Offshore Wind Farm Layout Design A Systems Engineering Approach. B. J. Gribben, N. Williams, D. Ranford Frazer-Nash Consultancy

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1 Offshore Wind Farm Layout Design A Systems Engineering Approach B. J. Gribben, N. Williams, D. Ranford Frazer-Nash Consultancy 0 Paper presented at Ocean Power Fluid Machinery, October 2010

2 Offshore Wind Farm Layout Design - A Systems Engineering Approach B. J. Gribben, N. Williams, D. Ranford Frazer-Nash Consultancy SYNOPSIS A systems engineering approach to offshore wind farm layout design is described. The main elements are flexibly defining layout parameterisations, integrating with multi-discipline performance and cost models, minimising non-value-added effort and promoting the development of insights into the design challenge. This approach is demonstrated using some simple but representative trade studies for offshore wind farm design, and is especially pertinent to the offshore environment where constraints on the detailed design of layouts are often fewer than onshore, and many of the cost elements are both greater and more complex than onshore, e.g. foundation construction and maintenance. Foundations Electrical Extreme Events Access Turbines Vehicles/ Vessels CAPEX Planning and Permissions Fatigue Loads Reliability OPEX Operation Maintenance Optimum Performance and Minimum Cost Market Tariffs and Incentives Wind Resource REVENUE Wake Losses Electrical Losses Figure 1: A multi-discipline model of an offshore wind farm, for optimum performance and minimum cost. 1 INTRODUCTION The offshore wind industry seeks to maximise total farm yield while minimising installation and through life costs. The new Round 3 sites bring fresh challenges due to the size of the farms, their distance from shore and the increased build areas. These challenges have placed an increased focus on refining layouts for optimising the overall performance of the development. The interaction of the various factors which contribute to the overall performance can be considered as a complex design optimisation problem, where a simultaneous consideration of models for each factor is attractive. Figure 1 indicates a number of the models which could be combined for such a multidiscipline approach. 1 Paper presented at Ocean Power Fluid Machinery, October 2010

3 The design analysis challenges can be summarised as follows: What is the best approach to characterise the design and explore different scenarios? Which models should be used, and when? What is the best overall design? The practical challenges in carrying out such studies are: Integrating tools; Running trade studies; Gathering and visualising results; Collating data. A systems engineering approach using dedicated process building and execution software does not solve any of the design problems. However, the practical challenges are significantly reduced, allowing a greater proportion of time to be devoted to design exploration. In this way the development of insights is promoted. This paper demonstrates a practical technical approach which can be employed to tackle these design challenges. Key elements are the flexible parameterisation of the layout, the rapid integration of existing and varied models for multiple disciplines, and the insight afforded by minimising nonvalue-added processing time in favour of design exploration. The paper centres on the implementation of wind farm design scenarios and their subsequent examination. The role played by generic, off-the-shelf process building and optimisation software is discussed. An open source wake effects analysis tool is included, with spreadsheet implementations for other models. Response Surface Modelling (RSM) is used to visualise the design space. Although specific models and integration software have been selected in order to populate the conceptual model, the overall conclusions are not tied to these selections. The approach is rapid and simple, allowing far more complex parameterisations and modelling as project maturity progresses or insights are developed. The system could be used to go beyond trade studies towards automated optimisation; however, fully automated optimisation is not considered as part of this paper. The overall approach is described in Section 2. The models and parameterisations employed are described in Section 3. Finally, the results of some trade studies are described in Section 4. 2 APPROACH The overall aim is to rapidly configure a multi-discipline wind farm layout design environment, where parameterisations can be explored and trade studies carried out. The emphasis is on what can be achieved practically with current technology. The methods presented demonstrate the validity of the approach via representative scenarios consisting of multiple models and multiple parameters. 2.1 CAPEX, OPEX and Revenue The performance of an offshore wind farm development over its lifetime can be considered as consisting of a number of elements. A typical approach to lifecycle costs is to consider revenue, OPEX (Operational Expenditure) and CAPEX (Capital Expenditure). Revenue accounts for the total power extracted from the site, including reductions due to lack of availability and poor generating conditions, combined with the price of electricity at the time. Revenue also accounts for total electrical losses from the point of generation to the point of sale to the utility, typically on shore. The initial cost of installation is given by the CAPEX model. This will include all lease application effort, site development, turbine purchase and installation and connection to the grid. OPEX accounts for the ongoing costs of running a wind farm, including maintenance, support staff and vehicles, monitoring and lease payments. Constraints on project development, which could also be accommodated in the proposed framework, may include unquantifiable elements such as environmental acceptability or disruption to shipping. Figure 2 indicates a number of models conceptually combined into our initial offshore wind farm performance model. 2 Paper presented at Ocean Power Fluid Machinery, October 2010

