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Power system optimization models are large mathematical models used byresearchers and policymakers that pose tractability issues when representingreal-world systems. Several aggregation techniques have been proposed toaddress these computational challenges and it remains a relevant topic in powersystems research. In this paper, we extend a recently developed Basis-Orientedtime series aggregation approach used for power system optimization models thataggregates time steps within their Simplex basis. This has proven to be anexact aggregation for simple economic dispatch problems. We extend thismethodology to include network and ramping constraints; for the latter (and tohandle temporal linking), we develop a heuristic algorithm that finds an exactpartition of the input data, which is then aggregated. Our numerical results,for a simple 3-Bus system, indicate that: with network constraints only, we canachieve a computational reduction by a factor of 1747 (measured in the numberof variables of the optimization model), and of 12 with ramping constraints.Moreover, our findings indicate that with temporal linking constraints,aggregations of variable length must be employed to obtain an exact result (thesame objective function value in the aggregated model) while maintaining thecomputational tractability, this implies that the duration of the aggregationsdoes not necessarily correspond to commonly used lengths like days or weeks.Finally, our results support previous research concerning the importance ofextreme periods on model results.

Enhancing Time Series Aggregation For Power System Optimization Models: Incorporating Network and Ramping Constraints