Special Issue Article
Modelling and simulation of the operation and maintenance of offshore wind turbines
Proc IMechE Part O: J Risk and Reliability 2015, Vol. 229(5) 385–393 Ó IMechE 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1748006X15589209 pio.sagepub.com
ˆ ngelo P Teixeira and C Guedes Soares Fernando Santos, A
Abstract The offshore environment limits the accessibility to the wind turbines and subjects them to faster degradation processes than in onshore. Thus, operation and maintenance is more challenging and costly and represents a considerable share of the cost of energy. It is therefore important to identify which factors most influence the turbines’ performance, namely, the availability, overall cost and revenues, so that actions can be taken to minimize their effect. This article addresses such issues by presenting a parametric study on how the variation of failure and repair models, vessels logistic times, weather windows and waiting times affect a wind turbine performance. Offshore failure models/data were not usually available on the public domain, being obtained herein from onshore ones using an empirical approach based on stress factors for mechanical systems. The baseline model results from the optimization of an operation and maintenance strategy based on corrective maintenance replacements and imperfect age-based preventive maintenance repairs. Generalized stochastic Petri nets with predicates coupled with Monte Carlo simulation are used for modelling and simulation. Results are discussed.
Keywords Maintenance, modelling, simulation, Petri nets, offshore wind turbine, performance influencing factors
Date received: 30 September 2014; accepted: 4 May 2015
Introduction Currently, more than 90% of the offshore wind capacity installed worldwide is in European waters. However, offshore wind energy development is starting to take off in China, followed by South Korea, Taiwan and the United States.1 Since the installation of the Vindeby offshore wind farm in Denmark, 23 years ago, projects have moved to water depths beyond 40 m and distances of up to 100 km from shore1 and the European planned projects for after 2015 are to be built in deeper waters and farther from shore.2 Although the costs have decreased approximately 30% per decade,1 a recent economic analysis in Europe has shown that the costs of installation, energy and operation and maintenance (O&M) of offshore wind turbines are around 100%, 33% and 18– 23% more costly than onshore, respectively.3 Moreover, the cost of offshore wind energy is highly influenced by O&M costs, which can reach up to 30% of such cost.4 The fact is that the uncertainties involved in the O&M of offshore wind turbines/farms make it more complex than onshore. The accessibility for
routine servicing and maintenance is more limited due to harsher weather conditions and sea-state, especially in winter when wind farms may be inaccessible for long periods of time, leading to major downtimes and losses of energy production. Also, the offshore distances, site conditions, wind farm size, wind turbine reliability and type of maintenance resources (e.g. vessels) make O&M tasks more costly than onshore, even in face of favourable weather conditions.3 Thus, assessing these effects on the O&M costs is important when planning O&M activities so that the expected total costs over a farm operating life can be minimized and consequently make offshore wind farm projects economically more viable.
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Te´cnico, Universidade de Lisboa, Lisboa, Portugal Corresponding author: Fernando Santos, Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Te´cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Email:
[email protected]
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A comprehensive review of the state-of-the-art on maintenance logistics in offshore wind energy has been recently provided by Shafiee.5 The review covers issues and challenges related to operation and maintenance logistics of offshore wind farms classified according to a strategic-tactical-operational framework. The strategic issues include among others wind farm design for reliability, selection of wind farm maintenance strategy. The tactical topics embrace spare parts inventory management, whereas the operational decisions include scheduling of maintenance tasks and routing of vessels. Several studies have also addressed the optimization of offshore wind turbines and farms maintenance.6,7 One problem that is particularly relevant is the role of grouping in the development of an overall maintenance optimization framework for offshore wind turbines. This results from the fact that wind turbines on the wind farm will behave similarly as when they are located in close proximity, which can be exploited at the wind farm level as done by Hameed and Vatn.8 Markov approaches have been used in the optimization of wind farms.9,10 Recently, a Markov decision process was proposed to determine the optimal maintenance and operation policy of an offshore wind farm taking into account the stochastic wind and weather conditions.10 Markov approaches have two main limitations: the number of states increases rapidly with system size and complexity leading to state-explosion; cannot model events that are not exponentially distributed.11 However, capturing the complexity of real systems performance features and modelling the dependencies and interactions between the system components is a challenge for maintenance planning and optimization12 which is feasible to be handled with Monte Carlo simulation (MCS) and Petri nets,13 as is demonstrated in this article. In this article, Generalized Stochastic Petri nets (GSPN) with predicates coupled with MCS are used to model, simulate and optimize an O&M strategy for an offshore wind turbine and to estimate the effect on the mean availability, overall cost (i.e. O&M and loss production costs) and revenues due to varying the values of several parameters relatively to baseline values. The loss production costs are estimated considering turbine rated capacity 3 capacity factor 3 tariff for wind energy 3 turbine downtime. Failure, repair, logistic and weather parameters are considered. The Petri net software used in this study allows creating prototypes of identical components (having similar operating modes) which can be stored in a library and then reproduced in several points in the Petri net model. The model presented in this article has been developed in this context. The objective of this article is to create a complex model of a single turbine that can be used as a repetitive block in the analysis of a larger scale model of an offshore wind farm that
consists of a system of several subsystems with identical characteristics. A wind turbine with four degraded components is subjected to corrective maintenance (CM) replacements when necessary and to age-based preventive maintenance (PM) repairs only in the summer. There is almost no public domain field data on offshore wind turbines,3 thus the failure models of the components are obtained from onshore ones using an empirical approach based on stress factors for mechanical systems.
