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

Author

Listed:
  • Song, Yuguang
  • Xia, Mingchao
  • Yang, Liu
  • Chen, Qifang
  • Su, Su

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.

Suggested Citation

  • Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    • Handle: RePEc:eee:renene:v:205:y:2023:i:c:p:747-762
    DOI: 10.1016/j.renene.2023.02.006
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    1. Zhao, Bingxu & Cao, Xiaodong & Duan, Pengfei, 2024. "Cooperative operation of multiple low-carbon microgrids: An optimization study addressing gaming fraud and multiple uncertainties," Energy, Elsevier, vol. 297(C).
    2. Zhang, Lingzhi & Shi, Ruifeng & Ma, Xiaolei & Jia, Limin & Lee, Kwang Y., 2025. "Highway self-contained multi-microgrid energy management strategy based on universal gravitation," Energy, Elsevier, vol. 327(C).

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