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Quantified flexibility evaluation of soft open points to improve distributed generator penetration in active distribution networks based on difference-of-convex programming

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  • Ji, Haoran
  • Wang, Chengshan
  • Li, Peng
  • Zhao, Jinli
  • Song, Guanyu
  • Wu, Jianzhong

Abstract

With the integration of high shares of distributed generators (DGs), both volatile DGs and demand-side resources make it increasingly difficult to cope with the various uncertainties and put forward a higher requirement for the operational flexibility in active distribution networks (ADNs). The highly integrated distribution-level power electronic devices represented by soft open point (SOP) significantly benefit the operation of ADNs. Due to the accurate and rapid power flow control provided by SOP, various flexible resources can be coordinated in the spatial and temporal aspects, which effectively increases the DG penetration of ADNs. In this paper, the multi-dimensional characteristics of distribution system flexibility are analysed and the maximum DG hosting capacity is adopted as the index to evaluate the flexibility brought by SOP. The flexibility evaluation model is further proposed to quantify the benefits of SOP-based flexible resources, such as conventional SOP, multi-terminal SOP and SOP with energy storage. Then, the original non-convex nonlinear model is converted into an effectively solved second-order cone programming (SOCP) model using convex relaxation. To tighten the deviation of convex relaxation to the predefined accuracy, a difference-of-convex programming (DCP)-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.

Suggested Citation

  • Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Wu, Jianzhong, 2018. "Quantified flexibility evaluation of soft open points to improve distributed generator penetration in active distribution networks based on difference-of-convex programming," Applied Energy, Elsevier, vol. 218(C), pages 338-348.
  • Handle: RePEc:eee:appene:v:218:y:2018:i:c:p:338-348
    DOI: 10.1016/j.apenergy.2018.02.170
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    3. 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.
    4. Li, Peng & Ji, Haoran & Yu, Hao & Zhao, Jinli & Wang, Chengshan & Song, Guanyu & Wu, Jianzhong, 2019. "Combined decentralized and local voltage control strategy of soft open points in active distribution networks," Applied Energy, Elsevier, vol. 241(C), pages 613-624.
    5. Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Ricardo Alberto Hincapié-Isaza & Mauricio Granada Echeverri & Alberto-Jesus Perea-Moreno, 2021. "Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA," Mathematics, MDPI, vol. 9(16), pages 1-18, August.
    6. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    7. Ibrahim Mohamed Diaaeldin & Shady H. E. Abdel Aleem & Ahmed El-Rafei & Almoataz Y. Abdelaziz & Ahmed F. Zobaa, 2020. "Enhancement of Hosting Capacity with Soft Open Points and Distribution System Reconfiguration: Multi-Objective Bilevel Stochastic Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    8. Guo Xinming & Huo Qunhai & Wei Tongzhen & Yin Jingyuan, 2020. "A Local Control Strategy for Distributed Energy Fluctuation Suppression Based on Soft Open Point," Energies, MDPI, vol. 13(6), pages 1-15, March.
    9. Bastami, Houman & Shakarami, Mahmoud Reza & Doostizadeh, Meysam, 2021. "A decentralized cooperative framework for multi-area active distribution network in presence of inter-area soft open points," Applied Energy, Elsevier, vol. 300(C).
    10. Escalera, Alberto & Prodanović, Milan & Castronuovo, Edgardo D. & Roldan-Perez, Javier, 2020. "Contribution of active management technologies to the reliability of power distribution networks," Applied Energy, Elsevier, vol. 267(C).
    11. Ricardo de Oliveira & Leonardo Willer de Oliveira & Edimar José de Oliveira, 2023. "Optimization Approach for Planning Soft Open Points in a MV-Distribution System to Maximize the Hosting Capacity," Energies, MDPI, vol. 16(3), pages 1-22, January.
    12. Su, Hongzhi & Wang, Chengshan & Li, Peng & Liu, Zhelin & Yu, Li & Wu, Jianzhong, 2019. "Optimal placement of phasor measurement unit in distribution networks considering the changes in topology," Applied Energy, Elsevier, vol. 250(C), pages 313-322.
    13. Klyapovskiy, Sergey & You, Shi & Michiorri, Andrea & Kariniotakis, George & Bindner, Henrik W., 2019. "Incorporating flexibility options into distribution grid reinforcement planning: A techno-economic framework approach," Applied Energy, Elsevier, vol. 254(C).

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