Analyzing time period aggregation methods for power system investment and operation models with renewables and storage

Abstract

The transition of the power system from its current state to the power system of the future is heavily influenced by the growing penetration of renewables combined with the increasing importance of storage technologies. We present and compare two different time-period aggregation methods (enhanced representative periods; and, chronological time-period clustering) that allow for the adequate representation of both renewables and storage technologies in power system models. And we assess the quality of both aggregation methods in terms of accurately predicting investment and operating decisions.