Wind data introduce error in time-series reduction for capacity expansion modelling

Abstract

Shares of renewable energy are rapidly increasing in many countries due to emissions policies and declining prices. Investment planning for future renewable deployment often relies on optimization models. Memory usage and solving time restrict these models, leading to tradeoffs in the treatment of temporal complexity, spatial complexity, and physical representation. A common approach is to reduce the temporal complexity of models. Reducing temporal complexity is often achieved by using time-series aggregating and modelling representative periods instead of a complete time series. But the impacts of such approaches are still poorly understood, especially for very low emissions systems with high shares of variable renewable energies. In this paper, the impacts of using time-series aggregation methods on optimal system design are investigated. It is found that the negative impact of time-series aggregation increases for lower emissions. It is also identified that modelling wind time-series data with representative days introduces this negative impact primarily and that representing wind time-series data with representative days decreases the reliability of supply defined as unserved load (0.05%–18.0%), introduces a bias in installed capacity (−31.15% to +12.2%), and underestimates total system cost (2.5%–44.9%). These effects are largest in cases with the strongest emission constraints. When designing low emissions systems with a high share of variable renewable energies, it is recommended not to use time-series aggregation to create representative days for wind power output. This paper contributes an Open Source analysis framework containing time-series aggregation and capacity expansion that should be applied when testing future time-series aggregation methods to reduce the identified negative impacts.