By using multiple wind measurements when designing wind farms, it is possible to decrease the uncertainty of wind farm energy assessments since the extrapolation distance between measurements and wind turbine locations is reduced. A WindScanner system consisting of two synchronized scanning lidars potentially represents a cost-effective solution for multipoint measurements, especially in complex terrain. However, the system limitations and limitations imposed by the wind farm site are detrimental to the installation of scanning lidars and the number and location of the measurement points. To simplify the process of finding suitable measurement positions and associated installation locations for the WindScanner system, we have devised a campaign planning workflow. The workflow consists of four phases. In the first phase, based on a preliminary wind farm layout, we generate optimum measurement positions using a greedy algorithm and a measurement “representative radius”. In the second phase, we create several Geographical Information System (GIS) layers such as exclusion zones, line-of-sight (LOS) blockage and lidar range constraint maps. These GIS layers are then used in the third phase to find optimum positions of the WindScanner systems with respect to the measurement positions considering the WindScanner measurement uncertainty and logistical constraints. In the fourth phase, we optimize and generate a trajectory through the measurement positions by applying the traveling salesman problem (TSP) on these positions. The described workflow has been digitalized into a Python package named
The development of a wind farm project begins with an assessment of the wind resources and the energy yield for the planned wind farm. Best practices recommend estimating wind resources based on local wind measurements
The single mast approach is affordable but can cause large uncertainties. Specifically, in complex terrain (mountainous and forested areas), the spatial extrapolation becomes challenging as the topography can significantly influence the flow. The ideal scenario would be to measure the local wind climate at every planned wind turbine position. However, erecting as many masts as wind turbines would be extremely costly and in some areas impossible.
Many large wind farm projects in complex terrain are developed using multiple masts. Combining one fixed mast and one or several roaming profiling lidars moved to different positions during the campaign is another option. The advantage of roaming vertical profiling lidars lies in their ability to provide affordable high-altitude measurements, ease of deployment and no need for building permits in comparison to the masts, while data availability and inaccuracy in complex terrain
A potential solution for multipoint measurements for wind resource assessment lies in the application of scanning lidars
The biggest challenge when designing a multilidar measurement campaign is deciding where to measure and how to position scanning lidars to acquire accurate measurements. As mentioned before, we would like to measure at every future wind turbine location. Realistically, in the case of large wind farms (many wind turbines), this is not feasible. As the laser beams need to traverse each turbine location, there would not be enough measurement samples per location to do a proper statistical analysis of the wind resources. A good rule of thumb is to have at least 10 samples for each measurement point per 10 min period. Therefore, we would need an approach to minimize the number of measurement points such that we satisfy spatial and temporal coverage of the wind farm resources (i.e., enough samples and a good distribution of measurements across the farm site).
Afterward, constraints that arise from scanning lidars, atmosphere and site characteristics dictate the positioning of lidars. Indeed, the laser beam of scanning lidars can be steered freely, but, on the other hand, it can be blocked in some directions by the terrain, vegetation or other obstacles. This impacts the lidar positioning since we need an unobstructed passage of the laser beams towards measurement points, i.e., clear line of sight (LOS). On the other hand, the lidar characteristics (e.g., laser wavelength and output power) in combination with the atmosphere characteristics (e.g., aerosol extinction, backscatter coefficient and atmospheric attenuation) impact the lidar range. Furthermore, retrieving the 2-D wind vector requires a limited beam elevation angle (e.g., smaller than 5
Once the lidar positions are set and measurement points determined, generating an optimized trajectory is essential to reduce the motion time from one measurement point to another, and thus boost the sampling rate per measurement point. Overall, at the same time, a campaign designer has to handle several constraints to find the best measurement locations and accordingly generate the best possible measurement campaign layout, or they can decide that a measurement campaign with scanning lidars is not practical.
In this paper, we describe a workflow which tackles the above-described challenges involved in the planning of scanning lidar campaigns together with an approach of the workflow digitalization. The workflow is based on the application of the methodology for multilidar experiments on wind resource assessment campaigns
The paper is organized as follows. Section 2 provides a detailed description of the four main phases of the workflow. Additionally, Sect. 2 provides a recipe for the workflow digitalization. In Sect. 3 we present results of applying the digitalized workflow for planning campaigns at three wind farm sites that are different in size, layout and complexity. Results and further development of the workflow are discussed in Sect. 4, and we provide our concluding remarks in Sect. 5.
