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Sizing of hybrid energy storage through analysis of load profile characteristics: A household case study
Hybrid Energy Storage System (HESS) have the potential to offer better flexibility to a grid than any single energy storage solution. However, sizing a HESS is challenging, as the required capacity, power and ramp rates for a given application are difficult to derive. This paper proposes a method fo...
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Published in: | Journal of energy storage 2022-08, Vol.52, p.104768, Article 104768 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Hybrid Energy Storage System (HESS) have the potential to offer better flexibility to a grid than any single energy storage solution. However, sizing a HESS is challenging, as the required capacity, power and ramp rates for a given application are difficult to derive. This paper proposes a method for splitting a given load profile into several storage technology independent sub-profiles, such that each of the sub-profiles leads to its own requirements. This method can be used to gain preliminary insight into HESS requirements before a choice is made for specific storage technologies. To test the method, a household case is investigated using the derived methodology, and storage requirements are found, which can then be used to derive concrete storage technologies for the HESS of the household. Adding a HESS to the household case reduces the maximum import power from the connected grid by approximately 7000 W and the maximum exported power to the connected grid by approximately 1000 W. It is concluded that the method is particularly suitable for data sets with a high granularity and many data points.
•Load profiles can be split into sub-profiles to find hybrid storage requirements.•An easy and fast way for preliminary investigation of storage sizing.•No storage technologies are considered which widens the search space.•For a residence, higher granularity of load profile data is preferred. |
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ISSN: | 2352-152X 2352-1538 |
DOI: | 10.1016/j.est.2022.104768 |