Baseline logistics and weather parameters Maintenance modelling and simulation is performed on four components, rotor, gearbox, generator and pitch system of a 5 MW offshore wind turbine located about 45 km from German shore. The capacity factor has been reported to be 50.8%.14 The tariff for wind energy produced by offshore wind turbines in Germany is considered to be e0.15/kWh.15 Three types of vessels or maintenance categories based on a report16 from the Dutch Offshore Wind Energy Converter (DOWEC) consortium are used according to the weight of the components under maintenance, as follows:
Jack-up vessel: used in the CM replacements of the rotor, which can weigh between 90 t and 150 t for a 5 MW turbine;17 Crane vessel: for the CM replacements of the generator and gearbox, which can weigh up to 20 t and 65 t,17 respectively; Supply vessel or supplier: used for the CM replacements of the pitch system, which can be lifted by the 1 t permanent internal crane of the wind turbine, and to support the CM operations of the larger vessels. Only this vessel is used in the PM of any of the four components.
Only the supply vessel is docked at the harbour and it transports a maintenance crew of four technicians, which works one 12 h shift per day, 7 days a week. The travelling times to and from the failed turbine are included in the shift, which can start at any time of the day as long as all the logistic support are in standby at the harbour, that is, the vessels and the maintenance team are in standby in the nearest harbour, ready to travel to the turbine so that maintenance actions can be performed; and a weather window is available for the total amount of days needed to end a maintenance task on the turbine. While the jack-up and crane vessels stay at the turbine during each full CM activity, the supply vessel has to travel to and from the turbine every day until the maintenance task ends. It is assumed that in the beginning of a CM task, the supply vessel arrives at the
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Table 1. Vessels’ logistic parameters and costs.
Table 3. Weather window probability, Pw, and waiting time, Tw.
Parameter
Supply
Jack-up
Crane
Season
Pw
Tw (day)
TtravHT (day) TtravLVH (day) Costs (e) Hourly rate Mobilized/demobilized
0.09 2a
0.19 21
0.13 6.67
600 N/A
6250 57,000
6250 45,000
Winter Autumn Spring Summer
0.30 0.50 0.60 0.80
60 30 10 3
N/A: not applicable. a Delay for a spare pitch system to be available.
