Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling

B. S. Palmintier and M. D. Webster

IEEE Transactions on Power Systems (May 2014)

DOI: 10.1109/TPWRS.2013.2293127

Designing future capacity mixes with adequate flexibility requires capturing operating constraints through an embedded unit commitment approximation. Despite significant recent improvements, such simulations still require significant computation times. Here we propose a method, based on clustering units, for approximate unit commitment with dramatic improvements in solution time. This method speeds computation by aggregating similar but non-identical units. This replaces large numbers of binary commitment variables with fewer integers while still capturing individual unit decisions and constraints. We demonstrate the trade-off between accuracy and run-time for different levels of aggregation. A numeric example using an ERCOT-based 205-unit system illustrates that careful aggregation introduces errors of 0.05

keywords: approximation theory;power generation scheduling;statistical analysis;binary commitment variable;capacity expansion;efficient operational flexibility modeling;embedded unit commitment approximation;heterogeneous unit clustering;power system modeling;Biological system modeling;Computational modeling;Fuels;Generators;Heating;Measurement;Wind forecasting;Capacity expansion;flexibility;integer programming;power generation scheduling;power system modeling;unit commitment

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