An historic weather data based study to analyse the potential integration of solar and wind power in North West Europe with and without demand response.
There is an increase in renewable energy production worldwide. Higher integration of solar and wind power in the energy mix means that at times there is a mismatch between supply and demand. For example, on a windy and sunny day, there might be overproduction of wind and solar energy while on a cloudy windless day, there might be not enough electricity. In this thesis I investigated location optimization of wind and solar assets in Northwest Europe so that they could best match the energy demand.
9 years of weather data was used from the ECMWF.
From the weather data, both solar and wind power was calculated using Python.
The energy demand was obtained from the Belgium, Denmark, Germany, Luxembourg, the Netherlands, Norway, Ireland, United Kingdom.
The hourly energy production and demand were combined in this step performed in R.
Portfolio analyses was used to determine the highest covariance between the location of wind and solar power and energy demand.
The results of the portfolio analyses showed that by changing the location of solar and wind assets, the covariance between renewable energy production and demand can be increased. However, the maximum covariance point does not overlap with the maximum correlation point. This means that strategically placing of solar and wind assets can have a positive influence on the relationship between demand and production.Click here to read my MSc. Thesis