GIS

LCP

2D – view on our 3D high-res digital elevation model (DEM) of a project area. White exclusions are „no-go-areas“, like e.g. natural protected zones. (Extract of the results of the master thesis of Olli Jakobsson)

GIS-BASED APPROACH FOR OPTIMIZATION OF ONSHORE WIND PARK INFRASTRUCTURE 

In a cooperating with the University of Turku (UTU), Finland, during scientific studies were methods for computer based optimization of wind park infrastructure developed, resulting in a master thesis of our student colleague, Mr Geographer Olli Jakobsson.

Background

  • Infrastructure costs of wind parks are generally in the range of 10 – 15 percent of the total project costs. On an absolute scale, infra costs might reach tens of millions of euros, depending on project size.
  • Infrastructure alignment planning is generally carried out by civil engineers using traditional and subjective work methods, such as field observations and manual drawings.
  • A path optimization algorithm offers a more objective, computational method for infra alignment. Combined with a high-resolution LIDAR terrain data, it can be a powerful tool to support infra alignment planning.

Objectives

  • Establish a methodology for determining optimal paths to support the process of wind park infrastructure alignment planning in GIS environment
  • Develop combined uses of high-resolution terrain data in a least-cost path (LCP) analysis by using multi-criteria evaluation (MCE) methods
  • Identify optimal paths for the alignment of infrastructure networks based on multiple different optimization objectives within the study area

Data

  • High-resolution terrain data (elevation, vegetation height) through LIDAR scannings, vertical resolution: 0,18 meters, horizontal resolution: 1,0 meters
  • Planning data according to the master plan (turbine locations, project area, restrictions areas)
  • Road requirements for modern wind turbines (max slope gradient around 8-12% or 5-7 degrees)

Experience 

  • The model successfully optimised road and electric grid total lengths by taking advantage of newest LIDAR and GIS technology.
  • The model is very flexible can integrate existing roads, avoiding ”no-go areas” e.g. like nature protected areas.
  • The implementation of different scenarios creates a good overview of existing options
  • It was possible to differentiate if to focus either more on roads or on grid optimization