4 Often models for these elements are available to developers, however their combination in a structured system for simultaneous analysis and trade studies is not common practice in the industry. As the performance of models representing different elements will often be contradictory when comparing layout designs, the need for trade studies is clear. For example, a layout with large separation distances between turbines will minimise wake effects therefore maximise yield, however construction costs will be high due to long cable inter-connect lengths. Combining these models can easily become a cumbersome and time consuming software engineering task, as can analysis of the data collected when performing design studies. To mitigate this, generic process development software is available where productivity gains and flexibility benefits are substantial. Exploitation of such a system is proposed, and discussed further in Section Model Selection In theory, all disciplines that contribute to CAPEX, OPEX or revenue could be represented by a cost model. Such a comprehensive approach would rapidly lead to a long list, such as: Fatigue loads; Reliability; Access, operations and maintenance; Electrical losses; Detailed electrical systems cost models. It is more practical to modify the selection of which models to include according to their level of significance to the overall design as it matures. Making this key choice is not the focus of this paper, however the flexibility to make such changes is demonstrated in the example trade studies in Section 4. A realistic wake effects model is described in Section 3.1. Simple models of cable costs and foundation costs are described in Sections 3.2 and Characterisation Characterisation of the design and selection of design parameters is key to the success of a multidisciplinary study. A balance has to be achieved between the completeness or level of fidelity of the model and the practicality of running simulations with large numbers of variables. There is no substitute for engineering insight into which parameters to select at which stage of the design maturity. For example, the characterisation of a 100 turbine offshore wind farm could include the following parameters: Location of each turbine (200 parameters); Turbine type (100 parameters); Foundation type (100 parameters); Servicing options (4 parameters). This list could be extended easily but it is clearly not practical to consider all of these parameters simultaneously. In reality, some key choices (e.g. turbine type) are likely to have been made before refining the layout design. As the practical limits to the number of parameters are likely to be limited at tens rather than hundreds, some layout parameterisations with fewer variables are discussed in Section Paper presented at Ocean Power Fluid Machinery, October 2010

5 Figure 2: Process building within ModelCenter 2.4 Creating a Multi-Discipline Model The commercial software ModelCenter (Reference 1) has been used in this study to create multidiscipline models. ModelCenter allows the rapid connection of software modules into processes, the variation of inputs to the process, and the examination of outputs from this process. As well as providing connectivity in a drag-and-drop manner to common engineering and office software packages, any batch programme can easily be linked into a process in a visual manner, taking a few minutes for each component. In this case, the components are the models described in Section 3. An efficient means to initiate design space exploration for multi-dimensional models is via a Design of Experiments (DoE) approach. For the relatively few parameters studied here, a full factorial variation of input parameters was employed. Having connected the models and selected the range of parameter values to examine, running the model elements and combining their results is automatic. The response of the model is recorded as discrete data, and can be visualised in a number of ways as both discrete and continuous data. In this study, a polynomial response surface was employed to visualise the data and aid in the detection of trends. The automatic optimisation capabilities of ModelCenter have not been explored in this study. The significance of using dedicated process development software in this study is that the effort required to define, run, exploit and modify multi-disciplinary models is dramatically reduced compared to a bespoke software integration effort. All of the models used in this study were created in a few days by an engineer with no prior experience of ModelCenter. 3 MODELS USED 3.1 Wake Effects Wake effects are well known to influence the yield of wind farms, with rapid semi-empirical models being most commonly used for wake effects predictions (Reference 2). A number of commercially available software packages are widely used for the analysis of turbine layouts. In making the selection of the wake effects model to use in this study, it was necessary to use software which both represented common industry practice and which was capable of running in batch mode. OpenWind, 4 Paper presented at Ocean Power Fluid Machinery, October 2010

