IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p2997-d559848.html
   My bibliography  Save this article

Unbalanced Voltage Compensation with Optimal Voltage Controlled Regulators and Load Ratio Control Transformer

Author

Listed:
  • Akito Nakadomari

    (Department of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

  • Ryuto Shigenobu

    (Department of Electrical, Electronic and Computer Engineering, University of Fukui 3-9-1, Bunkyo, Fukui 910-8507, Japan)

  • Takeyoshi Kato

    (Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan)

  • Narayanan Krishnan

    (Department of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India)

  • Ashraf Mohamed Hemeida

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

  • Hiroshi Takahashi

    (Fuji Electric Co., Ltd., Tokyo 191-8506, Japan)

  • Tomonobu Senjyu

    (Department of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)

Abstract

Penetration of equipment such as photovoltaic power generations (PV), heat pump water heaters (HP), and electric vehicles (EV) introduces voltage unbalance issues in distribution systems. Controlling PV and energy storage system (ESS) outputs or coordinated EV charging are investigated for voltage unbalance compensation. However, some issues exist, such as dependency on installed capacity and fairness among consumers. Therefore, the ideal way to mitigate unbalanced voltages is to use grid-side equipment mainly. This paper proposes a voltage unbalance compensation based on optimal tap operation scheduling of three-phase individual controlled step voltage regulators (3 ϕ SVR) and load ratio control transformer (LRT). In the formulation of the optimization problem, multiple voltage unbalance metrics are comprehensively included. In addition, voltage deviations, network losses, and coordinated tap operations, which are typical issues in distribution systems, are considered. In order to investigate the mutual influence among voltage unbalance and other typical issues, various optimization problems are formulated, and then they are compared by numerical simulations. The results show that the proper operation of 3 ϕ SVRs and LRT effectively mitigates voltage unbalance. Furthermore, the results also show that voltage unbalances and other typical issues can be improved simultaneously with appropriate formulations.

