Constrained evolutionary wind turbine placement with penalty functions

Geographical constraints are essential when planning the locations for wind turbines. In real-world scenarios, especially in densely populated countries, the designated area where turbines can be placed is not an empty map on which the turbines can be placed arbitrarily. Even in rural areas, streets, buildings, and rivers have to be considered. In this paper, we model two constrained turbine placement scenarios and use evolutionary algorithms to find optimized turbine locations. To evaluate the locations, we combine a proven wind model with real-world data of a wind prediction model from a meteorological service. Geographical data from a free map service is used to define constrained areas in the scenarios based on administrative rules. For the evolutionary optimization process, we consider five ways to handle penalties. Starting with a simple specification that can only achieve two different values, we end up in a definition that considers distances relative of the required minimum distances to all geographical objects for each turbine. We combine the penalty definitions with three types of penalty functions. In the experimental section, we compare the various configurations and show a detailed analysis of the results.