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Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties

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  • Pinto, Rafael S.
  • Unsihuay-Vila, Clodomiro
  • Tabarro, Fabricio H.

Abstract

Power systems have incorporated many new technologies in recent years and the introduction of energy storage systems and demand response provides additional challenges for their operation and expansion planning. Current distribution networks are complex active networks where many technologies are included, which must be considered and properly represented in expansion planning models. Thus, this paper proposes a robust model to solve the coordinated operation and expansion planning of active distribution networks with multiple microgrids, distributed energy resources (DERs), demand response, and N-1 generation contingency. The objective is to determine the best expansion planning proposals and the optimal daily operation (24-hour representation) for the DERs under the worst realization of uncertainties, using a contingency-constrained approach. Besides, the load is represented by non-controllable, controllable, and deferrable portions. The model is formulated as a tri-level problem that is solved using a two-stage robust optimization approach. The proposed method is illustrated using a modified version of the IEEE 123-bus test system. The results show that the coordinated operation and expansion planning decreases the total cost of the problem and provides more consistent results for demand response and DERs coordination. Simulations show a reduction of 13.8% in the total cost if the daily operation and demand response are represented, and the inclusion of contingencies provides a reduction of 70% in the EENS (expected energy not served) reliability index for the case study.

Suggested Citation

  • Pinto, Rafael S. & Unsihuay-Vila, Clodomiro & Tabarro, Fabricio H., 2021. "Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties," Applied Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:appene:v:297:y:2021:i:c:s0306261921005523
    DOI: 10.1016/j.apenergy.2021.117108
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    References listed on IDEAS

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    2. Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    3. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    4. Xiang, Yue & Dai, Jiakun & Xue, Ping & Liu, Junyong, 2023. "Autonomous topology planning for distribution network expansion: A learning-based decoupled optimization method," Applied Energy, Elsevier, vol. 348(C).
    5. Liu, Hong & Cao, Yuchen & Ge, Shaoyun & Xu, Zhengyang & Gu, Chenghong & He, Xingtang, 2022. "Load carrying capability of regional electricity-heat energy systems: Definitions, characteristics, and optimal value evaluation," Applied Energy, Elsevier, vol. 310(C).
    6. Rahim, Sahar & Wang, Zhen & Ju, Ping, 2022. "Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: A systematic review," Applied Energy, Elsevier, vol. 319(C).
    7. Qiu, Yibin & Li, Qi & Wang, Tianhong & Yin, Liangzhen & Chen, Weirong & Liu, Hong, 2022. "Optimal planning of Cross-regional hydrogen energy storage systems considering the uncertainty," Applied Energy, Elsevier, vol. 326(C).

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