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

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.

Automated summary

Integrating renewable energy into transportation infrastructures promotes collaborative decarbonization. For the HSAMEG, its optimization lacks source-load-storage cooperation and accurate modeling. A novel dispatch is proposed to balance dispatch reliability and flexibility, and model accuracy and complexity by combining robust and stochastic optimizations and applying multi-granularity modeling. The proposed strategy improves economic objective by 17% and self-contained objective by 16.2% compared to robust optimization, and improves computation efficiency by over 5 times and self-contained objective by 8.8% compared to stochastic optimization without constraint violations.