We assume that the location and the layout of the wind farm are known. This initial information is input to the campaign planning workflow, which consists of four sequential phases graphically represented in Fig.
Campaign planning workflow (figure designed using © freepik.com icon database).
This first phase of the workflow tackles challenges of measurement point optimization. We assume that the wind farm site has been selected and that a preliminary resource assessment and wind farm layout have been made before the campaign planning.
For small wind farms (either a limited number of turbines and/or a limited spatial extent), we consider the wind turbine positions as the measurement positions. For larger wind farms, the number of measurement points needs to be reduced. However, the reduced set of measurement points should be adequately distributed over the wind farm site to avoid long wind resource extrapolation distances.
The simplest approach is to group the wind turbine locations, which are close to each other in clusters and to assign a single measurement location per cluster.
In our case, the greedy algorithm implementation yields the set Initialize the set For any unique pair of wind turbine positions (there is Find the elements of the set Select a disk from the set The disk Points covered by Steps 1 to 6 are repeated until either the set If
At the end of this process, elements of the set
A stack of data used to generate the GIS layer for lidar placement:
In this phase, we will create a Geographical Information System (GIS) layer which includes site and lidar constraints while highlighting the best lidar installation locations considering the previously determined measurement points. We will denote this GIS layer as the combined layer and treat it as a set
At present we consider five types of constraints which are detrimental for a lidar installation: zones where a lidar cannot be installed (e.g., lakes, forests, etc.); topographical features that can block the beam; keeping the lidar elevation angle below a certain threshold to avoid measurement contamination with the vertical component of the wind; the maximum lidar range; practical matters such as access roads. To create the combined layer which contains all the above-listed constraints, first we will generate GIS layers for each constraint and afterward merge them. These individual GIS layers are (1) exclusion zones layer, (2) LOS blockage layer, (3) elevation angle layer, (4) lidar range layer and (5) logistical layer.
To create the exclusion zone layer we will use the land cover data and according to the land cover type (e.g., water surface, forest, etc.) classify areas of the site as suitable or not for a lidar installation. The land cover data can be treated as a set Lc of equally spaced elements Lc
Fictive exclusion zone layer represented as an array:
To generate the LOS blockage layer we need to create a dataset that contains the summed height of terrain and canopy. To do this we will add 20 m at each location in the DEM dataset where the CORINE Land Cover dataset contains code which corresponds to the forest (
Our focus is to design a dual-Doppler measurement campaign to retrieve the horizontal wind speed. Accordingly, a low elevation beam angle is required to avoid contamination of the LOS speed measurement with the vertical component of the wind vector. We create the elevation angle layer to serve this purpose. This layer is created through the following steps: we make a copy of the mesh, walk through each mesh point, fetch the height information from the DEM dataset, calculate the elevation angle from the mesh point to each measurement point, compare the calculated angle to a threshold value (e.g., a maximum of 5
In the lidar range layer, mesh points contain measurement points that are within reach of the lidar taking into account the expected range of the lidar for the given site. It is worth noting that the expected range is not the maximum range given in the product data sheet. The layer is created similarly to the previous one.
To create the combined layer, we will treat the four previously derived layers as sets
Therefore, the mesh points of the combined layer will contain which and how many measurement points are reachable considering the first four above-described constraints.
Finally, the acquired aerial image of the site (the logistical layer) is kept separately and will serve the important purpose of identifying existing road and power infrastructure.
The combined layer together with the underlying aerial image highlights the “best” locations for the placement of individual lidars considering all the above-described constraints. However, designing the campaign for a dual-Doppler system, where beams from two lidars need to synchronously cross every measurement position, adds one more constraint, which is the limitation on the beams intersection angle. The measurement uncertainty of a dual-Doppler system increases when the intersecting angle between the laser beam becomes small (see
The fourth phase consists of the optimization of the path through the measurement points (to boost the sampling rate) and the generation of the synchronized trajectories for two scanning lidars.
One way to optimize the path through the measurement points is to adapt the traveling salesman problem (TSP). In the regular TSP, the goal is to find the shortest path through a set of This heuristic for constructing a traveling salesman tour is near at hand. The salesman starts in some city and then visits the city nearest to the starting city. From there he visits the nearest city that was not visited so far, etc., until all cities are visited, and the salesman returns to the start.
