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Application of Ordinal Optimization to Reactive Volt-Ampere Sources Planning Problems

Author

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  • Wen-Tung Lee

    (Department of Computer Science & Information Engineering, Yuan Ze University, Taoyuan City 32003, Taiwan)

  • Shih-Cheng Horng

    (Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan)

  • Chi-Fang Lin

    (Department of Computer Science & Information Engineering, Yuan Ze University, Taoyuan City 32003, Taiwan)

Abstract

Reactive volt-ampere sources planning is an effort to determine the most effective investment plan for new reactive sources at given load buses while ensuring appropriate voltage profile and satisfying operational constraints. Optimization of reactive volt-ampere sources planning is not only a difficult problem in power systems, but also a large-dimension constrained optimization problem. In this paper, an ordinal optimization-based approach containing upper and lower level is developed to solve this problem efficiently. In the upper level, an ordinal search (OS) algorithm is utilized to select excellent designs from a candidate-design set according to the system’s structural information exploited from the simulations executed in the lower level. There are five stages in the ordinal search algorithm, which gradually narrow the design space to search for a good capacitor placement pattern. The IEEE 118-bus and IEEE 244-bus systems with four load cases are employed as the test examples. The proposed approach is compared with two competing methods; the genetic algorithm and Tabu search, and a commercial numerical libraries (NL) mixed integer programming tool; IMSL Numerical Libraries. Experimental results illustrate that the proposed approach yields an outstanding design with a higher quality and efficiency for solving reactive volt-ampere sources planning problem.

Suggested Citation

  • Wen-Tung Lee & Shih-Cheng Horng & Chi-Fang Lin, 2019. "Application of Ordinal Optimization to Reactive Volt-Ampere Sources Planning Problems," Energies, MDPI, vol. 12(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2746-:d:249314
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    References listed on IDEAS

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    1. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    2. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    3. Yangwu Shen & Feifan Shen & Yaling Chen & Liqing Liang & Bin Zhang & Deping Ke, 2018. "Reactive Power Planning for Regional Power Grids Based on Active and Reactive Power Adjustments of DGs," Energies, MDPI, vol. 11(6), pages 1-17, June.
    4. Roberts, Justo José & Marotta Cassula, Agnelo & Silveira, José Luz & da Costa Bortoni, Edson & Mendiburu, Andrés Z., 2018. "Robust multi-objective optimization of a renewable based hybrid power system," Applied Energy, Elsevier, vol. 223(C), pages 52-68.
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    1. Raj, Saurav & Mahapatra, Sheila & Babu, Rohit & Verma, Sumit, 2023. "Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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