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Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm

Author

Listed:
  • Hao He

    (Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China)

  • Jia Li

    (Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China)

  • Weizhe Zhao

    (Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China)

  • Boyang Li

    (School of Mechanical Electronic and Information Engineering, University of Mining and Technology (Beijing), Beijing 100083, China)

  • Yalong Li

    (School of Mechanical Electronic and Information Engineering, University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

In order to solve the reactive power and voltage control problem caused by the high proportion of new energy connected to the power grid, this paper takes the minimum voltage deviation, minimum network loss and maximum dynamic reactive power margin of the whole system as the comprehensive optimization objectives and establishes a reactive power and voltage optimization model by considering the reactive power regulation ability of SVC (Static Var Compensator) and new energy units. In view of the continuous and discrete variables in the model, the traditional continuous FPA (Flower Pollination Algorithm) is discretized to form an improved continuous-discrete hybrid FPA, and the tournament selection mechanism is adopted to speed up the convergence. Through the example analysis of the IEEE-39 bus system, the feasibility of the proposed reactive power and voltage optimal control method in the new energy grid is verified. Compared with GA (Genetic Algorithm), the results show that the improved FPA has high optimization accuracy, which is suitable for solving the reactive power and voltage optimization problem of the new energy grid.

Suggested Citation

  • Hao He & Jia Li & Weizhe Zhao & Boyang Li & Yalong Li, 2022. "Reactive Power and Voltage Optimization of New-Energy Grid Based on the Improved Flower Pollination Algorithm," Energies, MDPI, vol. 15(10), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3653-:d:817020
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    References listed on IDEAS

    as
    1. Danalakshmi D. & Gopi R. & A. Hariharasudan & Iwona Otola & Yuriy Bilan, 2020. "Reactive Power Optimization and Price Management in Microgrid Enabled with Blockchain," Energies, MDPI, vol. 13(23), pages 1-20, November.
    2. Fengli Jiang & Yichi Zhang & Yu Zhang & Xiaomeng Liu & Chunling Chen, 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization," Energies, MDPI, vol. 12(9), pages 1-14, May.
    3. Linan Qu & Shujie Zhang & Hsiung-Cheng Lin & Ning Chen & Lingling Li, 2020. "Multiobjective Reactive Power Optimization of Renewable Energy Power Plants Based on Time-and-Space Grouping Method," Energies, MDPI, vol. 13(14), pages 1-15, July.
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