Research on optimal self‐scheduling horizon for the wind power and large‐scale CAES combined system. Issue 22 (30th October 2019)

• Record Type: Journal Article
• Authors: Li, Yaowang; Miao, Shihong; Yin, Binxin; Liu, Junyao; Yang, Weichen; Zhang, Songyan
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 22(2019)
• Page Start: 5197
• Page End: 5206
• Publication Date: 2019-10-30
• Subjects: compressed air energy storage -- stochastic programming -- power markets -- power generation scheduling -- wind power plants -- linear programming; rolling day‐ahead self‐scheduling framework -- WF -- self‐scheduling horizon optimisation model -- bilayer stochastic chance‐constrained optimisation problem -- optimal self‐scheduling horizon -- wind power -- self‐scheduling problem -- CAES plant -- schedule parameter -- CAES energy storage capacity -- large‐scale CAES combined system -- wind farm -- compressed air energy storage -- deterministic linear formulation; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.7081
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The self‐scheduling horizon is an important schedule parameter in the self‐scheduling problem. A more reasonable self‐scheduling horizon can lead to higher benefits of the wind farm (WF) and large‐scale compressed air energy storage (CAES) combined system. However, very few studies have been reported about the optimisation of self‐scheduling horizon for a WF paired with a CAES plant. In this study, a rolling day‐ahead self‐scheduling framework for a WF and CAES combined system is first proposed. After that, the self‐scheduling horizon optimisation model is developed in the formulation of a bilayer stochastic chance‐constrained optimisation problem. The proposed model is converted into its equivalent deterministic linear formulation and then is solved. Based on the developed model, the impacts of self‐scheduling horizon on the profit of the combined system are analysed. Numerical simulation results indicate that the profit of the combined system increases after using the optimal self‐scheduling horizon, and the profit increment is more obvious with the increase of the CAES's energy storage capacity.