A Simulation Framework for Optimal Energy Storage Sizing
Despite the increasing interest in Energy Storage Systems (ESS), quantification of their technical and economical benefits remains a challenge. To assess the use of ESS, a simulation approach for ESS optimal sizing is presented. The algorithm is based on an adapted Unit Commitment, including ES...
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Online Access: | http://www.mdpi.com/1996-1073/7/5/3033 |
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doaj-14258fc5a01c4104a6efec7d282be4402020-11-24T20:50:04ZengMDPI AGEnergies1996-10732014-05-01753033305510.3390/en7053033en7053033A Simulation Framework for Optimal Energy Storage SizingCarlos Suazo-Martínez0Eduardo Pereira-Bonvallet1Rodrigo Palma-Behnke2SPEC Energy Consulting, Rosario Norte 400/51, 7561156 Las Condes, Santiago, ChileEnergy Centre FCFM, Universidad de Chile, Avenida Tupper 2007, 8370451 Santiago, ChileEnergy Centre FCFM, Universidad de Chile, Avenida Tupper 2007, 8370451 Santiago, ChileDespite the increasing interest in Energy Storage Systems (ESS), quantification of their technical and economical benefits remains a challenge. To assess the use of ESS, a simulation approach for ESS optimal sizing is presented. The algorithm is based on an adapted Unit Commitment, including ESS operational constraints, and the use of high performance computing (HPC). Multiple short-term simulations are carried out within a multiple year horizon. Evaluation is performed for Chile's Northern Interconnected Power System (SING). The authors show that a single year evaluation could lead to sub-optimal results when evaluating optimal ESS size. Hence, it is advisable to perform long-term evaluations of ESS. Additionally, the importance of detailed simulation for adequate assessment of ESS contributions and to fully capture storage value is also discussed. Furthermore, the robustness of the optimal sizing approach is evaluated by means of a sensitivity analyses. The results suggest that regulatory frameworks should recognize multiple value streams from storage in order to encourage greater ESS integration.http://www.mdpi.com/1996-1073/7/5/3033energy storage systemsunit commitmentrenewable energyoptimal sizinghigh performance computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carlos Suazo-Martínez Eduardo Pereira-Bonvallet Rodrigo Palma-Behnke |
spellingShingle |
Carlos Suazo-Martínez Eduardo Pereira-Bonvallet Rodrigo Palma-Behnke A Simulation Framework for Optimal Energy Storage Sizing Energies energy storage systems unit commitment renewable energy optimal sizing high performance computing |
author_facet |
Carlos Suazo-Martínez Eduardo Pereira-Bonvallet Rodrigo Palma-Behnke |
author_sort |
Carlos Suazo-Martínez |
title |
A Simulation Framework for Optimal Energy Storage Sizing |
title_short |
A Simulation Framework for Optimal Energy Storage Sizing |
title_full |
A Simulation Framework for Optimal Energy Storage Sizing |
title_fullStr |
A Simulation Framework for Optimal Energy Storage Sizing |
title_full_unstemmed |
A Simulation Framework for Optimal Energy Storage Sizing |
title_sort |
simulation framework for optimal energy storage sizing |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2014-05-01 |
description |
Despite the increasing interest in Energy Storage Systems (ESS), quantification of their technical and economical benefits remains a challenge. To assess the use of ESS, a simulation approach for ESS optimal sizing is presented. The algorithm is based on an adapted Unit Commitment, including ESS operational constraints, and the use of high performance computing (HPC). Multiple short-term simulations are carried out within a multiple year horizon. Evaluation is performed for Chile's Northern Interconnected Power System (SING). The authors show that a single year evaluation could lead to sub-optimal results when evaluating optimal ESS size. Hence, it is advisable to perform long-term evaluations of ESS. Additionally, the importance of detailed simulation for adequate assessment of ESS contributions and to fully capture storage value is also discussed. Furthermore, the robustness of the optimal sizing approach is evaluated by means of a sensitivity analyses. The results suggest that regulatory frameworks should recognize multiple value streams from storage in order to encourage greater ESS integration. |
topic |
energy storage systems unit commitment renewable energy optimal sizing high performance computing |
url |
http://www.mdpi.com/1996-1073/7/5/3033 |
work_keys_str_mv |
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