Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms. (15th May 2022)

• Record Type: Journal Article
• Authors: Kittel, Martin; Hobbie, Hannes; Dierstein, Constantin
• Is Part Of: Energy. Volume 247(2022)
• Publication Date: 2022-05-15
• Subjects: Cluster analysis -- Time series aggregation -- Variable renewable energy -- Electricity market modeling -- Typical system states; ARS Algorithm-representation scenario -- ATC Available transfer capacity -- CLINK Complete-linkage clustering -- CorrE Correlation error -- CostE System cost error -- CWE Central West European -- FBMC Flow-based market coupling -- HACAL Hierarchical cluster algorithm -- IEM Input Error Metrics -- MAE Mean absolute error -- PARTAL Partitional cluster algorithm -- PDC Price duration curve -- PV Photovoltaics -- OEM Output error metrics -- RMSE Root mean square error -- SSE Sum of squared errors -- SLINK Single-linkage clustering -- TC Total system cost -- UPGMA Unweighted pair group method using arithmetic mean -- UPGMC Unweighted pair-group method using centroids -- VarC Covered variance -- VRE Variable renewable energy sources -- WARD Ward's minimum variance method -- WPGMC Weighted pair-group method using centroids -- WPGMA Weighted pair-group method using arithmetic mean; 62H30; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123458
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Comprehensive numerical models are pivotal to analyze the decarbonization of electricity systems. However, increasing system complexity and limited computational resources impose restrictions to model-based analyses. One way to reduce computational burden is to use a minimum, yet representative, set of system states for model simulation. These states characterize fluctuating renewable generation and variable demand for electricity prevailing at a certain point in time. A review of possible time series aggregation techniques identifies cluster algorithms as most adequate, with k-Means and the Ward algorithm predominating. However, throughout the surveyed literature, the line of reasoning for the selection of these algorithms remains unclear. To support the electricity system modeling community in selecting an algorithm, this paper devises a systematic multi-stage evaluation approach to compare a large variety of cluster analysis configurations, differing in algorithm, cluster representation, and number of clusters. Results show that electricity demand and renewable energy generation time series can be compressed to below one percent while sustaining global characteristics of the original data. Two potent cluster configurations are identified, confirming k-Means and WARD as being prevalent. Beyond electricity market data, the methodology can be applied to various types of fundamental time-dependent input data. Highlights: Three-stage approach to evaluate cluster algorithms for time series reduction. 260 cluster analyses compared differing in algorithm, representation, and cluster number. Multivariate time series reduced to below 100 representative system states. k-Means and Ward most performant in time series reduction for electricity market modeling.