Table 2. Cost of the turbine’s components. Component
Cost/unit (e)
Gearbox Generator Pitch system Rotor
863,000 247,000 123,300 1,849,000
turbine location at the same time as the jack-up or crane vessels. Based on a report16 from the DOWEC consortium, Table 1 shows the travel time (TtravHT) of each vessel from the harbour to the turbine, estimated from the vessels’ velocities; the travel time (TtravLVH) larger vessels take to arrive at the harbour and the time needed for a spare pitch system to be available; the vessels’ hourly rates and mobilization costs. The hourly rate of a technician is e70 and it is assumed that rotor, gearbox and generator spares are ready to be transported by the time the larger vessels arrive at the harbour. Table 2 presents the approximate costs of the components of a 5 MW offshore wind turbine.17 The cost of transporting a spare from the manufacturer to the harbour is considered to be included in the unit cost of the component. In a wind turbine, a maintenance action may comprise different operations, each with their own operational limits. In offshore conditions, these require different types of vessels with their specific operational limits. Thus, for offshore wind turbines, a corrective or PM action is only performed when the wind speed and wave height are within the operational limits of that action for a period of time long enough to end the maintenance action. In other words, when there is a weather window available. The probability of occurrence of a weather window, Pw, and the waiting time for it, Tw, are estimated from correlated wind and wave data and considering the different operational limits. However, in this article, not having such data available for the location being studied, a set of seasonal probabilities, Pw, and waiting times, Tw, were defined in Table 3 to work as a mere example. When a CM or PM is to be performed, there must be a weather window available. Thus, as soon as the vessel(s) and the maintenance crew are in standby at the harbour, the model generates a random number
between 0 (zero) and 1. If this value is smaller or equal to the seasonal Pw, a weather window is available and the maintenance action can carry on, otherwise a waiting time, Tw, for a weather window is set. When this waiting time ends, a weather window is available. Also, more sophisticated weather models can be developed and implemented in the Petri net model when data are available in particular sites. Probabilistic models of weather windows can be developed from existing weather data, as done, for example, by Martins et al.,18 and used in this type of study, instead of the more simplified approach adopted now. When considering a wind farm with several wind turbines, other maintenance options such as opportunistic maintenance strategies can be adopted. Opportunistic maintenance consists in taking a maintenance action or system shutdown as an economically worthwhile opportunity to perform maintenance on other components that were not the cause of the opportunity. This issue has been addressed, for example, by Ding and Tian19 and it is expected to reduce the maintenance costs due to this strategy.
Stress factors and O&M strategy The offshore wind turbines are subjected to higher environmental and power utilization stresses than onshore turbines resulting from the marine environment. Therefore, they have higher failure rates,20 which is the reason why onshore failure models are not appropriate for modelling the O&M and performance of offshore turbines. Given that public domain field data of offshore wind turbines are scarce,3,20 power rating and environmental stress factors for mechanical systems21 are used herein as an empirical approach to obtain the offshore failure models of the turbine’s components from characteristic onshore failure distributions. In order to use the aforementioned approach, it is assumed that both wind turbines, onshore and offshore, are of the same type and that the higher environmental and power utilization stresses of offshore wind turbines are taken into account.20 The empirical equation (1) is based on Davidson21 to derive the offshore failure rates loffshore = lonshore ðK1 K2 Þ pðAÞ
ð1Þ
where loffshore is the derived failure rate of a component of the offshore wind turbine for failure mode A; lonshore is the failure rate of the same component but under
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Proc IMechE Part O: J Risk and Reliability 229(5) Table 4. Onshore and offshore failure models. Component
Rotor Gearbox Generator Pitch
Distribution
Weibull
a (day)
b
Onshore
Offshore
3000 2400 3300 1858a
1847 1477 1594 1144
3 3 2 3
a
Parameter derived from a MSc Thesis23 and a wind energy report.24
Figure 1. K2 as function of the component nominal rating.
ideal conditions of minimal stresses (e.g. onshore); K1 and K2 are the environmental and power rating stress factors of the component under offshore conditions, that is, represent the influence of the weather and marine environment, and the effect of different operating power ranges on its reliability, respectively; p(A) is the proportion of the failure rate for failure mode A of the system. In this article only a failure rate and mode per component is assumed, thus p(A) = 1. All four components of the offshore turbine are inside the nacelle, unexposed directly to the marine environment, that is, are considered to be marine, sheltered, an environmental condition for which K1 = 1.5.21 The empirical values for the power rating stresses21 are given in Figure 1. The trend line of Figure 1 shows that K2 grows exponentially with the increase in the component rating over its nominal rating. Offshore wind turbines endure higher wind speeds than onshore ones of the same type and, therefore, their average capacity is higher. The approach used to derive K2 relates the higher usage (higher capacity factor) of an offshore wind turbine compared to an equivalent onshore one and assumes that this higher capacity factor is attained by considering an increase of the power rating above the nominal rating and the effect of this ‘over rating’ (‘high usage’) is accounted by an increase in the power rating stress factor, K2, given in Figure 1. Thus, the K2 of an offshore wind turbine can be estimated from the capacity factor of a particular installation. Also, when applied to failure rates, K2 accounts for the difference among the capacity factors of offshore and onshore wind turbines.20,22 The average capacity factor of the German wind turbines was 19% at the end of 2011 and 99.3% of the total installed wind capacity was onshore.15 By assuming this average capacity factor for onshore wind turbines and that it represents 100% of a component nominal rating, then from Figure 1, K2 = 1. Now, considering 50.8% as the capacity factor of an equivalent offshore wind turbine, one has 131.8% (i.e. 50.8%– 19% + 100%) of component nominal rating, which from the exponential equation in Figure 1 gives K2 = 2.86.