6 an open source wind farm layout modelling package (Reference 3), was selected as it meets both these criteria. OpenWind includes an implementation of the common Modified Park model for wake effects (Reference 4), which has been used throughout this study. The semi-empirical wake interaction modelling allows rapid turnaround on large numbers of analyses an important aspect when thoroughly investigating the design space. The availability of source code means that the software is suited for integration within an automated process building software package. Other graphically driven wind resource modelling packages rarely have this flexibility. The inputs to the wake effects analysis module are the individual locations of turbines. The turbine type are representative of those commonly used in today s North Sea wind farms, and a typical wind climate for the North Sea has also been selected. The output from the model is the predicted array efficiency. 3.2 Electrical cables Subsea electrical cabling is expensive. For some of the proposed far offshore developments this will account for a substantial proportion of the installation costs. In maximising the overall economic viability of a development, the CAPEX associated with cabling must be included in early design stage models. The trade-off between closely packed turbines which minimise cable costs, and improved yield performance by spacing the turbines further apart is one example of a project life cycle trade-off. Indicative cable costs can be easily incorporated into the trade off studies. The simple model used in the current study is implemented in a spreadsheet, requiring the individual locations of turbines as inputs and providing cable cost as an output, assuming cable cost varies linearly with the cumulative separation distances. A more complex electrical cable model would be unlikely to require any additional integration effort. 3.3 Foundation costs The cost of foundations increases as depth increases. Local bathymetry data can be assessed against proposed layouts and the associated costs can be incorporated into the optimisation. The current simple model is implemented in a spreadsheet, requiring the individual locations of turbines as inputs and providing the total foundations cost as an output, assuming that foundation cost increases linearly with depth. 3.4 Layout parameterisation In the current study a spreadsheet has been constructed to generate turbine layouts using various parameterisations. In the trade studies shown in this paper, the parameters used are orientation, aspect ratio and skew, indicated in Figure 3. A full design space of layouts can be generated quickly. Examples of other parameterisations which could also be implemented with no alteration to other elements of the process are: Width of column breaks / separation distance between farms or sub-farms; Size of holes in centre of wind farm; More turbines on exterior / fewer on interior; Individual turbine positions; Selective downrating. Aspect Ratio Skew Orientation Figure 3: Variables used in Layout Parameterisation 5 Paper presented at Ocean Power Fluid Machinery, October 2010

7 4 TRADE STUDIES 4.1 Trade Study 1: Orientation A square layout of 64 turbines with equal spacing has been used to determine the impact of orientation on wake losses. The aspect ratio and skew are constant, with the orientation being varied in 10º increments through 180º. ModelCenter has performed an automated batch-run of the 19 layouts and returned performance data for each orientation. Figure 4 indicates the ModelCenter process and the selection of models used in the trade study. Design Parameters Trade Study 1 Orientation Trade Study 2 Orientation, Aspect Ratio, Skew Trade Study 3 Distance from shore Trade Study 4 Individual Turbine Positions Array Efficiency Figure 4: Trade-study 1 Figure 5: Results from Trade-Study 1 Array Efficiency vs Array Orientation Orientation (degrees) Clear peaks in performance of the total development can be seen (Figure 5). This subset of cases, shown in red in Figure 5, has been selected for further investigation by exploring additional parameters in Trade Study 2 The user effort required to run this study is small. The batch execution of the trade studies required an order of magnitude less user effort that a manual model set-up and analysis. 4.2 Trade Study 2: Orientation, skew and aspect ratio In order to investigate the performance of the method with multiple parameters, a 2 nd trade study has been performed using orientation, skew and aspect ratio (Figure 6). The best performing design from the 1 st trade study has been used as the baseline. 125 designs points were evaluated, with an elapsed time of approximately 5 minutes per design point. Trade Study 1 Orientation Design Parameters Trade Study 2 Orientation, Aspect Ratio, Skew Trade Study 3 Distance from shore Figure 6: Trade-study 2 Trade Study 4 Individual Turbine Positions 6 Paper presented at Ocean Power Fluid Machinery, October 2010

