Session description

This session considers the broad topic of fixed offshore foundation systems and includes papers addressing the primary elements of global or whole-structural-system analysis and assessment. A range of speakers will represent academia and industry with contributions covering different aspects of bottom-fixed support structures and foundations, their design, analysis and optimisation. Topics addressed will include hydrodynamic loads, soil-structure interaction and geotechnical issues, support structure dynamics and simulation technology, field testing and laboratory experiments as well as pile design.

Learning objectives

• Better understand soil-structure interaction mechanisms and analyse methods
• Appreciate how to analyse and assess structural dynamic behaviour
• Examine fatigue damage models applied to offshore wind foundations
• Recognise performance indicators for the whole-structure
• Identify methods to objectively assess optimum foundation configuration

Panagiotis Chaviaropoulos CRES, Greece
Co-authors:
Panagiotis Chaviaropoulos (1) ^{F P} Anand Natarajan (2) Peter Hjuler Jensen (2)
(1) CRES, PIKERMI, Greece (2) DTU-WIND, Roskilde, Denmark

Key performance indicators and target values for multi-megawatt offshore turbines

Introduction

This work is in the context of the FP7 Innwind.EU Project whose objective is the high performance innovative design of beyond state-of-the-art 10-20 MW offshore wind turbines. The assessment of innovation necessitates a framework where different designs can be compared against a reference on the basis of key performance indicators (KPIs). Following the European Wind Industrial Initiative the Levelized Cost of Electricity (LCOE) and its driving components are investigated, while quantifying the sensitivity of LCOE to its constituent factors. Targets are set to the LCOE by associating with specific technologies and high Customer Net Present Value (NPV).

Approach

The research focus is on quantifying the LCOE of innovative designs for very large offshore turbines (10-20 MW) at deep waters (50+ meters).
The LCOE of a wind farm depends on:
• All turbine capital costs (C )
• Balance of plant including the foundation, electrical cabling, logistics (BOP)
• FCR – fraction of capital costs paid each year
• Annualized O & M (OPEX)
• Annual energy production, AEP
LCOE = ((C+BOP)*FCR+O&M)/AEP
LCOE calculation follows the methodology and assumptions [1] introduced by EWII (European Wind Industrial Initiative) for monitoring progress in the SET-Plan. The anticipated LCOE time-evolution is also compatible with the EWII figures (Figure 1). The Net Present value (NPV) is computed for a wind farm owner based on electricity pricing guidelines [3], wind farm efficiency and assuming a variation of BOP costs over the years.
Keeping the EWII assumptions for OPEX we investigate the CAPEX and Capacity Factor targets that will allow meeting the 2020 LCOE target value (85 €/MWh) of Figure 1.

A possible pathway to this target value, resulting from a combination of classical up-scaling [2], [3] and technology improvement anticipations [4]- [6], is shown in Figure 2. A 5 MW turbine, the maximum size with available mass and cost data ([6] – [11]), is projected to the 10+ MW scale.

The overall target CAPEX is distributed to its major cost sub-components (rotor, nacelle, tower, offshore support structure etc). Following technology learning curves and pre-design calculations, the LCOE for upscaled turbines and associated innovation is shown in Figure 3 for a 10 MW WT where actual cost and up-scaling target values for all the major cost components are set.
Further, turbine design parameters which have a significant influence on the LCOE, BOP and the turbine CAPEX are sought. We have identified three candidates for which preliminary investigations regarding their down-stream influence have been made. These are: a) the rotational speed of the rotor, b) the tower-top mass and c) the design thrust of the rotor.
For the targeted designs, a plot of the annual cash flow for the customer versus the LCOE depicts the trade-off in the choice of technology.



Main body of abstract

In this section we discuss the results presented in Figure 2 and Figure 3. The “classical up-scaling” (using same technology) and “innovation-based up-scaling”, which adopts new technologies with a strong potential for cutting the costs (and weight) but also increases the offshore wind farm capacity factor are utilized. All LCOE and NPV computations herein are based on fixed capacity wind farms of 300 MW.
Effect of up-scaling on the wind farm capacity factor: Even classical up-scaling has a positive effect on the capacity factor of a large offshore wind farm. It is seen in Figure 4 (from calculations performed with the CRES-Farm engineering model [12]) that the wind farm aerodynamic capacity factor increases with the size of the turbine from 5 to 10 MW by nearly 2.5 % and from 10 to 20 MW by another 2.5%. Further, we have shown in [4] that innovative rotor design, such as low-induction / low-thrust rotors of increased swept area may produce more power with the same turbine loading. It is seen that by using the less loaded – larger diameter – turbines, the wind farm capacity factor increases by nearly 3 %. Half of this 3% comes from the increased annual production of the larger diameter turbine and half from the reduction of the wake losses due to the lower axial induction and, therefore, thrust coefficients of the larger rotors. Assuming that the turbine size effect and the innovative design effect on wind farm capacity factor can be superimposed the net capacity factor of a large offshore wind farm can increase by 3 % for a standard design and by 7 % for an innovative design from 5 to 10 MW (see CFs in Figure 2).

