EURO 2024 Copenhagen
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2291. Enhancing electricity system robustness through the application of a new time series cluster algorithm

Invited abstract in session MA-21: Planning problems in electrical energy systems, stream OR in Energy.

Monday, 8:30-10:00
Room: 49 (building: 116)

Authors (first author is the speaker)

1. Leonie Sara Plaga
Ruhr-Universität Bochum
2. Valentin Bertsch
Chair of Energy Systems and Energy Economics, Ruhr-Universität Bochum

Abstract

The ongoing evolution of climate change presents unprecedented challenges to electricity system performance and reliability, which are inherently sensitive to weather conditions affecting both energy demand and generation capacities. To aid decision-making in energy systems under climate change influence, energy system optimization models can be combined with climate model outcomes. As future climate development is highly uncertain, there are many different climate models and human greenhouse gas emission scenarios projecting the future. Thus, we propose a new time series clustering algorithm. This algorithm allows to incorporate various climate models and emission scenarios into energy system optimization while focusing on providing a robust solution, meaning that the amount of unmet demand is low no matter which climate scenario is realized. The algorithm operates by first splitting the time series into single days which are optimized individually. The investment results of the single days are then used to calculate the amount of unmet demand which occurs when the single-day investment decsisions are used with the complete time series. This data informs the clustering process, prioritizing days with high unmet demand. Our results demonstrate that this new clustering algorithm can reduce unmet demand compared to traditional cluster algorithms like k-means. In further research we plan to compare our approach to traditional robust optimization approaches.

Keywords

Status: accepted


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