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A data-driven approach for microgrid distributed generation planning under uncertainties

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  • Yin, Mingjia
  • Li, Kang
  • Yu, James

Abstract

The increasing demand for power system decarbonization and resilience raises the necessity of incorporating the renewable distributed generation (DG) into the microgrid planning. The complexity of the microgrid renewable DG planning largely roots from the intermittent wind and solar energy and load variations throughout the planning period. This paper proposes a novel two-stage data-driven adaptive robust distributed generation planning (DDARDGP) framework considering both grid-connected and islanded modes of microgrids, wherein the overall system cost is minimized. By leveraging the spatio-temporal property of historical weather and grid information, a compact uncertainty set is developed based on a data-driven Bayesian nonparametric approach. The problem is further solved by a modified column and constraint generation (CC&G) algorithm. In the study, the effectiveness of the proposed framework is demonstrated using a modified IEEE 33-bus test system. The case study considers the optimal generation sizing, allocation and mixtures. The simulation results confirm that the proposed data-driven uncertainty set adapts well to the increase of data dimensions and solves the over-conservatism issue, leading to 34.14% reduction in uncertainty estimation compared with the traditional budget uncertainty set. Accordingly, the total cost can achieve a $23,185 reduction under the proposed DDARDGP framework.

Suggested Citation

  • Yin, Mingjia & Li, Kang & Yu, James, 2022. "A data-driven approach for microgrid distributed generation planning under uncertainties," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016561
    DOI: 10.1016/j.apenergy.2021.118429
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    References listed on IDEAS

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    Cited by:

    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. Ning Ren & Xiufan Zhang & Decheng Fan, 2022. "Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019," IJERPH, MDPI, vol. 19(23), pages 1-24, November.
    3. Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
    4. Yang, Xiaohui & Wang, Xiaopeng & Deng, Yeheng & Mei, Linghao & Deng, Fuwei & Zhang, Zhonglian, 2023. "Integrated energy system scheduling model based on non-complete interval multi-objective fuzzy optimization," Renewable Energy, Elsevier, vol. 218(C).
    5. 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.
    6. Shuaijia He & Junyong Liu, 2024. "Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response," Energies, MDPI, vol. 17(4), pages 1-15, February.

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