Importance subsampling for power system planning under multi-year demand and weather uncertainty

Hilbers, A. P., Brayshaw, D. ORCID: https://orcid.org/0000-0002-3927-4362 and Gandy, A. (2020) Importance subsampling for power system planning under multi-year demand and weather uncertainty. In: International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 18-21 Aug 2020, Liege, Belgium. doi: 10.1109/PMAPS47429.2020.9183591

Abstract/Summary

This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models. Recent studies indicate that reliably determining optimal electricity (investment) strategy under climate variability requires the consideration of multiple years of demand and weather data. However, solving planning models over long simulation lengths is typically computationally unfeasible, and established time series reduction approaches induce significant errors. The importance subsampling method reliably estimates long-term planning model outputs at greatly reduced computational cost, allowing the consideration of multi-decadal samples. The key innovation is a systematic identification and preservation of relevant extreme events in modeling subsamples. Simulation studies on generation and transmission expansion planning models illustrate the method’s enhanced performance over established "representative days" clustering approaches. The models, data and sample code are made available as open-source software.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/92784
Identification Number/DOI	10.1109/PMAPS47429.2020.9183591
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
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