Equation (1) has been used considering constant failures rates.20 However, in this article, the failure rates follow two-parameter Weibull distributions and increase with time. Thus, there is one equation and two unknown ‘offshore’ parameters (a and b). Consequently, one of these has to be kept constant. Knowing that the mean time to failure (MTTF) of a degraded (Weibull) component is more sensitive to the variation of the scale parameter (a), then the shape parameter (b) is assumed the same for both onshore and offshore equivalent components. Therefore, equation (1) turns into equation (2) loffshore =
b tb1 aboffshore
=
b tb1 abonshore
K1 K2
ð2Þ
Manipulating equation (2) one gets aoffshore =
aonshore 1
1 b
= 4:29
aonshore
ð3Þ
ðK1 K2 Þb
Using typical failure distributions of onshore turbine components19 in equation (3), one obtains the respective failure models for an equivalent offshore wind turbine, as in Table 4. Both onshore and offshore turbines are assumed to be of the same model and have 5 MW of rated capacity. The O&M strategy consists of CM replacements when components fail and age-based imperfect PM repairs carried out only in the summer. The failure models and the effective repair/replacement times of the components are assumed to be independent. The PM is performed when the age of a component is equal to or larger than a time p 3 MTTF, where the preventive repair threshold parameter is 0 \ p41. When the repair ends, the age is reduced by q, an age reduction ratio, which is 0 \ q \ 1. The repair cost is the q2 factor19 times the cost of a new component. The CM and PM distributions of the effective replacements and repair times, respectively, are given in Table 5.
Stochastic Petri nets model Petri nets are a tool that combines graphical and mathematical modelling to simulate and analyse discrete event systems. They were first introduced by Carl
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Table 5. CM and PM distributions. Component
CM (exponential) 21
PM (lognormal)
l (day )
MTTR (day)
SD (day)
0.480
2.083
0.625
2.400 0.600
0.417 1.667
0.125 0.500
Gearbox Generator Pitch system Rotor
CM: corrective maintenance; PM: preventive maintenance; MTTR: mean time to repair; SD: standard deviation.
Adam Petri in 1962. Since then, many different extensions have been proposed to the original ones for application in diverse fields such as computer science, communications, automation and industrial production systems, among others. Petri nets can model a system and simulate its behaviour through the use of the following three basic elements: places and transitions, which model the conditions and events of a system, respectively; directed arcs connect places to transitions or transitions to places. Generally, places are graphically represented by circles, transitions by rectangles and arcs by directed arrows.25 Generally, places symbolize the states of systems/ components and can also be seen as deposits of resources, while transitions represent the events (e.g. failures, tests and maintenance actions) which manipulate those resources and are responsible for the system state changes. Graphically, resources are represented by small full marks named tokens which are held inside places. The behaviour of a system and its maintenance are governed by the interactions and dependencies between system’s components, and Petri nets allow modelling them properly. Stochastic performance outputs can be obtained combining such nets with MCS.13 An extension to the original Petri nets which have proven to be powerful in modelling complex systems are the GSPN with predicates coupled with MCS.11,26–29 Their transitions can fire deterministically, stochastically and be conditioned by predicates, that is, by guards and assignments, to model the marking (i.e. states) of a net (e.g. system). The guards are pre-conditions that can enable or inhibit the firing of transitions, while assignments are post-condition messages that update variables used in the model (e.g. in transitions). They are both identified with two prefixes, ?? and !!, respectively. The GSPN model used for this article comprises 50 places and 56 transitions, thus only the PM Petri net of the gearbox shown in Figure 2 will be described. Three primary conditions (i.e. guards) must be checked before assessing whether there is a weather window to perform the gearbox PM:
The gearbox must be functioning (i.e. ??Gear_avail == 1 is true);
Its age is equal or greater than a p 3 MTTFgearbox (i.e. when the if then else PerformGear_PM = ite((time() 2 max(Gear_lastCM, Gear_lastPM) + Gear_age) . = (p* Gear_MTTF), true, false)); It is summer (i.e. summer is true).
