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A flexible active distribution system expansion planning model: A risk-based approach

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  • Moradijoz, M.
  • Moghaddam, M. Parsa
  • Haghifam, M.R.

Abstract

This paper presents an active distribution network expansion planning framework, which concurrently uses the renewable distributed generations and energy storage systems as capacity expansion options. In order to enhance the network reliability, the model takes into account the island mode operation of the renewable resources and energy storage systems. The proposed planning framework, which is modeled as a probabilistic bi-level optimization problem quantifies and controls the economic risk level associated with the stochastic nature of these resources. The master level is devoted to the here-and-now decisions in the planning phase, whereas the slave level, which is formulated as a two-stage model, is related to the wait-and-see decisions in the operational phase. At the first stage of the slave level, the network operational behaviour is determined by performing an optimal power flow modeled as a mixed-integer linear programming problem. At the second stage of the slave problem, the network reliability is optimized considering island mode operation and taking into account energy-limited nature of storage systems. The effectiveness of the proposed active distribution network planning model is demonstrated through several case studies. Simulation results demonstrate that the proposed approach can result in a flexible low-risk plan for the expansion of the network.

Suggested Citation

  • Moradijoz, M. & Moghaddam, M. Parsa & Haghifam, M.R., 2018. "A flexible active distribution system expansion planning model: A risk-based approach," Energy, Elsevier, vol. 145(C), pages 442-457.
    • Handle: RePEc:eee:energy:v:145:y:2018:i:c:p:442-457
    DOI: 10.1016/j.energy.2017.12.160
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    Citations

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    Cited by:

    1. Wang, Jueying & Hu, Zhijian & Xie, Shiwei, 2019. "Expansion planning model of multi-energy system with the integration of active distribution network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Shen, Lu & Dou, Xiaobo & Long, Huan & Li, Chen & Chen, Kang & Zhou, Ji, 2021. "A collaborative voltage optimization utilizing flexibility of community heating systems for high PV penetration," Energy, Elsevier, vol. 232(C).
    3. 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).
    4. Dong Zhang & GM Shafiullah & Choton Kanti Das & Kok Wai Wong, 2023. "Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks," Energies, MDPI, vol. 16(20), pages 1-35, October.
    5. Habiba Drias & Lydia Sonia Bendimerad & Yassine Drias, 2022. "A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-20, January.
    6. Alex Valenzuela & Iván Montalvo & Esteban Inga, 2019. "A Decision-Making Tool for Electric Distribution Network Planning Based on Heuristics and Georeferenced Data," Energies, MDPI, vol. 12(21), pages 1-18, October.
    7. Moradijoz, Mahnaz & Moradijoz, Saeed & Moghaddam, Mohsen Parsa & Haghifam, Mahmoud-Reza, 2020. "Flexibility enhancement in active distribution networks through a risk-based optimal placement of sectionalizing switches," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    8. Zhong, Haiwang & Zhang, Guanglun & Tan, Zhenfei & Ruan, Guangchun & Wang, Xuan, 2022. "Hierarchical collaborative expansion planning for transmission and distribution networks considering transmission cost allocation," Applied Energy, Elsevier, vol. 307(C).

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