Variable time-step: A method for improving computational tractability for energy system models with long-term storage. (15th December 2020) - de Guibert, Paul; Shirizadeh, Behrang; Quirion, Philippe
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. Highlights: Energy system models with large proportion of variable renewable energy require a long period of hourly weather data. We introduce a new method to reduce the optimization time, based on a variable time-step. Hourly temporal resolution is used for critical periods, while for the rest the temporal resolution is coarser. The method brings very accurate results except for battery volume. The optimization time is reduced by a factor of around 60.