If so, transition CheckGear_forPM fires immediately (i.e. dirac = 0) removing and creating a mark in places NoGearPMstate and GearPMwaiting_WW, respectively. Now, if the supply vessel and the maintenance crew are on standby and no other component is in repair/replacement, either a waiting delay for favourable weather conditions must elapse or a weather window is stochastically ‘generated’. In the first case, the PM is cancelled (i.e. NoGearPM fires) if meanwhile the gearbox fails or the summer ends. In the second, the vessel travels to the turbine and the value true is assigned to variable Crew_inMaint. As soon as the maintenance crew arrives at the turbine, the PM starts immediately (i.e. Start_GearPM fires). A maintenance task may last several shifts, and while the maintenance crew is working (i.e. ??Crew_working is true) the transition EndGearPM stays enabled. Otherwise, it is inhibited and stops accumulating repair time given that a shift or the repair has ended. This is possible due to the ‘memory’ property (i.e. MEM) of the transition, which resets when the repair time ends. Therefore, the turbine downtime is larger than the effective repair time. After the imperfect age-based PM, the gearbox age is 12q times its accumulated age, where q is the age reduction ratio (i.e. !!Gear_age = (time() 2 max(Gear_lastCM, Gear_lastPM) + Gear_age) * (1.02q)). The transition EndGearPM fires stochastically according to the repair lognormal distribution (i.e. nlog GearPM_MTTR, Error_Factor), releasing the crew (i.e. !!Crew_inMaint = false) and updating the calendar time of this ‘last’ maintenance (i.e. Gear_lastPM = time()).
Results and discussion A convergence analysis of the MCS was performed on the GSPN model of the offshore wind turbine. The baseline logistic, weather and O&M parameters presented in sections ‘Baseline logistics and weather parameters’ and ‘Stress factors and O&M strategy’ were considered along with q = 0.6, p = 0.5 and simulations of duration of 10,950 days (i.e. 30 years). The variation of all important output statistics was below 0.1% when the number of simulations increased from 28,000– 29,000. Typically wind turbines are developed for a 20year design life. However, there have been recent initiatives indicating a gradual change to 25-year or 30-year operating periods. So, for this study, the upper limit of 30-year operating period was chosen. A preliminary study was performed to assess the influence of the power rating stress factor, K2 (equation (1)) on the results. It was found that varying K2 from
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Figure 2. GSPN of the gearbox PM.
230% to 30% in steps of 10% produces an almost linear variation of only 21.59% in the turbine mean availability. The optimal values of the O&M strategy were obtained for q = 0.6 and p = 0.4, being the following: 93.4% of mean availability, e65.975 m of mean overall costs and e27.512 m of mean revenues. The optimal values were obtained considering 90 combinations
of p and q resulting from the variation of 0 \ q \ 1 and 0 \ p 4 1 in steps of 0.1, being performed 29,000 simulations in each combination. The total simulation time was 3 h and 43 min on a 3.3 MHz Intel Core i3. A parametric study of the baseline model was performed, keeping the optimal values of q and p fixed. Accordingly, each of the following parameters was varied from 230% to + 30% relatively to the baseline
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Figure 5. Effect on the revenues by varying parameters. Figure 3. Effect on turbine availability by varying parameters.
