|Institution:||KTH Royal Institute of Technology|
|Keywords:||Engineering and Technology; Electrical Engineering, Electronic Engineering, Information Engineering; Other Electrical Engineering, Electronic Engineering, Information Engineering; Teknik och teknologier; Elektroteknik och elektronik; Annan elektroteknik och elektronik; Civilingenjörsexamen - Farkostteknik; Master of Science in Engineering - Vehicle Engineering|
|Full text PDF:||http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168390|
The installed wind power capacity has increased rapidly over the last decades and wind power now has a strong impact on the European electric system. The development of wind power is expected to continue in the coming decades and it is therefore crucial to correctly take it into account in network simulations of the future system. Metrix is a model used at EDF R&D for technical and economical simulations of the European electric system. It uses a multi-scenario approach that aims at calculating different possible states of the network for a chosen moment in the future. Until this master thesis, the generation of the wind farms was the same in all the scenarios. This does not reflect the high variability of wind power generation and does not allow to correctly simulate the effects of wind power on the system. The goal of the project presented in this report is to integrate a multiscenario approach of wind power with spatial variations in Metrix in order to represent a range of wind power situations that is representative of what might happen at a simulated moment in the future. The chosen method consists of using wind scenarios from the past, applying them to the future wind park and integrating them in the scenarios used in the simulations. The database of wind situations used in this project allows to have 13 wind zones over Europe. An analysis of the seasonal and diurnal cycles of wind power generation is performed for the abovementioned purpose. The methodology is applied to the study of the winter peak and leads to the choice of up to 1092 suitable wind power scenarios. Then, statistical methods are used to estimate the number of scenarios that is necessary to reach the desired accuracy in the simulation results. Finally, the benefits of the proposed approach of wind power are demonstrated by showing how it allows to analyse the impact of wind power generation on different system quantities and components.