In our case, we have a single set of measurement points
Let us consider that the set
The adapted TSP NNH solution for dual-Doppler trajectory can be described in the algorithmic sense with the following steps:
Initialize empty sets Select an arbitrary index Set an element Select elements Add the elements Calculate sets Form a set Find index Repeat steps 3 to 8 until the sets
The main modification of a standard TSP NNH solution is the addition of Step 7, which ensures that the trajectory will be optimal for both lidars instead of only one. The difference between the standard and adapted TSP NNH solution can be seen from an example shown in Fig.
TSP NNH:
To get the lidars to follow the optimized trajectory, we need to describe the motion of the scanners as a function of time. In other words, we need to add the time component of the trajectory to the spatial description we yielded in the previous steps. When calculating the timing for the trajectory, we assume that the lidars will stop at each measurement point and sample wind speed before they continue to the next measurement point. Therefore, we expect that lidars will perform so-called step-stare trajectories. There are several reasons for selecting step-stare trajectories instead of continuously scanning the flow through the trajectory described by the measurement points. Step-stare trajectories are simpler for the implementation and synchronization of multiple lidars; also, the step-stare data processing is less complex when compared to the continuously scanning trajectories.
The timing for the step-stare scans can be calculated using a simple solution for the kinematics elevator problem (KEP)
In the case when rotational axes of the scanner head have different kinematic limits, it is advisable to use limits that are more conservative (i.e., lower maximum allowed acceleration and speed).
Since we have two lidars that move from one to another measurement point, we will generally have two different moving times to perform angular motions. To keep the lidar measurements in sync, we take the maximum of the two derived values.
The previously described workflow has been digitalized using Python and a set of public Python libraries resulting in a Python package
In this section, the campaign planning workflow is demonstrated through the application of
The only information needed for each site is the wind turbine positions and their hub height. This input could be generated arbitrarily, but to make the examples realistic the actual operational wind farms have been chosen.
For all three sites, we aim to design the campaign for the long-range WindScanner system configured in a dual-Doppler mode (i.e., the system will have two scanning lidars). The system is described in detail in
The Scottish site consists of 22 wind turbines with 47 m hub heights and has a quite compact layout (Fig.
The aerial image of the Scottish site. Aerial data: © Google Maps, DigitalGlobe.
Due to the compact design of the wind farm, we decided to skip the measurement position optimization and try to generate a measurement campaign in which we intend to measure at every wind turbine position. Considering that the site is relatively close to the coast, surrounded by agricultural land and the altitude is about 300 m a.s.l., thus relatively low, the site should experience a good concentration of aerosols. Nevertheless, we cannot expect that the WindScanner lidars will have a 6 km range all the time and assume that on average the WindScanner lidars would have a range of at least 3 km at the selected site (i.e., half of the maximum claimed range). This estimation is based on our experience in doing measurement campaigns at various locations and in different atmospheric conditions.
Using this range together with the map extent,
Placing lidars at the Scottish site:
As explained in Sect. 2 (Phase 3), the first lidar placement is instrumental for the second lidar placement because of the intersecting angle between the respective lidars' beams. There is only one area of the map where the placement of the second lidar assures that all measurement points are within reach and measurable with fair accuracy (bottom image in Fig.
Since we have both the measurement and lidar positions, we have all the elements to optimize and generate the trajectory. Figure
Final campaign design at Scottish site.
Considering the kinematic limits of the scanner head and that we are performing step-stare scans, we can apply the elevator kinematic problem on the trajectory points. This step yields the required time to move the scanner heads from one point of the trajectory to another, which in our case is about 14 s for the entire trajectory with an additional 22 s for measurements. Overall, one complete scan of all measurement points will take about 36 s, which results in about 16 samples of each measurement point per 10 min period. Typically we aim at having at least 10 samples per 10 min period which is satisfied with this configuration.
To verify that the optimized trajectory indeed takes the least amount of time, we have calculated the motion time for
A histogram of motion time for
One way to gauge the value of a WindScanner campaign is to estimate how much the uncertainty of the annual energy production (AEP) is reduced compared to a single met mast campaign. Since the flow model can account for a large part of the total uncertainty in complex terrain, a reduction in the extrapolation distance (i.e., a distance between a turbine and measurement position) gives a reduced flow model uncertainty. A method for estimating the flow model uncertainty has been published by
We will consider a single centrally placed met mast and calculate an average
The Italian wind farm consists of 36 wind turbines with a 78 m hub height. The turbines are distributed over a large area (see Fig.
