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Optimal ESS size calculation for ramp rate control of grid-connected microgrid based on the selection of accurate representative days

Authors
Tahir, HiraPark, Dong-HwanPark, Su-SeongKim, Rae-Young
Issue Date
Jul-2022
Publisher
Elsevier Ltd
Keywords
Energy storage systems; Microgrid; Optimization; Ramp rate; Renewable energy; Representative days
Citation
International Journal of Electrical Power and Energy Systems, v.139, pp.1 - 13
Indexed
SCIE
SCOPUS
Journal Title
International Journal of Electrical Power and Energy Systems
Volume
139
Start Page
1
End Page
13
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/170065
DOI
10.1016/j.ijepes.2022.108000
ISSN
0142-0615
Abstract
Recently, energy storage system (ESS) is in the spotlight because of its deployment to alleviate high ramp rate in the microgrids; enabling the large-scale penetration of renewable energy resources into the utility grid. A limited number of representative days are often chosen to ensure computational tractability while optimizing the ESS size for ramp rate control. Most developed ESS size optimization approaches focused on optimal ESS operation during the optimization horizon. However, the solution's optimality also depends on the accuracy of selected representative days, as sizing is based on the optimal operation during these days. This study develops a comprehensive methodology for optimal ESS size calculation by incorporating the selection of accurate representative days. A novel representative day selection technique is proposed to select accurate representative days from the data spanning the optimization horizon in a reasonable amount of time. The suitability of the obtained representative days was assessed with regard to the cost of ramp violations. The results indicate that the proposed technique can obtain more accurate representative days. Thus, by employing this technique, optimality of the ESS size can be guaranteed. The significance of representative days’ selection from a larger data set rather than a single year is highlighted. Moreover, we demonstrate how the adoption of worst-case scenario can undermine the solution's optimality.
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