Understanding the spatiotemporal complementarity of wind and solar power generation and their combined capability to meet the demand of electricity is a crucial step towards increasing their share in power systems without neglecting neither the security of supply nor the overall cost efficiency of the power system operation. This work proposes a methodology to exploit the complementarity of the wind and solar primary resources and electricity demand in planning the expansion of electric power systems. Scenarios that exploit the strategic combined deployment of wind and solar power against scenarios based only on the development of an individual renewable power source are compared and analysed. For each scenario of the power system development, the characterization of the additional power capacity, typical daily profile, extreme values, and energy deficit are assessed. The method is applied to a Portuguese case study and results show that coupled scenarios based on the strategic combined development of wind and solar generation provide a more sustainable way to increase the share of variable renewables into the power system (up to 68% for an annual energy exceedance of 10% for the renewable generation) when compared to scenarios based on an individual renewable power source. Combined development also enables to reduce the overall variability and extreme values of a power system net load.

Short-term forecasting and diagnostic tools for severe changes of wind power production (power ramps) may provide reliable information for a secure power system operation at a small cost. Understanding the underlying role of the synoptic weather regimes (WRs) in triggering the wind power ramp events can be an added value to improve and complement the current forecast techniques. This work identifies and classifies the WRs over mainland Portugal associated with the occurrence of severe wind power ramps. The most representative WRs are identified on compressed surface level atmospheric data using principal component analysis by applying K-means clustering. The results show a strong association between some synoptic circulation patterns and step variations of the wind power production indicating the possibility to identify certain WRs that are prone to trigger severe wind power ramps, thus opening the possibility for future development of diagnostic warning systems for system operators use.