A framework for estimating flexible resources according to future Korean renewables scenario: Robust optimization approach considering multiple uncertainties. (June 2020)

• Record Type: Journal Article
• Authors: Jeong, Jinwoo; Lee, Byongjun
• Is Part Of: International journal of electrical power & energy systems. Volume 118(2020)
• Publication Date: 2020-06
• Subjects: Robust optimization -- Power system flexibility -- Energy storage system -- Renewable energy -- Uncertainty; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.105728
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: This study addresses how to estimate flexible resource capacity to ensure flexibility. The uncertainties related to RES is considered in the robust optimization model. The result of the proposed method on the Korean electric power system is presented. Abstract: This study presents a robust optimization model to secure flexibility under a high penetration of renewable energy systems in a future grid. An increase in renewable energy into a power system causes difficulties and complexities with regard to power system planning and operation owing to an increase in uncertainty. This trend can create an issue in the flexibility of a power system under a high penetration of renewable energy. To acquire sufficient flexibility, numerous flexible resources such as conventional generators with a ramping capability, energy storage systems and demand response program should be procured. Herein, we propose estimating the flexible resource capacity required to prevent a flexibility deficit when considering multiple uncertainties such as the effective capacity and 1-min power fluctuation rate of the renewable energy systems. To solve this problem, including uncertainties, through an optimization technique, we adopt a robust optimization to deal with uncertainty by constructing an uncertainty set and provide a robust solution considering the worst case within such a set. The robust optimization model was tested using data from the Korean electric power system for the year 2030. In addition, the results from a robust optimization are compared with the results from a deterministic approach.