Long term storage in generation expansion planning models with a reduced temporal scope

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models.

Automated summary

The study compared time series aggregation methods and generation expansion planning models, finding that the selection of hours or days has a greater impact on performance than the aggregation method itself. Some generation expansion planning models were able to capture the value of long-term storage, but there was also potential for over or underinvestment.