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Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties

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  • Chen, Xianqing
  • Dong, Wei
  • Yang, Qiang

Abstract

Microgrid is considered an efficient paradigm for managing the massive number of distributed renewable generation and storage facilities. The optimal microgrid capacity planning is a non-trivial task due to the impact of randomness and uncertainties of renewable generation sources, and the adopted energy management strategies. In this paper, an optimal capacity planning model for the grid-connected microgrid is developed fully considering the renewable generation uncertainties through efficient scenario generation and reduction based on the deep convolutional generative adversarial network (DCGAN) and improved k-medoids clustering algorithm, as well as the microgrid energy management strategy. The proposed solution optimizes the capacity planning for the maximization of renewable energy utilization efficiency, and minimizes the economic cost and carbon emissions. The proposed solution is assessed using a case study of a microgrid (MG) project in northern China through a comparative study and the numerical results confirm the cost-effectiveness of the proposed solution.

Suggested Citation

  • Chen, Xianqing & Dong, Wei & Yang, Qiang, 2022. "Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009436
    DOI: 10.1016/j.apenergy.2022.119642
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    Cited by:

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    2. Wei Wei & Li Ye & Yi Fang & Yingchun Wang & Xi Chen & Zhenhua Li, 2023. "Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    3. Tan, Mao & Li, Zibin & Su, Yongxin & Ren, Yuling & Wang, Ling & Wang, Rui, 2024. "Dual time-scale robust optimization for energy management of distributed energy community considering source-load uncertainty," Renewable Energy, Elsevier, vol. 226(C).
    4. Wenshuai Bai & Dian Wang & Zhongquan Miao & Xiaorong Sun & Jiabin Yu & Jiping Xu & Yuqing Pan, 2023. "The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    5. Fabian Zuñiga-Cortes & Eduardo Caicedo-Bravo & Juan D. Garcia-Racines, 2023. "Reference Framework Based on a Two-Stage Strategy for Sizing and Operational Management in Electrical Microgrid Planning," Sustainability, MDPI, vol. 15(19), pages 1-27, October.
    6. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2022. "Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 866-880.
    7. Kerscher, Selina & Koirala, Arpan & Arboleya, Pablo, 2024. "Grid-optimal energy community planning from a systems perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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