Figure 4. Effect on the overall cost by varying parameters.
values, in steps of 10%: MTTF and MTTR of the turbine, travelling times (TtravHT) of each vessel from the harbour to the turbine, travelling times larger vessels take to arrive at the harbour and the time to get a spare pitch system (TtravLVH) and weather parameters, Pw and Tw. The major effects on the turbine’s performance measures, that is, on the mean availability, overall cost and revenues are shown in Figures 3–5. The variation of the turbine’s MTTF has the highest effect on the three performance measures. In absolute values, the ranges are around 4.4%, e48.740 m and e53.098 m, respectively. The fact is that the number of failures and the season in which they occur will greatly influence the downtime, thus production losses, and the use of the costly larger vessels to perform CM replacements. The MTTR has the second highest effect on the turbine’s availability (i.e. jDj’2.8%), but close to that of the probability of occurrence of weather windows (i.e. Pw) and respective waiting times (i.e. Tw), that is, jDj ’ 1.4%. However, the effect of the MTTR on the overall cost, jDj ’ e9.46 m, and revenues, jDj ’ e12.23 m, is considerably surpassed by the variation outcome of the Pw and Tw, that is, jDj ’ e16.95 m and jDj ’ e18.36 m, respectively. The reason is that despite the number of
Tw for CM being much smaller than the ones of the PM, they are long enough for the expensive hourly rates of the larger vessels to increase the amplitude of the effect of Pw and Tw on the overall cost and revenues. The supplier travelling time from harbour to turbine, TtravHT, Supplier, has only a small influence on the turbine’s availability, jDj’ 0.5%, and a slight impact on the overall cost and revenues. The increase in the TtravHT, Supplier times reduces the effective working hours of the maintenance crew shift, which may imply, for example, one more day to conclude a maintenance task with the associated travelling and operation costs. To minimize this effect, the solution is to have two shifts per day since labour and supplier vessel hourly rates are much smaller. The variation on the travelling times, TtravHT, of the jack-up and crane vessels from the harbour to the turbine have nearly no effect on the turbine’s performance due to the following: the number of CM actions is much smaller than the PM ones – for optimal maintenance values strategy, they are ’ 0.35/year and ’ 3.1/year, respectively; the jack-up and crane vessels stay at the turbine, thus doing only two travels during the whole maintenance period. The effect is also similar when considering the times, TtravLVH, those vessels take to arrive at the harbour after mobilization given that this cost was assumed to be constant, the number of CM is small and the hourly cost of loss production (i.e. e381/h) is much smaller than the hourly rates of the larger vessels. Additionally, varying the time needed for a spare pitch system to be available has almost no effect on the turbine’s performance given its short period, low number of CM and the comparatively low hourly cost of loss production. These parameters are not shown in any figures given that they have little impact. A limitation of this work is due to the simplified Pw and Tw values, which are not based on correlated weather and sea-state data since these are not always available on public databases. More advanced models are provided, for example, by Rademakers and Braam16 and Feuchtwang and Infield.30 Moreover, the operational limits of the vessels and maintenance tasks
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were assumed to be the same. Nonetheless, and even with such simplification, choosing more favourable weather and sea-state conditions is very important to reduce financial risks in a wind farm project.
Conclusion An O&M strategy consisting of CM replacements and age-based imperfect PM repairs was modelled, simulated and optimized using GSPN with predicates coupled with MCS. Optimal values were obtained for two decision parameters: preventive repair threshold and age reduction ratio. Keeping these values fixed, baseline values of failure, repair, logistic and weather parameters were varied to assess the effect on the turbine’s performance (i.e. mean availability, overall cost and revenues). The variation of the MTTF is the most influential parameter with large effects on the downtime, production losses and O&M costs; this last due to CM replacements which are performed by costly larger vessels. In terms of the turbine’s availability, the effect of the MTTR is close to that of Pw and Tw. However, this last influences more the overall cost and revenues due to the off-summer large waiting times to access the turbine for CM, thus the expenditure with the hourly costly large vessels. In all, developments for better operation of offshore wind turbines must be set on improving the accessibility and MTTR. Enhancing wind turbine designs, use of proper maintenance strategies and faster maintenance vessels with more favourable operational limits are requirements for such objectives. This gets even more important as wind farm projects are moving to greater depths and farther from shore, thus to harsher environments. The model presented in this article can be extended from dealing with one turbine to a farm of turbines by reproducing the individual turbine model as many times as the number of turbines in the farm. Declaration of conflicting interests The authors declare that there is no conflict of interest. Funding This work has been initiated within the project Marine Renewable Energy - Energy Extraction and Hydroenvironmental Sustainability (MAREN), and was completed in the project "Hydro-environmental modelling of multi-purpose marine renewable energy platforms" (MAREN2) both of which are partially funded by the Atlantic Area Programme.
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