The aerial image of the Italian site. Aerial data: © Google Maps, DigitalGlobe.
Measurement locations for Italian site: black dots – wind turbine positions, gray circles – disks covering wind turbine positions, red dots – optimized measurement positions (i.e., disk centers).
Given the specific layout of the wind farm, having more or less isolated groups of tightly packed wind turbines, we decided to apply the measurement point optimization. For this wind farm, the representativeness radius was set to 500 m, which is 4 times smaller than the maximum suggested value for the complex terrain sites
From there, the workflow was applied in the same way as it was for the Scottish site. In comparison to the Scottish site, the Italian wind farm is even closer to the sea, and it is surrounded by an urban area that in our experience increases the aerosol concentration resulting in an improved lidar range. Therefore, for the Italian site, we can expect to have an average measurement range of 4 km for the WindScanner systems. The combined layer generated using
Placing lidars at Italian site:
Considering the positions of WindScanner systems, reachable measurement points and kinematic limits, we derived an optimum trajectory through the measurement points and calculated the timing for the synchronized scanner head motion (Fig.
Final campaign design for Italian site.
To verify that the optimized trajectory is indeed the shortest in duration, we have generated all possible trajectories by making permutations of trajectory points. Unlike for the Scottish site, for the Italian site this is feasible since the trajectory contains only eight measurement points, thus there is 40 320 (8!) unique trajectories. For each trajectory, we have calculated the total motion time considering that the lidars will be operated in sync. The results are shown in Fig.
A histogram of motion time for 40 320 different trajectory configurations for the Italian site. The min, mean, max and standard deviation are 11.93, 18.14, 22.66 and 2.1 s, respectively.
Like in the previous example we will calculate the averaged extrapolation uncertainty considering a single centrally placed met mast (
The Turkish wind farm consists of 22 wind turbines with an 80 m hub height. The wind farm extends 8 km from north to south (see Fig.
The aerial image of the Turkish site. Aerial data: © Google Maps, DigitalGlobe.
For this site, we assumed the average lidar measurement range to be 3 km, and we used the representativeness radius of 400 m. Our assumption on the average range in the case of the Turkish site is probably closer to what a lidar would probably achieve in a field operation (thus less conservative) due to operation in high altitude where we usually experience low aerosol concentration and often low clouds and fog. On the other hand, the selected representative radius is 100 m lower than in the case of the Italian site, thus about 5 times smaller than the recommended value by MEASNET. Running the workflow using these parameters we generate a measurement layout with 10 measurement positions (see Fig.
Measurement locations for the Turkish site: black dots – wind turbine positions, gray circles – disks covering wind turbine positions, red dots – optimized measurement positions (i.e., disk centers).
Placing lidars at the Turkish site:
There are only a few good locations for placing the two lidars, due to the wind farm length (8 km) and the lidar average range (3 km). Once again the best solution is to place the lidars in the middle of the wind farm. The top image of Fig.
Knowing the first lidar position leads us to the generation of the second lidar placement layers. From the bottom image in Fig.
The designed WindScanner layout can provide measurements in 4 out of 10 measurement points that cover the middle part of the wind farm (Fig.
Final campaign layout for Turkish site.
Considering the WindScanner systems and measurement locations together with the kinematic limits as the input for the last phase of the workflow we reach the optimized trajectory. The trajectory total time is 13.26 s of which 4 s are spent on the wind speed measurements. This trajectory would provide about 43 samples of each measurement point within a 10 min period. Similar to the Italian site, to verify that the optimized trajectory is the shortest one, all possible trajectories have been generated (the total of
We will repeat the same analysis of the extrapolation uncertainty as in the case of the previous sites. Considering a single centrally placed met mast the average extrapolation uncertainty
The primary purpose of the described workflow is to design dual-Doppler measurement campaigns for wind resource assessment (WRA). This scope follows the RECAST project ambition which is focused on developing a new way of performing WRA based on multiple measurement points using WindScanner systems. This has driven the choice of examples for Sect. 3 of this paper. However, the workflow and its digitalized version (i.e.,
Planning the measurement campaign thoroughly especially with such complex instruments as scanning lidars ensures higher data availability during the campaign and eventually saves time and money. Lidars are very mobile and allow agile measurement campaigns compared to a met mast, but too often the ease of deployment is mistaken with a limited (underestimated) need of planning. This study provides solutions for optimizing measurement points, lidar positions and measurement trajectory which represent a necessary foundation for accurate measurements of wind resources over the wind farm site. We have demonstrated that the above-stated optimization leads to reduced trajectory timing and thus an increase in the sampling rate.
