Abstract
Abstract. 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 campaign-planning-tool, which gives users an effective way to design measurement campaigns with WindScanner systems. In this study, the Python package has been tested on three different sites characterized by different terrain complexity and wind farm dimensions and layouts. With minimal effort, the Python package can optimize measurement positions and suggest possible lidar installation locations for carrying out resource assessment campaigns.
Highlights
The development of a wind farm project begins with an assessment of the wind resources and the energy yield for the planned wind farm
Measurement campaigns designed for wind resource assessment have historically relied on anemometers and wind vanes mounted on tall meteorological masts to measure a wind climate similar to the wind climate the wind turbines will experience during their lifetime
By solving a disk-covering problem (e.g., Biniaz et al, 2017), in which we aim to find a minimum number of disks with a radius equal to representativeness radius” (Rr) that cover all locations of wind turbines, we cluster the wind turbines and optimize the measurement locations
Summary
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 (MEASNET, 2016). Measurement campaigns designed for wind resource assessment have historically relied on anemometers and wind vanes mounted on tall meteorological masts ( called met masts) to measure a wind climate similar to the wind climate the wind turbines will experience during their lifetime. To account for the seasonal and interannual variations of the wind, the observed wind climate is long-term corrected using long-term reference data from a nearby meteorological station, reanalysis data or mesoscale models (Carta et al, 2013). The longterm-corrected wind climate is extrapolated vertically and horizontally, typically using a flow model such as WAsP (Mortensen et al, 2014) to estimate the wind resource at hub height for every wind turbine location
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