Economically-Limited Onshore Wind Capacity and Production in European Nations

In the context of meeting CO2 emission reduction targets to fulfill climate action plans, expansion of renewable energy sources into future energy systems represents a common research theme. Onshore wind turbines currently... [ view full abstract ]

In the context of meeting CO₂ emission reduction targets to fulfill climate action plans, expansion of renewable energy sources into future energy systems represents a common research theme. Onshore wind turbines currently hold one of the highest shares in renewable electricity generation worldwide, with the second highest growth over the last 25 years, which will surely continue in the future. It is well known that electricity generation from wind turbines is characterized by spatially-sensitive intermittency, and furthermore that the placement of turbines is strongly influenced by the sociotechnical criteria; such as proximity to settlements, terrain suitability, and conservation efforts. Nevertheless, wind scenario studies commonly do not account for all of these concerns in detail; typically over simplifying the impact of sociotechnical criteria on the final distribution of a desired capacity across a study region. To improve upon this deficiency, the work described here incorporates contemporary techniques for the simulation of hourly wind turbine performance at large spatial scales in coordination with land eligibility concerns.

Using the European continent as the study region, the applied method proceeds as follows. First, every 1 km² location in Europe is simulated at the hourly level considering multiple turbine models and 36 weather years (Figure 1). A previous land eligibility result is used along with a turbine placement algorithm to identify the maximal number of turbines which can be placed in the available areas with a minimal distance of 850 meters enforced between turbines. The placements are then matched to their expected FLH, and a cost model is used to determine the best turbine model and the associated levelized cost of electricity (LCOE) for each placement following the approach of Robinius et. al. Ordering by cheapest LCOE, the average FLH and LCOE of each country is determined as a function of installed capacity (sample trends displayed in Figure 2). By choosing an economically-constrained average LCOE of 11, 9, 7, and 5 Euro-ct/kWh, it finally becomes possible to determine the economically-limited capacity and production within each of the evaluated countries, shown in Table 1.