Wind Energy Prospecting
With global energy trends moving towards sustainable solutions, energy explorers are seeking new ways to effectively search for potential wind farm sites. In the past, wind prospecting has been undertaken using limited tools and data. Explorers are often left asking if all the sites are taken, and if not, where will the next new site be? Our new advanced spatial modelling tools can provide a fresh perspective on these questions. Using sophisticated meso-scale wind speed modelling, advanced terrain analysis, and current land use knowledge, we undertake probabilistic analysis and intelligent spatial modelling of the wind energy potential in a country or region.
For our wind prospecting work to date, Kenex have collaborated with Aurecon, a leading global group of engineers created by the recent coming together of three world-class companies namely Africon, Connell Wagner and Ninham Shand. Aurecon have provided Kenex with mesoscale wind speed data as well as technical expertise on wind farm terrain and design. Kenex expertise in spatial data analysis and modelling is used to apply this knowledge to predictive targeting for ideal wind farm sites.
Together, Kenex and Aurecon have developed spatial modelling techniques that combine wind speed and direction data, advanced terrain analysis, and land use variables to define the extent of potential wind farms at regional and country-wide scales. Our modelling can quickly and cost effectively target and rank new wind farm opportunities and define the potential extent of an individual wind farm. It allows for easy approximation of the number of turbines that the wind farm can hold, can be a guide for turbine and monitoring mast placement, and can be used to identify the land owners that need to be approached early on in the development process. This is the most comprehensive spatial modelling method being used for wind energy prospecting.
Wind prospecting targets over the Manawatu Gorge, NZ. The model results are validated by existing turbines at wind farms on both sides of the gorge
We use Fuzzy Logic, a modelling method used to combine spatial data using expert knowledge. With this method predictive variables are weighted and statistically combined so that every variable has its relevant influence on the model output. This provides a much more integrated result than just overlaying or masking variables from the initial data, i.e. the wind speed grid. The fuzzy logic technique requires the creation of classified grids (predictive maps) for each variable. Each grid class is weighted using a fuzzy membership function (a value between 0 and 1), which expresses the degree of importance of the various map layers as predictors for wind farm locations. The predictive maps are combined using a combination of the fuzzy functions (AND, OR, SUM, product and gamma), to produce a final map that highlights potential wind farm locations and takes into account all of the input variables.
Our modelling has been used by Genesis Energy in New Zealand to locate new sites for wind farm development and determine possible turbine layouts at current prospects. It is also being considered by a major Australian electricity provider to assess a new wind farm layout and undertake state-wide modelling for new projects throughout several states in Australia.
Furthermore, the development of new business opportunities in the Argentinian Renewable Energy Market has been a key project for Kenex during 2011, with the rapidly growing business relationship fostered with Buenos Aires based company Emprendimientos Energéticos y Desarrollos S.A (EEDSA). Read more about Kenex and EEDSA projects in Latin America.