The point of the workflow and the corresponding Python package is also to carefully consider the relevance of using scanning lidars for a measurement campaign. We have shown that for large sites one set of two WindScanner systems cannot measure over the whole wind farm area. This is very important to realize at the campaign planning stage when there is still time to either give priority to one part of the site or consider using a second set of two WindScanner systems to cover the rest of the site.
Nevertheless, with a rather simplified approach in the assessment of the AEP uncertainty, we have shown that even when WindScanner systems cannot cover the entire site, still a reasonable reduction of the uncertainty should be expected. Specifically, based on the presented examples, using multilidars and optimizing measurement and lidar positions on average the horizontal extrapolation contribution to the AEP uncertainty can be reduced from 2 % to 5 % compared to a single mast campaign.
The presented workflow can already solve many important challenges regarding the scanning lidar deployment. Nevertheless, we envisage a further development of the workflow and thus the Python package
In the current application of the workflow, we were predicting the lidar range based on our experience. We plan to develop a Python package that will be able to predict the lidar range using external databases of global atmospheric visibility for a given site. In the mean time, our suggestion when planning the lidar campaign is to generate campaign layouts considering a conservative approach in which the expected range of the lidar should be in the range from 75 % to 50 % of the claimed range by the lidar manufacturers. Directly connected to the range prediction is the development of a Python package which will be able to predict the lidar data availability at any desired range during the planned measurement period taking into account, for example, the cloud height, fog or mist occurrence from the Weather Research and Forecasting (WRF) model.
Furthermore, the proposed approach in optimizing measurement positions will be extended by considering other criteria for finding measurement positions apart from the representativeness radius. These are, for example, terrain elevation, speed-up factors, roughness changes, local obstacles, etc. In principle, we will strive to incorporate as many factors as possible that can cause local changes in the flow. In other words, the optimization of measurement positions will consider drivers of flow model uncertainty when finding measurement positions.
Finally, eye safety has not been considered in the presented workflow. In the next iteration of the workflow, we will incorporate these issues as yet another restriction zone (GIS) layer for the placement of lidars.
This paper provides an exhaustive description of a workflow for planning and configuring scanning lidar measurement campaigns. The purpose is to find the most suitable measurement positions (considering a preliminary wind farm layout), survey scanning lidar placements considering lidar and site constraints to secure accurate measurements, and optimize scanning lidar trajectory to boost the number of measurement samples. The presented workflow can help to avoid many pitfalls that can be predicted before the start of the campaign, limiting the risks to the campaign itself. The workflow, in its digitalized version, has been demonstrated for planning campaigns for resource assessment for three different sites. For a small wind farm layout, a dual-Doppler system could be positioned such that measurements could be made at all turbine positions. For the other larger sites, the number of measurement points had to be optimized and a set of two lidars could only cover some parts of the sites. Nevertheless, for all examples, we have demonstrated that using the proposed workflow a significant reduction of the trajectory timing and AEP uncertainty are achievable.
Data described in Sect. 3 of the paper are available as a data collection at
NV, AV and AB performed the conceptualization of the presented work. NV detailed the methodology from the concept, developed software with help from AV, provided background resources together with AB, made visualization of results, handled data curation for the presented study and performed the formal analysis of derived results. All authors contributed to writing the original draft and performed review and editing during the peer-review process. RW and AB acquired funding for the presented work.
The authors declare that they have no conflict of interest.
The authors would like to acknowledge Morten Thøgersen (EMD) for his support during the conceptualization phase of the study described in the paper
This research has been supported by the Innovation Fund Denmark (grant no. 7046-00021B).
This paper was edited by Joachim Peinke and reviewed by Ines Wuerth and two anonymous referees.