Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligencePower system modelling affects decisions on over $450 billion worth of assets world-wide each year. While complex and computationally demanding models, when properly simplified a balance between accuracy and simulation time can be achieved. Solutions and results for this thoroughly studied problem tend to be rather case-specific, and the Portuguese system presents challenges that make existing approaches insufficient. To better understand this system and how its peculiarities can be used to reduce its modelling complexity, a model of the Portuguese electricity system using PLEXOS software was developed and used to test the impact of different clustering techniques on the model’s output results. We show that including natural hydro inflow in the clustering to find representative days for a system where hydro generation plays such a large role can improve model output accuracy. This is typically ignored in the literature. Additionally, we demonstrate that using data disregarding daylight saving time changes can have an impact on results. Finally, we indicate that intraday downsampling might have limited effect on modelling accuracy, and open the way for future work on weighting clustering input dimensions differently to improve accuracy of representative days
