  Intermittent renewable energy resources like wind and solar pose great
uncertainty of multiple time scales, from minutes to years, on the design and
operation of power systems. Energy system optimization models have been
developed to find the least-cost solution to matching the uncertainty with
flexibility resources. However, input data that capture such multi-time-scale
uncertainty are characterized with a long time horizon and bring great
difficulty to solving the optimization model. Here we propose an adaptive
clustering method based on the decision variables of optimization model to
alleviate the computational complexity, in which the energy system is optimized
over selected representative time periods instead of the full time horizon. The
proposed clustering method is adaptive to various energy system optimization
models or settings, because it extracts features from the optimization models.
Results show that the proposed clustering method can significantly lower the
error in approximating the solution with the full time horizon, compared to
traditional clustering methods.
