸𝑋 =𝐶𝑂𝑃𝐸𝑋
𝐴𝐸𝑃𝐶𝑂𝑃𝐸𝑋
€
𝑀𝑊ℎconstant per year
𝐴𝐸𝑃: Annual Energy Production𝐸: Energy Production𝑡: Time𝐿𝑇: Lifetime𝑟: Discount rate C: Cost𝑎: Levelizing factor
24
Levelized Cost of Energy (LCoE)
Key parameters Anholt Horns Rev 3 Kriegers Flak Vesterhav Nord Vesterhav Syd
Farm capacity [MW] 399.6 406.7 604.8 168 176.4
Tender; FID [year] 2010; 2011 2015; 2015 2016; 2018 2016; 2017 2016; 2017
Turbine type SWT-3.6-120 V164-8.3MW SG8.4-167 DD SG8.4-167 DD SG8.4-167 DD
Distance to PCC [km] 68 63 62 38 42
Water depth [m] 16 19 18 18 22
Total investment [M€] 1,207 963 1,342 376 357
Investment per MW [M€] 3.02 2.37 2.22 2.13 2.12
OPEX [€/MWh] 25.25 16.78 15.09 15.93 15.93
Net AEP [GWh/year] 1,561 1,560 2,174 650 612
LCoE incl. Grid 15 [€/MWh] 110.0 85.0 56.9 55.1 55.1
Interest rate to reach LCoE [%] 11.7% 11.6% 5.2% 5.2% 5.2%
15 Vindmølleindustrien {2016}: https://windpower.org/da/aktuelt/aktuelt_i_vindmoelleindustrien/news_q4_2016/kystnaere_havvindmoeller_fortsat_billigst.html
Additional slides
25
LCoE uncertainty
▪ Median (50% of Danish Sea area) has LCoE value of 45 €/MWh.
▪ The uncertainty for a high and low cost scenario is +12 €/MWh and −10 €/MWh.
Additional slides
26
LCoE sensitivity
▪ LCoE especially sensitive to:
▪ Discount rate
▪ Lifetime
▪ Turbine cost
▪ OPEX
Additional slides
27
Transmission cost
𝑉 = 300 𝑘𝑉
HVAC vs. HVDC
𝑉 = 220 𝑘𝑉
𝐼𝑠𝑠𝑛 = 942 𝐴 → 𝐴𝐶𝑢 = 1000 𝑚𝑚2
→ Determining number of cables needed.
Cost: 1240€2017
𝑚
Break-even distance: 65 − 100 𝑘𝑚
Depending on farm capacity.
Additional slides
28
Terminal cost: Low Terminal cost: High