Optimal ESS size calculation for ramp rate control of grid-connected microgrid based on the selection of accurate representative days
- Authors
- Tahir, Hira; Park, Dong-Hwan; Park, Su-Seong; Kim, 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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