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• W3172692116 abstract "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. • Comparison of methods which allow for long term storage arbitrage. • Weak link between approximating original time series and full year solution. • Minimal differences between time series aggregation methods. • Two methods capture value of long term storage, but risk of over or underinvestment. • Care should be taken with temporal scope reduction and long term storage." @default.
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• W3172692116 date "2021-09-01" @default.
• W3172692116 modified "2023-10-16" @default.
• W3172692116 title "Long term storage in generation expansion planning models with a reduced temporal scope" @default.
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• W3172692116 doi "https://doi.org/10.1016/j.apenergy.2021.117168" @default.
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