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A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks

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  • Umme Mumtahina

    (School of Engineering and Technology, Central Queensland University, Rockhampton 4701, Australia)

  • Sanath Alahakoon

    (School of Engineering and Technology, Central Queensland University, Rockhampton 4701, Australia)

  • Peter Wolfs

    (EleXsys Ptd Ltd., Brisbane 4072, Australia)

Abstract

The existing distribution networks were designed at a time when there was virtually no embedded generation. The design methods ensured the voltage at various parts of the network remained within the limits required by standards, and for the most part, this was very successfully achieved. As Distributed Energy Resources (DERs) started to grow, the rise in voltage due to injected currents and the local impedances started to push network voltages toward, and even above, the desired upper limits. Voltage limits are based on typical appliance requirements, and long-term over-voltages will ultimately result in unacceptably short appliance life spans. Distribution Static Compensators (dSTATCOMs) are shunt-connected devices that can improve low-voltage networks’ performance by injecting currents that do not transfer real power. The currents can be reactive, negative or zero sequence, or harmonic. System performance can be improved by reducing conduction loss, improving voltage profile and voltage balance, or reducing Total Harmonic Distortion (THD). To obtain these benefits, optimal sizes of dSTATCOMs need to be placed at optimal locations within the distribution network. This paper has considered seventy research articles published over the past years related to the optimal placement and sizing of dSTATCOMs. In this study, minimization of power losses, voltage profile improvement, loadablity factor, voltage sag mitigation, and reduction in annual operating costs are considered fitness functions that are subjected to multiple constraint sets. The optimization algorithms found in the literature are categorized into six methods: analytical methods, artificial neural network-based methods, sensitivity approaches, metaheuristic methods, a combination of metaheuristic and sensitivity analysis, and miscellaneous. This study also presents a comparison among distribution network types, load flow methods optimization tools, etc. Therefore, a comprehensive review of optimal allocation and sizing of dSTATCOMs in distribution networks is presented in this paper, and guidance for future research is also provided.

Suggested Citation

  • Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2023. "A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks," Energies, MDPI, vol. 16(17), pages 1-38, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6122-:d:1222715
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    References listed on IDEAS

    as
    1. Jun-Hao Chen & Kuang-Hsiung Tan & Yih-Der Lee, 2022. "Intelligent Controlled DSTATCOM for Power Quality Enhancement," Energies, MDPI, vol. 15(11), pages 1-19, May.
    2. Ferminus Raj, A. & Gnana Saravanan, A., 2023. "An optimization approach for optimal location & size of DSTATCOM and DG," Applied Energy, Elsevier, vol. 336(C).
    3. Syed Ali Abbas Kazmi & Usama Ameer Khan & Hafiz Waleed Ahmad & Sajid Ali & Dong Ryeol Shin, 2020. "A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth," Energies, MDPI, vol. 13(6), pages 1-71, March.
    4. Nien-Che Yang & Zhe-Hao Chang, 2023. "Optimal Capacity and Location for STATCOM with Seasonal Wind Power Prediction Using C-Vine Copula," Mathematics, MDPI, vol. 11(13), pages 1-21, June.
    5. Valeriya Tuzikova & Josef Tlusty & Zdenek Muller, 2018. "A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM," Energies, MDPI, vol. 11(10), pages 1-15, October.
    6. Ehsan, Ali & Yang, Qiang, 2019. "State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review," Applied Energy, Elsevier, vol. 239(C), pages 1509-1523.
    7. Li, Yang & Feng, Bo & Wang, Bin & Sun, Shuchao, 2022. "Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach," Energy, Elsevier, vol. 245(C).
    8. Niknam, Taher & Taheri, Seyed Iman & Aghaei, Jamshid & Tabatabaei, Sajad & Nayeripour, Majid, 2011. "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources," Applied Energy, Elsevier, vol. 88(12), pages 4817-4830.
    9. Rohouma, Wesam & Balog, Robert S. & Peerzada, Aaqib Ahmad & Begovic, Miroslav M., 2020. "D-STATCOM for harmonic mitigation in low voltage distribution network with high penetration of nonlinear loads," Renewable Energy, Elsevier, vol. 145(C), pages 1449-1464.
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