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

Sonja Wogrin; Diego Alejandro Tejada Arango; S. Pineda; J.M. Morales

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

Sonja Wogrin, Diego Alejandro Tejada Arango, S. Pineda, J.M. Morales

Institute of Electricity Economics and Energy Innovation (4340)

Research output: Contribution to conference › Abstract › peer-review

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.

Original language	English
Publication status	Published - 2019
Event	INFORMS Annual Meeting - Seattle, United States Duration: 20 Oct 2019 → 23 Oct 2019

Conference

Conference	INFORMS Annual Meeting
Country/Territory	United States
City	Seattle
Period	20/10/19 → 23/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "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.",

author = "Sonja Wogrin and {Tejada Arango}, {Diego Alejandro} and S. Pineda and J.M. Morales",

year = "2019",

language = "English",

note = "INFORMS Annual Meeting ; Conference date: 20-10-2019 Through 23-10-2019",

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TY - CONF

T1 - Analyzing time period aggregation methods for power system investment and operation models with renewables and storage

AU - Wogrin, Sonja

AU - Tejada Arango, Diego Alejandro

AU - Pineda, S.

AU - Morales, J.M.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

M3 - Abstract

T2 - INFORMS Annual Meeting

Y2 - 20 October 2019 through 23 October 2019

ER -