• Energy Research

<p>Power system expansion models are a widely used tool for planning power systems, especially considering the integration of renewable resources. Studies using these models form the basis for far-reaching political decisions. The backbone of power system models is an optimization problem, which depends on a number of economic and technical parameters. Although these parameters contain significant uncertainties, a consistent way to quantify the sensitivity to these uncertainties does not yet exist. Here, we analyze and quantify the sensitivity of a power system expansion model to the meteorological parameter time series based on a novel misallocation metric. We find that the sensitivity to the weather data is in the same order of magnitude as the sensitivity to the definition of cost. By comparing different climatic periods both from a meteorological perspective and with respect to the impacts on the optimal power system design we can, additionally, identify representative weather years and periods which should rather not be used for expansion planning.</p>

Abstract Increasing amounts of high-velocity spatio-temporal data reinforce the need for clustering algorithms which are effective for big data processing and data reduction. As currently applied spatio-temporal clustering algorithms have certain drawbacks regarding the comparability of the results, we propose an alternative spatio-temporal clustering technique which is based on empirical spatial correlations over time. As a key feature, CorClustST makes it easily possible to compare and interpret clustering results for different scenarios such as multiple underlying variables or varying time frames. In a test case, we show that the clustering strategy successfully identifies increasing spatial correlations of wind power forecast errors in Europe for longer forecast horizons. An extension of the clustering algorithm is finally presented which allows for a large-scale parallel implementation and helps to circumvent memory limitations. The proposed method will especially be helpful for researchers who aim to preprocess big spatio-temporal datasets and who intend to compare clustering results and spatial dependencies for different scenarios.

In order to reduce greenhouse gas emissions of the power sector, high shares of renewable power sources need to be integrated into existing systems. This will require vast amounts of investments. Cost of the capital needed for these investments are unevenly distributed among European regions. They show a clear North-South and West-East divide, which has not exhibited significant signs of narrowing in recent years. Power system studies investigating a continent-wide European power system, however, usually assume homogeneous cost of capital. The objective of this paper is to investigate how regional differences in cost of capital affect the result of these studies with respect to the optimal power system design. Our analysis is based on power system optimization with inhomogeneous cost of capital in Europe. We find that assuming homogeneous cost of capital leads to estimates on the levelized costs of electricity in a highly renewable European power system, which are too conservative. The optimal system design is significantly affected compared to an inhomogeneous scenario. In particular, we show that inhomogeneous cost of capital favors overall wind power deployment in the case of Europe, while the investment in solar power decreases.

Advances in applied energy 16, 100190 - (2024). doi:10.1016/j.adapen.2024.100190 Published by Elsevier ScienceDirect, [Amsterdam]