8 A number of plotting tools are available in ModelCenter for viewing data resulting from multidisciplinary trade-offs and optimisations. Figure 7 and Figure 8 display response surfaces of the resulting data. This is an effective way to identify trends in the response of the model to parameter variations. Figure 9 shows an alternative representation of the design space, with the evaluated design points coloured by an array efficiency scale and the optimal solution highlighted Array Efficiency Array Efficiency Aspect Ratio Orientation Figure 7: Response Surface Aspect Ratio and Orientation Skew Orientation Figure 8: Response Surface Orientation and Skew Figure 9: Design visualisation Optimal Solution 7 Paper presented at Ocean Power Fluid Machinery, October 2010

9 4.3 Trade Study 3: Foundations, cable costs, yield & distance from shore The resulting sub-set from Trade study 2 has been carried forward to investigate further options (Figure 10). A cost-weighted function has been constructed for foundations, cabling and yield as a consequence of the overall distance of the development from the shore. A total cable cost model consists of Export Cable and Array Cable parts (Figure 11). For the purposes of this study an approximate but representative bathymetry is supplied to the foundations cost model as shown in Figure 11. Design Parameters Trade Study 1 Orientation Trade Study 2 Orientation, Aspect Ratio, Skew Trade Study 3 Distance from shore Trade Study 4 Individual Turbine Positions distance from shore (m) Export Cable y location (m) Array Cable Figure 10: Trade-study 3 Figure 11: Indicative sea bed bathymetry for the foundation cost function, and a simple cable costs model. Figure 12: Relationship between costs and distance from shore Figure 13: Relationship between yield and distance from shore The variation in costs (Figure 12) and yield (Figure 13) with the distance from shore are shown. In this case, wind strength is assumed to increase with distance from shore. There is a clear benefit to revenue in moving the array development offshore, however, the CAPEX also increases. The optimal position is therefore a straightforward trade-off between costs and revenue. In Figure 14 the models are combined in a weighted average, with the overall best solution indicated in blue on the colour scale. 8 Paper presented at Ocean Power Fluid Machinery, October 2010

10 Figure 14: Trade off parameters in ModelCenter visualisation 4.4 Other Trade Studies The effort required to implement new parameterisations depends on the complexity of those parameterisations, but is relatively small. For example, introducing a column separation distance parameter would require around ten minutes. Introducing new or alternative cost or performance models is also rapid. The most likely limitation of the approach is the maximum number of parameters. For example, for 64 turbines, a parameterisation of their locations would consist of 128 parameters, which is unlikely to be practical unless investment is made in dedicated computing resources or in accelerating the underlying methods. 5 CONCLUSIONS An approach for performing wind farm design trade studies has been described. The flexibility of the approach to rapidly support other design scenarios has been demonstrated. This approach enables a focus on design challenges rather than on overcoming practical issues. The potential for maximising productivity gains, and for minimising cost by a full exploration of the design space, is clear. To effectively tackle the design challenges requires expertise and experience, and insight into effective characterisation. The systems engineering approach using ModelCenter does not solve any of the design problems, but significantly reduces the practical challenges and promotes the development of design insights. 6 ACKNOWLEDGEMENTS The authors would like to thank Phoenix Integration for supplying ModelCenter, and AWS Truepower for OpenWind support. 9 Paper presented at Ocean Power Fluid Machinery, October 2010

11 7 REFERENCES Wind turbine wake aerodynamics. L.J. Vermeer, J.N. Sørensen, and A. Crespo. Progress in Aerospace Sciences, 39: , A Simple Model for Cluster Efficiency, I. Katic, J. Højstrup, and N.O. Jensen. EWEC 1986 Proceedings, 7-9 October, Rome-Italy, Paper presented at Ocean Power Fluid Machinery, October 2010