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Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory

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  • Shojaei, Amir Hossein
  • Ghadimi, Ali Asghar
  • Miveh, Mohammad Reza
  • Gandoman, Foad H.
  • Ahmadi, Abdollah

Abstract

This study deals with multiobjective reactive power planning, considering the uncertainties of load demand and wind power generation. The main feature of the current study is to examine the impact of multiple uncertainties on Reactive Power Planning (RPP), while several objectives exist. To fulfill this goal, the Information Gap Decision Theory (IGDT) is used to handle the uncertainties of load demand and wind power production. In order to cope with the probabilistic optimal RPP problem and to create Pareto optimal solutions, the ε-Constraint method is utilized. Fuzzy Decision Maker (FDM) and min-max approach are jointly applied to find the Best Compromise Solution (BCS). To evaluate the efficiency and the proficiency of the proposed multiobjective RPP model, it is implemented on the IEEE-30 bus test system via the GAMS software environment. To prove the superiority of the proposed model, the obtained results are compared with the scenario-based approach. The results imply that for specific amounts of uncertainty, the IGDT method performs reasonably towards the scenario-based approach.

Suggested Citation

  • Shojaei, Amir Hossein & Ghadimi, Ali Asghar & Miveh, Mohammad Reza & Gandoman, Foad H. & Ahmadi, Abdollah, 2021. "Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory," Renewable Energy, Elsevier, vol. 163(C), pages 1427-1443.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:1427-1443
    DOI: 10.1016/j.renene.2020.06.129
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    References listed on IDEAS

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    1. Alonso, Monica & Amaris, Hortensia & Alvarez-Ortega, Carlos, 2012. "A multiobjective approach for reactive power planning in networks with wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 180-191.
    2. Neculai Andrei, 2013. "Nonlinear Optimization Applications Using the GAMS Technology," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4614-6797-7, September.
    3. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    4. Weng, Yung-Tsai & Hsu, Yuan-Yih, 2016. "Reactive power control strategy for a wind farm with DFIG," Renewable Energy, Elsevier, vol. 94(C), pages 383-390.
    5. Li, Jian & Wang, Ni & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal reactive power dispatch of permanent magnet synchronous generator-based wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 145(C), pages 1-12.
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    Cited by:

    1. Zhang, Zhaoyi & Shang, Lei & Liu, Chengxi & Lai, Qiupin & Jiang, Youjin, 2023. "Consensus-based distributed optimal power flow using gradient tracking technique for short-term power fluctuations," Energy, Elsevier, vol. 264(C).
    2. Vahid-Ghavidel, Morteza & Shafie-khah, Miadreza & Javadi, Mohammad S. & Santos, Sérgio F. & Gough, Matthew & Quijano, Darwin A. & Catalao, Joao P.S., 2023. "Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system," Energy, Elsevier, vol. 265(C).
    3. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    4. Jair Salazar & Diego Carrión & Manuel Jaramillo, 2022. "Reactive Compensation Planning in Unbalanced Electrical Power Systems," Energies, MDPI, vol. 15(21), pages 1-18, October.

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