A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market

Seong-Hyeon Cha, Sun-Hyeok Kwak and Woong Ko ()
Additional contact information
Seong-Hyeon Cha: Department of Electrical Engineering, Changwon National University (CWNU), 20 Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea
Sun-Hyeok Kwak: Department of Electrical Engineering, Changwon National University (CWNU), 20 Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea
Woong Ko: Department of Electrical Engineering, Changwon National University (CWNU), 20 Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea

Energies, 2023, vol. 16, issue 20, 1-27

Abstract: As the share of distributed generation increases, so do the opportunities for aggregators to participate in the electricity market. In particular, aggregators participating in both the day-ahead and real-time markets contribute to improving the reliability of the power system. In addition, aggregators seeking additional revenue can benefit from providing reserves in a joint electricity market environment. However, aggregated resources with uncertainty are limited because of the uncertain nature of both reserve provision and the amount of reserves they can provide. Therefore, this study proposes a robust optimization model for an aggregator to formulate a strategy for participation in the day-ahead markets and deploys energy control in the real-time operation. The serving ratio reflects the availability of the aggregator’s reserve participation. Both the deployed up/down power and renewable energy in the real-time operation are considered as uncertain parameters to reflect the uncertainty. In the case study, we analyze the profit-maximization strategy of an aggregator that owns renewable energy resources and energy-storage systems under the variation interval for uncertain parameters and the serving ratio. The bidding strategies vary by the variation interval and the serving ratio.

Keywords: aggregator; day-ahead markets; joint electricity market; mixed-integer linear programming; real-time operation; reserve; robust optimization; serving ratio; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/20/7061/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/20/7061/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:20:p:7061-:d:1258486

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().