Generation Capacity Expansion Planning (GCEP) requires high temporal resolution to account for the volatility of renewable energy supply. Because the GCEP optimization problem is often computationally intractable, time-series input data are often aggregated to representative periods using clustering. However, clustering removes extreme events, which are important to achieve reliable system designs. We present a method to include extreme periods into time-series aggregation for GCEP that guarantees reliable system designs on the full input data even though only the reduced data set is used for system design. Our method iteratively adds extreme periods to the set of representative periods based on information from the optimization problem itself until the energy system provides power reliably. We perform a comprehensive analysis on several case studies of both German and Californian energy systems and show that our method leads to meeting electricity demand at all times, whereas when clustering without extreme periods, lost load is between 1.9%-13.5% of total system load.. We show that our method outperforms the state-of-the-art method of adding a pre-defined number of extreme periods based on statistical properties of the data itself.
Stanford University, August 2021
View Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation