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Decentralized stochastic optimization based planning of integrated transmission and distribution networks with distributed generation penetration

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  • Liu, Jia
  • Cheng, Haozhong
  • Zeng, Pingliang
  • Yao, Liangzhong
  • Shang, Ce
  • Tian, Yuan

Abstract

In a current power system, numbers of distribution networks are physically connected to a transmission network at different boundary buses. As the planning solution of one network significantly influences the decisions made by planners of other networks, the transmission and distribution networks should coordinate and cooperate with each other to design the entire power system in a secure and economic manner. Inspired by decentralized and hierarchical optimization theories, this paper proposes a coordinated decision-making framework to determine the planning scheme and scenario based generation schedule for integrated transmission and distribution networks (ITDNs) with the penetration of distributed generations (DGs). A stochastic bi-level hierarchy is presented to decompose the centralized optimal planning of ITDNs. The obtained subproblems for independent transmission and distribution networks are formulated and relaxed to convex models. An improved iterative solution procedure is developed by exploiting the cascaded structure of the problems. Theoretical analysis and numerical results demonstrate the convergence properties of the decentralized optimization algorithm. The proposed coordinated planning framework outperforms conventional independent methods by decreasing expansion investment and improving DG accommodation.

Suggested Citation

  • Liu, Jia & Cheng, Haozhong & Zeng, Pingliang & Yao, Liangzhong & Shang, Ce & Tian, Yuan, 2018. "Decentralized stochastic optimization based planning of integrated transmission and distribution networks with distributed generation penetration," Applied Energy, Elsevier, vol. 220(C), pages 800-813.
    • Handle: RePEc:eee:appene:v:220:y:2018:i:c:p:800-813
    DOI: 10.1016/j.apenergy.2018.03.016
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    4. Zhang, Tao & Mu, Yunfei & Dong, Lei & Jia, Hongjie & Pu, Tianjiao & Wang, Xinying, 2023. "Fully parallel decentralized load restoration in coupled transmission and distribution system with soft open points," Applied Energy, Elsevier, vol. 349(C).
    5. Talal Alazemi & Mohamed Darwish & Mohammed Radi, 2022. "TSO/DSO Coordination for RES Integration: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, October.
    6. 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.
    7. Hirase, Y. & Noro, O. & Nakagawa, H. & Yoshimura, E. & Katsura, S. & Abe, K. & Sugimoto, K. & Sakimoto, K., 2018. "Decentralised and interlink-less power interchange among residences in microgrids using virtual synchronous generator control," Applied Energy, Elsevier, vol. 228(C), pages 2437-2447.
    8. Yi Luo & Yin Zhang & Muyi Tang & Youbin Zhou & Ying Wang & Defu Cai & Haiguang Liu, 2021. "A Novel Receiving End Grid Planning Method with Mutually Exclusive Constraints in Alternating Current/Direct Current Lines," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    9. Tang, Chong & Liu, Mingbo & Dai, Yue & Wang, Zhijun & Xie, Min, 2019. "Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.

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