Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities

The rising significance of renewable energy increases the importance of representing time-varying input data in energy system optimization studies. Time-series aggregation, which reduces temporal model complexity, has emerged in recent years to address this challenge. We provide a comprehensive review of time-series aggregation for the optimization of energy systems. We show where time series affect optimization models, and define the goals, inherent assumptions, and challenges of time-series aggregation. We review the methods that have been proposed in the literature, focusing on how these methods address the challenges. This leads to suggestions for future research opportunities. This review is both an introduction for researchers using time-series aggregation for the first time and a guide to “connect the dots” for experienced researchers in the field. We recommend the following best practices when using time-series aggregation: (1) Performance should be measured in terms of optimization outcome and should be validated on the full time series; (2) aggregation methods and optimization problem formulation should be tuned for the specific problem and data; (3) wind data should be aggregated with extra care; (4) bounding the error in the objective function should be considered; (5) inclusion of real “extreme days” in addition to aggregated days can often greatly improve performance. • Review and discussion of time series aggregation methods. • Energy systems optimization compute time can be reduced by 1–3 orders of magnitude. • Identification of best practices and outline of future research opportunities. • Synthesis of the literature based on challenges common to all applications. • Integration of many applications for which methods have been developed individually.