The post which describes the background to the project, also contains links to related posts.
The core functionality of Doris is the facility to make estimates of the output of wind and solar generators using aviation weather reports (Metars). It is written in Python 2.7 and accesses historic weather data stored in an SqLite3 database. The code is modified explore a range of configurations and scenarios, in this case climate.
When I first started seeking out data on wind and solar resources, it became apparent that it was difficult to make like-for-like comparisons, this was largely due to variations in climate and terrain. It's easy to look for an explanation in maths and stats, but a quick look through the travel section of a newspaper will provide an answer. Many North Europeans take their holidays in Spain because of the clear skies and sunshine and few South Europeans trek north to in mid winter because of the wind and cloudy skies. For wind turbines terrain is important, the ideal location for a turbine is on a ridge which is at right angles to the prevailing wind, when they are placed in valleys and urban areas they may not perform well. Also the distribution over a country can vary considerably, for example in the UK, the average wind speed in exposed western coastal areas is higher than sheltered ones in the east. When comparing the experience of different countries, the variations in climate should be taken into account.
The base case for Doris is a location in the south of England (Koppen climate type: Cfb) with 1 kw each of wind and solar generating capacity. The post "You have to do both" contains some discussion of a wind/solar based system might behave in this climate.
For this post, I've deliberately chosen some extreme and contrasting climates. In the context of Doris, variations in climate come from the location which is used to provide estimates of clear sky solar irradiance, a crude model which provides some allowance for variations in cloud cover and weather reports from a local airfield.
The first graphic comes from a run where the base configuration was run with data from hot desert region (Koppen climate type: Bwh). In this case, solar makes the greatest contribution to meeting the load, but at 35 deg. North of the equator, there is enough season variation to draw in supplies from the grid, the average wind speed in this place was low, so the yield from wind was low.
Based on the results from the base configuration, a possible adaption was to remove the wind generating component and double the solar capacity, the effect was to significantly reduce the draw down form conventionally generated sources.
At the other extreme is a northern continental area (Koppen climate type: Dfc) at a latitude of approximately 65 deg. There is significant variation in the yield from solar over the year, this drops to zero in December and January, but provides a useful contribution in summer. This arbitrarily selected location is not very windy and the contribution from wind even in winter is low resulting in a large draw down form the grid.
A logical adaption was to triple the wind generating capacity and halve the solar element, this still did not make a major reduction in grid draw down.
Since I have been studying sustainable energy sources, there is a recurring issue which might be termed the "winter problem". In the northern hemisphere the demand for electricity peaks in the winter months whilst the supply can be low due to low solar radiance and periods of calm.
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