A Bi-level optimization model of integrated energy system considering wind power uncertainty. (January 2023)

• Record Type: Journal Article
• Authors: Fan, Wei; Tan, Qingbo; Zhang, Amin; Ju, Liwei; Wang, Yuwei; Yin, Zhe; Li, Xudong
• Is Part Of: Renewable energy. Volume 202(2023)
• Page Start: 973
• Page End: 991
• Publication Date: 2023-01
• Subjects: Integrated energy system -- Energy storage -- Bi-level optimization -- Uncertainty -- Karush-kuhn-tucker -- Robust optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.12.007
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: To cope with the volatility of renewable energy and improve the efficiency of energy storage investment, a bi-level (B-L) optimization model of an integrated energy system (IES) with multiple types of energy storage is established by considering the uncertainty of wind power. The upper-level optimization model considers the lowest configuration cost of energy storage as the objective function and satisfies the constraints of the energy storage configuration. The lower-level optimization model considers the lowest operation cost of the IES as the objective function and satisfies the constraints of the system operation. Second, to overcome the fluctuation problem of wind power output, a robust optimization theory is introduced to describe the uncertainty. Robust coefficients are set to reflect different risk attitudes, which improves the adaptability of the system to uncertainty. Third, the B-L optimization model is solved using the Karush–Kuhn Tucker condition. Finally, a new park is used to implement the simulation. The conclusions are as follows: (1) The economic configuration strategy and optimal operation scheme can be obtained by applying the B-L optimization model, and the upper- and lower-levels interact with each other. The optimal targets of the upper- and lower-level models are −115, 848 ¥ and 57, 131, 102 ¥, respectively. (2) The robust optimization theory improves the ability of a system to deal with risks. Robust optimization theory improves the ability of a system to deal with risks. With an increase in the robustness coefficient, the profit space of the upper-level model increases; however, the operation cost of the lower-level model increases.