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Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders

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  • Zhou, Yulu
  • Zhang, Jingrui

Abstract

The increasing penetration of renewable energy resources (RES) has prompted the gradual evolution of distribution systems from passive networks to active ones. The independent operators of various microgirds and active users urgently need a new optimization scheme to replace the traditional centralized scheduling method in active distribution network (ADN). This paper proposes a three-layer day-ahead optimal schedule mechanism taking account of multi-stakeholders in ADN. In the proposed mechanism, the distribution network (DN) is divided into three layers structurally: User layer, microgrid (MG) layer and DN layer. Meanwhile, the corresponding three-layer optimization method is proposed correspondingly. Firstly, the optimal user power consumption scheme and the optimal user energy storage operation scheme can be obtained by minimizing the total cost of an active user. Subsequently, the MG economic dispatch is performed within the obtained user power purchase information from the User layer. By ensuring the interests of active users, an optimal MG economic dispatch scheme can be obtained, in which the information of the connection point between the DN and the MG is transmitted to the upper DN as one of the constraints in the optimization of DN layer. Finally, the third-layer optimization is performed to obtain the optimization results of the DN layer. An actual 47-bus distribution system is employed to verify the proposed three-layer optimizing framework and the results show the effectiveness of the proposed method.

Suggested Citation

  • Zhou, Yulu & Zhang, Jingrui, 2020. "Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313700
    DOI: 10.1016/j.energy.2020.118263
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    References listed on IDEAS

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    1. Shams, Mohammad H. & Shahabi, Majid & Khodayar, Mohammad E., 2018. "Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response," Energy, Elsevier, vol. 155(C), pages 326-338.
    2. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Yu, Hao & Wu, Jianzhong, 2019. "Quantified analysis method for operational flexibility of active distribution networks with high penetration of distributed generators," Applied Energy, Elsevier, vol. 239(C), pages 706-714.
    3. Mahdavi, Sajad & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2018. "Two-level planning for coordination of energy storage systems and wind-solar-diesel units in active distribution networks," Energy, Elsevier, vol. 151(C), pages 954-965.
    4. Xie, Min & Ji, Xiang & Hu, Xintong & Cheng, Peijun & Du, Yuxin & Liu, Mingbo, 2018. "Autonomous optimized economic dispatch of active distribution system with multi-microgrids," Energy, Elsevier, vol. 153(C), pages 479-489.
    5. Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2015. "Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response," Energy, Elsevier, vol. 86(C), pages 649-660.
    6. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    7. Kia, Mohsen & Setayesh Nazar, Mehrdad & Sepasian, Mohammad Sadegh & Heidari, Alireza & Siano, Pierluigi, 2017. "An efficient linear model for optimal day ahead scheduling of CHP units in active distribution networks considering load commitment programs," Energy, Elsevier, vol. 139(C), pages 798-817.
    8. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    9. Bai, Linquan & Jiang, Tao & Li, Fangxing & Chen, Houhe & Li, Xue, 2018. "Distributed energy storage planning in soft open point based active distribution networks incorporating network reconfiguration and DG reactive power capability," Applied Energy, Elsevier, vol. 210(C), pages 1082-1091.
    10. Zhang, Shenxi & Cheng, Haozhong & Wang, Dan & Zhang, Libo & Li, Furong & Yao, Liangzhong, 2018. "Distributed generation planning in active distribution network considering demand side management and network reconfiguration," Applied Energy, Elsevier, vol. 228(C), pages 1921-1936.
    11. Najafzad, Hamid & Davari-Ardakani, Hamed & Nemati-Lafmejani, Reza, 2019. "Multi-skill project scheduling problem under time-of-use electricity tariffs and shift differential payments," Energy, Elsevier, vol. 168(C), pages 619-636.
    12. Chang Ye & Shihong Miao & Yaowang Li & Chao Li & Lixing Li, 2018. "Hierarchical Scheduling Scheme for AC/DC Hybrid Active Distribution Network Based on Multi-Stakeholders," Energies, MDPI, vol. 11(10), pages 1-16, October.
    13. Zhang, Lu & Shen, Chen & Chen, Ying & Huang, Shaowei & Tang, Wei, 2018. "Coordinated allocation of distributed generation, capacitor banks and soft open points in active distribution networks considering dispatching results," Applied Energy, Elsevier, vol. 231(C), pages 1122-1131.
    Full references (including those not matched with items on IDEAS)

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    3. Zhang, Jingrui & Zhou, Yulu & Li, Zhuoyun & Cai, Junfeng, 2021. "Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach," Energy, Elsevier, vol. 219(C).
    4. Zakernezhad, Hamid & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage," Applied Energy, Elsevier, vol. 314(C).

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