Variable time-step: A method for improving computational tractability for energy system models with long-term storage

Paul de Guibert, Behrang Shirizadeh and Philippe Quirion

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Abstract: Optimizing an energy system model featuring a large proportion of variable (non-dispatchable) renewable energy requires a fine temporal resolution and a long period of weather data to provide robust results. Many models are optimized over a limited set of 'representative' periods (e.g. weeks) but this precludes a realistic representation of long-term energy storage. To tackle this issue, we introduce a new method based on a variable time-step. Critical periods that may be important for dimensioning part of the electricity system are defined, during which we use an hourly temporal resolution. For the other periods, the temporal resolution is coarser. This method brings very accurate results in terms of system cost, curtailment, storage losses and installed capacity, even though the optimization time is reduced by a factor of around 60. Results are less accurate for battery volume. We conclude that further research into this 'variable time-step' method would be worthwhile.

Keywords: Energy system model; renewable energies; computational tractability; time series aggregation; complexity reduction (search for similar items in EconPapers)
Date: 2020-12
New Economics Papers: this item is included in nep-cmp and nep-ene
Note: View the original document on HAL open archive server: https://hal.science/hal-03100309v1
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Citations: View citations in EconPapers (6)

Published in Energy, 2020, 213, pp.119024. ⟨10.1016/j.energy.2020.119024⟩

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Journal Article: Variable time-step: A method for improving computational tractability for energy system models with long-term storage (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03100309

DOI: 10.1016/j.energy.2020.119024

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