Assessing the impact of sampling and clustering techniques on offshore grid expansion planning

Abstract

Due to the ongoing large-scale connection of non-dispatchable renewable energy sources to the power systems, short- to long-term planning models are challenged by an increasing level of variability and uncertainty. A key contribution of this article is to explore and assess the implications of different dimension reduction approaches for long-term Transmission Expansion Planning (TEP) models. For the purpose of this study, a selection of sampling and clustering techniques are introduced to compare the resulting sample errors with a variety of sampling sizes and two different scaling options of the original data set. Based on the generated samples, a range of TEP model runs are carried out to investigate their impacts on investment strategies and market operation in a case study reflecting offshore grid expansion in the North Sea region for a 2030 scenario. The evaluations show that dimension reduction techniques performing well in the sampling and clustering process do not necessarily produce reliable results in the large-scale TEP model. Future work should include ways of incorporating inter-temporal constraints to better capture medium-term dynamics and the operational flexibility in power system models.