Suggested Citation

  • Akito Nakadomari & Ryuto Shigenobu & Takeyoshi Kato & Narayanan Krishnan & Ashraf Mohamed Hemeida & Hiroshi Takahashi & Tomonobu Senjyu, 2021. "Unbalanced Voltage Compensation with Optimal Voltage Controlled Regulators and Load Ratio Control Transformer," Energies, MDPI, vol. 14(11), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:2997-:d:559848
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/2997/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/11/2997/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    2. Carpinelli, G. & Mottola, F. & Proto, D. & Varilone, P., 2017. "Minimizing unbalances in low-voltage microgrids: Optimal scheduling of distributed resources," Applied Energy, Elsevier, vol. 191(C), pages 170-182.
    3. Yue Zhang & Anurag Srivastava, 2021. "Voltage Control Strategy for Energy Storage System in Sustainable Distribution System Operation," Energies, MDPI, vol. 14(4), pages 1-12, February.
    4. Ryuto Shigenobu & Akito Nakadomari & Ying-Yi Hong & Paras Mandal & Hiroshi Takahashi & Tomonobu Senjyu, 2020. "Optimization of Voltage Unbalance Compensation by Smart Inverter," Energies, MDPI, vol. 13(18), pages 1-22, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammad Alathamneh & Haneen Ghanayem & Xingyu Yang & R. M. Nelms, 2022. "Three-Phase Grid-Connected Inverter Power Control under Unbalanced Grid Conditions Using a Time-Domain Symmetrical Components Extraction Method," Energies, MDPI, vol. 15(19), pages 1-16, September.
    2. Tomonobu Senjyu & Mahdi Khosravy, 2022. "Power System Planning and Quality Control," Energies, MDPI, vol. 15(14), pages 1-2, July.
    3. Mohammad Alathamneh & Haneen Ghanayem & Xingyu Yang & R. M. Nelms, 2022. "Three-Phase Grid-Connected Inverter Power Control under Unbalanced Grid Conditions Using a Proportional-Resonant Control Method," Energies, MDPI, vol. 15(19), pages 1-17, September.
    4. Mohammad Alathamneh & Haneen Ghanayem & R. M. Nelms, 2022. "Bidirectional Power Control for a Three-Phase Grid-Connected Inverter under Unbalanced Grid Conditions Using a Proportional-Resonant and a Modified Time-Domain Symmetrical Components Extraction Method," Energies, MDPI, vol. 15(24), pages 1-23, December.
    5. Luis Guasch-Pesquer & Sara García-Ríos & Adolfo Andres Jaramillo-Matta & Enric Vidal-Idiarte, 2022. "Improved Method for Determining Voltage Unbalance Factor Using Induction Motors," Energies, MDPI, vol. 15(23), pages 1-13, December.
    6. Yih-Der Lee & Wei-Chen Lin & Jheng-Lun Jiang & Jia-Hao Cai & Wei-Tzer Huang & Kai-Chao Yao, 2021. "Optimal Individual Phase Voltage Regulation Strategies in Active Distribution Networks with High PV Penetration Using the Sparrow Search Algorithm," Energies, MDPI, vol. 14(24), pages 1-22, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chiara Gruden & Irena Ištoka Otković & Matjaž Šraml, 2020. "Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    2. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    3. Thibaud Deguilhem & Juliette Schlegel & Jean-Philippe Berrou & Ousmane Djibo & Alain Piveteau, 2024. "Too many options: How to identify coalitions in a policy network?," Post-Print hal-04689665, HAL.
    4. Anurag Agarwal, 2009. "Theoretical insights into the augmented-neural-network approach for combinatorial optimization," Annals of Operations Research, Springer, vol. 168(1), pages 101-117, April.
    5. Mohammad Javad Feizollahi & Igor Averbakh, 2014. "The Robust (Minmax Regret) Quadratic Assignment Problem with Interval Flows," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 321-335, May.
    6. Nha Vo‐Thanh & Hans‐Peter Piepho, 2023. "Generating designs for comparative experiments with two blocking factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3574-3585, December.
    7. Сластников С.А., 2014. "Применение Метаэвристических Алгоритмов Для Задачи Маршрутизации Транспорта," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 117-126, январь.
    8. H. A. J. Crauwels & C. N. Potts & L. N. Van Wassenhove, 1998. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 341-350, August.
    9. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    10. Nair, D.J. & Grzybowska, H. & Fu, Y. & Dixit, V.V., 2018. "Scheduling and routing models for food rescue and delivery operations," Socio-Economic Planning Sciences, Elsevier, vol. 63(C), pages 18-32.
    11. Cazzaro, Davide & Fischetti, Martina & Fischetti, Matteo, 2020. "Heuristic algorithms for the Wind Farm Cable Routing problem," Applied Energy, Elsevier, vol. 278(C).
    12. İbrahim Muter & Ş. İlker Birbil & Güvenç Şahin, 2010. "Combination of Metaheuristic and Exact Algorithms for Solving Set Covering-Type Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 603-619, November.
    13. Kadri Sylejmani & Jürgen Dorn & Nysret Musliu, 2017. "Planning the trip itinerary for tourist groups," Information Technology & Tourism, Springer, vol. 17(3), pages 275-314, September.
    14. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    15. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "Optimization of manufacturing systems using a neural network metamodel with a new training approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1191-1197, September.
    16. S-W Lin & K-C Ying, 2008. "A hybrid approach for single-machine tardiness problems with sequence-dependent setup times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1109-1119, August.
    17. J-F Chen & T-H Wu, 2006. "Vehicle routing problem with simultaneous deliveries and pickups," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 579-587, May.
    18. Joseph B. Mazzola & Robert H. Schantz, 1997. "Multiple‐facility loading under capacity‐based economies of scope," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(3), pages 229-256, April.
    19. Kadri Sylejmani & Jürgen Dorn & Nysret Musliu, 0. "Planning the trip itinerary for tourist groups," Information Technology & Tourism, Springer, vol. 0, pages 1-40.
    20. Abdmouleh, Zeineb & Gastli, Adel & Ben-Brahim, Lazhar & Haouari, Mohamed & Al-Emadi, Nasser Ahmed, 2017. "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, Elsevier, vol. 113(C), pages 266-280.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:2997-:d:559848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.