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Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

Author

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  • Olusayo A. Ajeigbe

    (Department of Electrical Engineering, French South African Technology Institute, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Staatsartillerie Road, Pretoria 0183, South Africa)

  • Josiah L. Munda

    (Department of Electrical Engineering, French South African Technology Institute, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Staatsartillerie Road, Pretoria 0183, South Africa)

  • Yskandar Hamam

    (Department of Electrical Engineering, French South African Technology Institute, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Staatsartillerie Road, Pretoria 0183, South Africa
    ESIEE Paris, 2 Boulevard Blaise Pascal, Cité Descartes, BP 99, 93162 Noisy-le-Grand, France)

Abstract

This paper solves the allocation planning problem of integrating large scale renewable energy hybrid distributed generations and capacitor banks into the distribution systems. Extraordinarily, the integration of renewable energy hybrid distributed generations such as solar photovoltaic, wind, and biomass takes into consideration the impact assessment of variable generations from PV and wind on the distribution networks’ long term dynamic voltage and small-signal stabilities. Unlike other renewable distributed generations, the variability of power from solar PV and wind generations causes small-signal instabilities if they are sub-optimally allocated in the distribution network. Hence, the variables related to small-signal stability are included and constrained in the model, unlike what is obtainable in the current works on the planning of optimal allocation of renewable distributed generations. Thus, the model is motivated to maximize the penetration of renewable powers by minimizing the net present value of total cost, which includes investment, maintenance, energy, and emission costs. Consequently, the optimization problem is formulated as a stochastic mixed integer linear program, which ensures limited convergence to optimality. Numerical results of the proposed model demonstrate a significant reduction in electricity and emission costs, enhancement of system dynamic voltage and small-signal stabilities, as well as improvement in welfare costs and environmental goodness.

Suggested Citation

  • Olusayo A. Ajeigbe & Josiah L. Munda & Yskandar Hamam, 2019. "Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement," Energies, MDPI, vol. 12(24), pages 1-31, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4777-:d:298117
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    References listed on IDEAS

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    1. Dicorato, M. & Forte, G. & Trovato, M., 2008. "Environmental-constrained energy planning using energy-efficiency and distributed-generation facilities," Renewable Energy, Elsevier, vol. 33(6), pages 1297-1313.
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    Cited by:

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    2. Adedayo Owosuhi & Yskandar Hamam & Josiah Munda, 2023. "Maximizing the Integration of a Battery Energy Storage System–Photovoltaic Distributed Generation for Power System Harmonic Reduction: An Overview," Energies, MDPI, vol. 16(6), pages 1-22, March.
    3. Abdullah Albaker & Mansoor Alturki & Rabeh Abbassi & Khalid Alqunun, 2022. "Zonal-Based Optimal Microgrids Identification," Energies, MDPI, vol. 15(7), pages 1-15, March.
    4. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    5. Xu, Bin & Luo, Yuemei & Xu, Renjing & Chen, Jianbao, 2021. "Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model," Energy, Elsevier, vol. 236(C).
    6. Huiru Zhao & Hao Lu & Xuejie Wang & Bingkang Li & Yuwei Wang & Pei Liu & Zhao Ma, 2020. "Research on Comprehensive Value of Electrical Energy Storage in CCHP Microgrid with Renewable Energy Based on Robust Optimization," Energies, MDPI, vol. 13(24), pages 1-22, December.

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