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Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization

Author

Listed:
  • Minsheng Yang

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianqi Li

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Rui Du

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianying Li

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jian Sun

    (College of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China)

  • Xiaofang Yuan

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Jiazhu Xu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Shifu Huang

    (Changde Guoli Transformer Co., Ltd., Changde 415000, China)

Abstract

Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.

Suggested Citation

  • Minsheng Yang & Jianqi Li & Rui Du & Jianying Li & Jian Sun & Xiaofang Yuan & Jiazhu Xu & Shifu Huang, 2022. "Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization," Energies, MDPI, vol. 15(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2235-:d:774452
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    References listed on IDEAS

    as
    1. Mohamed Abd-El-Hakeem Mohamed & Ziad M. Ali & Mahrous Ahmed & Saad F. Al-Gahtani, 2021. "Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm," Energies, MDPI, vol. 14(11), pages 1-24, May.
    2. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2019. "Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms," Energies, MDPI, vol. 12(22), pages 1-36, November.
    3. Roustaei, M. & Niknam, T. & Salari, S. & Chabok, H. & Sheikh, M. & Kavousi-Fard, A. & Aghaei, J., 2020. "A scenario-based approach for the design of Smart Energy and Water Hub," Energy, Elsevier, vol. 195(C).
    4. Fang, Xin & Cui, Hantao & Yuan, Haoyu & Tan, Jin & Jiang, Tao, 2019. "Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    6. Sachin Kumar & Kumari Sarita & Akanksha Singh S Vardhan & Rajvikram Madurai Elavarasan & R. K. Saket & Narottam Das, 2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique," Energies, MDPI, vol. 13(21), pages 1-30, October.
    7. Zheng, Xuyue & Qiu, Yuwei & Zhan, Xiangyan & Zhu, Xingyi & Keirstead, James & Shah, Nilay & Zhao, Yingru, 2017. "Optimization based planning of urban energy systems: Retrofitting a Chinese industrial park as a case-study," Energy, Elsevier, vol. 139(C), pages 31-41.
    8. Lei, Yang & Wang, Dan & Jia, Hongjie & Chen, Jingcheng & Li, Jingru & Song, Yi & Li, Jiaxi, 2020. "Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
    9. Bai, Linquan & Li, Fangxing & Cui, Hantao & Jiang, Tao & Sun, Hongbin & Zhu, Jinxiang, 2016. "Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 270-279.
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    Cited by:

    1. Yi Zhang & Pengtao Liu, 2023. "Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm," Energies, MDPI, vol. 16(20), pages 1-20, October.

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