In the previous GIS blog we described some typical questions that GIS analysis is uniquely placed to answer. We also showed that a GIS analysis led by BVGA for WindEurope in 2017 showed how different areas in northern Europe vary in offshore wind LCOE.

So, how did we go about building this map?

LCOE is made up of three components that each vary geospatially: energy production, CAPEX and OPEX.

Energy production is primarily affected by wind speed. We used a turbine power curve, along with assumptions about losses, to calculate the average energy production.
For all models, skill and care are required when finding the right balance between detail and function. You need to identify and include the primary inputs, allowing second and third order effects as useful additions, but only within your budget for time and cost. For a large-scale analysis such as this, annual mean wind speed is the primary driver of energy production. There are several other spatial factors which we could include in the calculation of energy production – such as air density, wind speed distribution, seasonality and long-term variation – but which were left out of this exercise.

CAPEX is affected by the distance to port (installation cost), distance to grid (transmission cost) and the water depth (support structure cost, and installation cost). Second order inputs such as the impact of soil conditions and wave height were excluded from the models.

OPEX is affected mainly by distance to port. As well as cost increasing as vessels have further to travel, when the farm is far from port longer weather windows are needed for interventions.

For energy production, CAPEX and OPEX, we built functions which used the environmental variables as parameters. We built separate functions for three foundation types: monopile, jacket and floating. Using GIS software, we applied these functions to geospatial datasets. This allowed the geospatial LCOE to be calculated for each foundation type at each location, and therefore the lowest LCOE at each location to be determined.

With this output map we were able to analyse the LCOE variation. For example, we produced a merit order of LCOE that shows how many MW of offshore wind capacity are available for different average LCOEs. We were also able to describe these geospatial LCOE patterns to a range of wide range of stakeholders.

We’ll complete this series by talking about how including exclusions within the analysis helped to show practical, rather than optimistic values for WindEurope.

In the meantime, if you’d like to know about how our geospatial work can help your project minimize costs and maximise revenue, get in touch