Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid. (March 2023)

• Record Type: Journal Article
• Authors: Song, Yuguang; Xia, Mingchao; Yang, Liu; Chen, Qifang; Su, Su
• Is Part Of: Renewable energy. Volume 205(2023)
• Page Start: 747
• Page End: 762
• Publication Date: 2023-03
• Subjects: Decarbonization of transportation -- Operation method optimization -- Multi-granularity modeling -- Uncertainty; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2023.02.006
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Integrating renewable energy into planning and operation of transportation infrastructures can help to promote the various sector collaborative decarbonization. For the highway service area micro-energy grid (HSAMEG), its optimization lacks the source-load-storage cooperation and the modeling that considers both accuracy and complexity, and is hard to balance reliability and flexibility due to uncertainties in renewable energy and charging-demand. For these issues, a novel dispatch is proposed to balance the dispatch reliability and flexibility, and the model accuracy and complexity by combining advantages of robust and stochastic optimizations and applying the multi-granularity modeling. First, the source-load-storage configuration is established. Then the multi-granularity model is developed by fine-grained model based on the operating characteristics and coarse-grained model based on the equivalent energy storage characteristics. Finally, based on distribution characteristics of online-optimization forecast-errors, a multi-granularity source-load-storage cooperative dispatch combining robust optimization and stochastic optimization is proposed. The simulation results show that the source-load-storage collaboration increases the self-contained objective by 10%. Compared with robust optimization, the proposed strategy enhances the economic objective by 17% and the self-contained objective by 16.2%. Compared with stochastic optimization, the proposed strategy improves the computation efficiency by over 5 times and the self-contained objective by 8.8% without constraint violations.