Document Version Peer reviewed versionLink back to DTU Orbit Citation (APA): Lythcke-Jørgensen, C. E., Münster, M., Ensinas, A. V., & Haglind, F. (2016). A method for aggregating external operating conditions in multi-generation system optimization models. Applied Energy, 166, 59-75.
Abstract
10This paper presents a novel, simple method for reducing external operating condition datasets to be used 11 in multi-generation plant optimization models. The method, called the Characteristic Operating Pattern 12 (CHOP) method, is a visually-based aggregation method that clusters reference data based on parameter 13 values rather than time of occurrence, thereby preserving important information on short-term relations 14 between the relevant operating parameters. This is opposed to commonly used methods where data are 15 averaged over chronological periods (months or years), and extreme conditions are hidden in the averaged 16
values. 17The CHOP method is tested in a case study where the operation of a fictive Danish combined heat and 18 power plant is optimized over a historical 5-year period. The optimization model is solved using the full 19 external operating condition dataset, a reduced dataset obtained using the CHOP method, a monthly-20 averaged dataset, a yearly-averaged dataset, and a seasonal peak/off-peak averaged dataset. The 21 *Revised Manuscript with No Changes Marked economic result obtained using the CHOP-reduced dataset is significantly more accurate than that obtained 22 using any of the other reduced datasets, while the calculation time is similar to those obtained using the 23 monthly averaged and seasonal peak/off-peak averaged datasets. The outcomes of the study suggest that 24 the CHOP method is advantageous compared to chronology-averaging methods in reducing external 25 operating condition datasets to be used in the design optimization models of flexible multi-generation 26 plants. 27