Adequacy of time-series reduction for renewable energy systems

To reduce computational complexity, macro-energy system models commonly implement reduced time series data. For renewable energy systems dependent on seasonal storage and characterized by intermittent renewables, like wind and solar, adequacy of time-series reduction is in question. Using a capacity expansion model, we evaluate different methods for creating and implementing reduced time series regarding loss of load and system costs. Results show that adequacy greatly depends on the length of the reduced time-series and how it is implemented into the model. Implementation as a chronological sequence with re-scaled time-steps prevents loss of load best but imposes a positive bias on seasonal storage resulting in an overestimation of system costs. Compared to chronological sequences, grouped periods require more time so solve for the same number of time-steps, because the approach requires additional variables and constraints. Overall, results suggest further efforts to improve time series reduction and other methods for reducing computational complexity. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The adequacy of time-series reduction depends on the length of the reduced time-series and how it is implemented into the model. Implementing the time-series as a chronological sequence with re-scaled time-steps prevents loss of load best, but results in overestimation of system costs for seasonal storage. Grouped periods require more time to solve for the same number of time-steps compared to chronological sequences.