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Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response

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  • Li, Yang
  • Han, Meng
  • Shahidehpour, Mohammad
  • Li, Jiazheng
  • Long, Chao

Abstract

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

Suggested Citation

  • Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:appene:v:335:y:2023:i:c:s0306261923001137
    DOI: 10.1016/j.apenergy.2023.120749
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    14. Cai, Pengcheng & Mi, Yang & Ma, Siyuan & Li, Hongzhong & Li, Dongdong & Wang, Peng, 2023. "Hierarchical game for integrated energy system and electricity-hydrogen hybrid charging station under distributionally robust optimization," Energy, Elsevier, vol. 283(C).
    15. Athanasios G. Lazaropoulos & Helen C. Leligou, 2024. "Integration of LiFi, BPL, and Fiber Optic Technologies in Smart Grid Backbone Networks: A Proposal for Exploiting the LiFi LED Street Lighting Networks of Power Utilities and Smart Cities," Sustainability, MDPI, vol. 16(2), pages 1-30, January.
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    18. Qiu, Haifeng & Vinod, Ashwin & Lu, Shuai & Gooi, Hoay Beng & Pan, Guangsheng & Zhang, Suhan & Veerasamy, Veerapandiyan, 2023. "Decentralized mixed-integer optimization for robust integrated electricity and heat scheduling," Applied Energy, Elsevier, vol. 350(C).
    19. Li, Yang & Cao, Jiting & Xu, Yan & Zhu, Lipeng & Dong, Zhao Yang, 2024. "Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    20. Bo Zhou & Erchao Li, 2024. "Multi-Regional Integrated Energy Economic Dispatch Considering Renewable Energy Uncertainty and Electric Vehicle Charging Demand Based on Dynamic Robust Optimization," Energies, MDPI, vol. 17(11), pages 1-25, May.

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