With increase in turbine rating beyond 15MW, the rate of decrease in LCOE for fixed capacities is lower than the rate of increase in cash flow. Decision to move to 20MW turbine ratings may be based more on return of investments rather than the LCOE target.
Effect of up-scaling on CAPEX: In classical up-scaling the scaling exponent for CAPEX is λ_c=3 for the turbine and its main subcomponents [2], [3] and λ_c=2 for the BoP. UPWIND project showed that for a fixed water depth, the electrical infrastructure and connection scales-up with the power of the turbine (λ_c=2) and similar assumptions are made in [11] for the other BoP cost categories (offshore foundation system, transportation, installation etc. For a fixed water-depth and a bottom-mounted design it is logical to assume that the offshore foundation system (monopile, jacket) weight is scaling-up in two dimensions and not in three (as constrained by the fixed water-depth), thus λ_w=2. Going to the “innovation-based up-scaling” figures we shall assume λ_w values lower than 3 and 2 for the turbine and BoP parts respectively. For the turbine every such λ drop is directly related to technological improvements while for the offshore substructure the fact that the hub height in not up-scaling linearly but adjusts to a fixed blade-mean sea level clearance leads to λ_w values closer to 1.7 than 2.0.
Since the turbine λ_c is still larger than 2 but the BoP λ_c is now smaller than 2 the contribution of the BoP in LCOE at a given water depth reduces as the rated power increases along with the contribution of the CAPEX. A fixed BOP cost that scales with the number of turbines is added to the cost per MW to account for costs such as logistics of installation and number of electrical cables. For bottom mounted designs, the optimum sizing of the turbine derives by balancing the extra turbine cost with the lower BoP cost per MW as the turbine size increases. Though as water depth increases, larger turbines may be the optimal solution, the optimal turbine size is still very dependent on how successful the new lower cost technologies in turbine and offshore substructure designs are implemented.
The main findings of Figure 2 and the discussion followed are summarized in Figure 5 where the re-distribution of the total CAPEX between the turbine part and the BoP part as the turbine size increases is evident.


Conclusion

A 20% LCOE drop from present values until 2020 seems quite feasible for deep offshore wind farms. Large (10 MW+), offshore-dedicated, wind turbines designs will be needed for that.
For fixed water depth, the optimum sizing of the turbine derives by balancing the extra turbine cost with the lower BoP cost per MW as the turbine size increases. This is a common conclusion in all offshore cost studies. It looks that as the water depth increases larger turbines will be the optimum bottom-fixed solution. Nevertheless, this optimum size is still very much dependent on how successful we’ll be in implementing new lower cost technologies in turbine and offshore substructure designs.
Significant LCOE reduction can be expected by improving the wind farm capacity factor. This can be done by using larger turbines with low induction (low-thrust) rotors for better aerodynamic performance and by improving the efficiency of the drive train, power electronics and array cables.
Coming to the downstream influence of the nacelle mass we have seen that even a very drastic reduction does not have an equally important effect on tower and foundation masses for bottom-mounted designs. This is somehow expected since the compressive load associated to the tower-head mass has a relatively small contribution to the tower and foundation design stresses. Thus, for bottom-mounted offshore designs, the reduction of the tower-head mass if not followed by an associated cost reduction (rotor or drive train) or an increase of the turbine capacity factor is not a target by itself and it can by no means pursued at the cost of drive train efficiency. This statement is not valid for floating designs where the tower-head mass might be an important driver of the cost of the floater.
Contrary to tower-head mass, the sensitivity of the overall support structure mass to the maximum (design) thrust is significant. This is a very important effect and should be one of the areas where innovation should be further pursuit. The concept of low-induction rotors is again a sound option for design thrust reduction.



Learning objectives
We present and discuss suitable performance indicators for assessing innovative offshore designs at the components and at the system’s level.
These performance indicators are cost driven and evaluate the
• Effect on energy yield
• Direct effect on LCOE and Customer NPV
• Indirect effect on downstream components (loads, weight)
Using learning curves and scaling laws target values are set at the subcomponents level with the purpose of satisfying the required overall performance improvement.



References
1. Key Performance Indicators for the European Wind Industrial Initiative, SETIS-TPWIND, Version: 3, 7th November 2011
2. Sieros G, Chaviaropoulos P, Sorensen JD, Bulder BH, Jamieson P. Up-scaling Wind Turbines: Theoretical and practical aspects and their impact on the cost of energy. Wind Energy 2012. 15(1): 3-17
3. The Crown Estate, Offshore wind cost reductions pathways study, May 2012.
4. Sieros G, Chaviaropoulos P.K. Aspects of up-scaling beyond similarity. Proceedings of ‘The Science of Making Torque from Wind’ Conference, Heraklion, Greece, 28-30/6/2010, Edited by S. Voutsinas and P. K. Chaviaropoulos.
5. Chaviaropoulos, T; H.J.M. Beurskens and S. Voutsinas, Moving towards large(r) high speed rotors – is that a good idea? Proc. Scientific Track, EWEA 2013 Conference, Vienna.
6. BTM Consultants, World Market Update 2011, http://www.btm.dk/reports/world+market+update+2011
7. The Crown Estate, Offshore Wind Cost Reduction. Pathways study. May 2012
8. LCICG, Technology Innovation Needs Assessment (TINA), Offshore Wind Power, Summary Report, February 2012
9. Renewable UK, Offshore Wind, Forecasts of future costs and benefits, www.RenewableUK.com, June 2011
10. The Economics of Wind Energy, EWEA Report, Soeren Krohn (editor), March 2009
11. Peter Jamieson, Innovation in Wind Turbine Design, A John Wiley & Sons, Ltd., Publication, ISBN 978-0-470-69981-2, 2011
12. Wind Turbine Design Cost and Scaling Model, by L. Fingersh, M. Hand, and A. Laxson, NREL/TP-500-40566, December 2006
13. E. S. Politis and P. K. Chaviaropoulos, “Micrositing and Classification of Wind Turbines in Complex Terrain,” Proceedings of the 2008 European Wind Energy Conference & Exhibition, Brussels, 31/3-3/4/2008, Edited by P. K. Chaviaropoulos, pp. 126-130.