A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage

A common solution to mitigate the complexity of power system studies is time aggregation. This is to replace the actual data set for all time intervals with representative time periods. Previous research confirms that when energy storage systems are involved in the study, preserving the overall shap...

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Autores principales:	Nima Sarajpoor, Logan Rakai, Juan Arteaga, Hamidreza Zareipour
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Power system planning aggregation clustering dynamic time warping storage Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/8a9b6f98e25442d2b2949dfdeb353689
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spelling	oai:doaj.org-article:8a9b6f98e25442d2b2949dfdeb3536892021-11-18T00:11:44ZA Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage2687-791010.1109/OAJPE.2021.3097366https://doaj.org/article/8a9b6f98e25442d2b2949dfdeb3536892021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9486903/https://doaj.org/toc/2687-7910A common solution to mitigate the complexity of power system studies is time aggregation. This is to replace the actual data set for all time intervals with representative time periods. Previous research confirms that when energy storage systems are involved in the study, preserving the overall shape of the original data is crucial. This paper proposes a new time aggregation framework to incorporate a shape-based distance to jointly extract representative periods of wind and demand data. The duration curve of the net demand is used as a data-based validation index to compare the performance of the proposed method against other techniques. Also, a 3-bus case study that includes a wind resource, an energy storage system, and two conventional generators is designed. Four model-based validation indices are defined and applied for performance measurement, including the annual operation cost of the system, the annual wind curtailment in the system, the energy throughput of the storage facility, and the daily average of the state of the charge of the energy storage for each 365 days of the year.Nima SarajpoorLogan RakaiJuan ArteagaHamidreza ZareipourIEEEarticlePower system planningaggregationclusteringdynamic time warpingstorageDistribution or transmission of electric powerTK3001-3521Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENIEEE Open Access Journal of Power and Energy, Vol 8, Pp 448-459 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Power system planning aggregation clustering dynamic time warping storage Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	Power system planning aggregation clustering dynamic time warping storage Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Nima Sarajpoor Logan Rakai Juan Arteaga Hamidreza Zareipour A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
description	A common solution to mitigate the complexity of power system studies is time aggregation. This is to replace the actual data set for all time intervals with representative time periods. Previous research confirms that when energy storage systems are involved in the study, preserving the overall shape of the original data is crucial. This paper proposes a new time aggregation framework to incorporate a shape-based distance to jointly extract representative periods of wind and demand data. The duration curve of the net demand is used as a data-based validation index to compare the performance of the proposed method against other techniques. Also, a 3-bus case study that includes a wind resource, an energy storage system, and two conventional generators is designed. Four model-based validation indices are defined and applied for performance measurement, including the annual operation cost of the system, the annual wind curtailment in the system, the energy throughput of the storage facility, and the daily average of the state of the charge of the energy storage for each 365 days of the year.
format	article
author	Nima Sarajpoor Logan Rakai Juan Arteaga Hamidreza Zareipour
author_facet	Nima Sarajpoor Logan Rakai Juan Arteaga Hamidreza Zareipour
author_sort	Nima Sarajpoor
title	A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
title_short	A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
title_full	A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
title_fullStr	A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
title_full_unstemmed	A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
title_sort	shape-based clustering framework for time aggregation in the presence of variable generation and energy storage
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8a9b6f98e25442d2b2949dfdeb353689
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A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage

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