Extreme events in time series aggregation: A case study for optimal residential energy supply systems

Holger Teichgraeber, Constantin P. Lindenmeyer, Nils Baumgärtner, Leander Kotzur, Detlef Stolten, Martin Robinius, André Bardow and Adam R. Brandt

Applied Energy, 2020, vol. 275, issue C, No S0306261920307352

Abstract: To account for volatile renewable energy supply, energy systems optimization problems require high temporal resolution. Many models use time-series clustering to find representative periods to reduce the amount of time-series input data and make the optimization problem computationally tractable. However, clustering methods remove peaks and other extreme events, which are important to achieve robust system designs. This work addresses the challenge of including extreme events. We present a general decision framework to include extreme events in a set of representative periods. We introduce a method to find extreme periods based on the slack variables of the optimization problem itself. Our method is evaluated and benchmarked with other extreme period inclusion methods from the literature for a design and operations optimization problem: a residential energy supply system. Our method ensures feasibility over the full input data of the residential energy supply system although the design optimization is performed on the reduced data set.

Keywords: Clustering; Energy systems; Time-series aggregation; Temporal resolution; Extreme periods; Optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.apenergy.2020.115223

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