IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i21p4062-d280085.html
   My bibliography  Save this article

Reactive Power Compensation with PV Inverters for System Loss Reduction

Author

Listed:
  • Saša Vlahinić

    (Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia)

  • Dubravko Franković

    (Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia)

  • Vitomir Komen

    (HEP—Distribution system operator, Elektroprimorje, 51000 Rijeka, Croatia)

  • Anamarija Antonić

    (HOPS–Croatian transmission system operator, 51211 Matulji, Croatia)

Abstract

Photovoltaic (PV) system inverters usually operate at unitary power factor, injecting only active power into the system. Recently, many studies have been done analyzing potential benefits of reactive power provisioning, such as voltage regulation, congestion mitigation and loss reduction. This article analyzes possibilities for loss reduction in a typical medium voltage distribution system. Losses in the system are compared to the losses in the PV inverters. Different load conditions and PV penetration levels are considered and for each scenario various active power generation by PV inverters are taken into account, together with allowable levels of reactive power provisioning. As far as loss reduction is considered, there is very small number of PV inverters operating conditions for which positive energy balance exists. For low and medium load levels, there is no practical possibility for loss reduction. For high loading levels and higher PV penetration specific reactive savings, due to reactive power provisioning, increase and become bigger than additional losses in PV inverters, but for a very limited range of power factors.

Suggested Citation

  • Saša Vlahinić & Dubravko Franković & Vitomir Komen & Anamarija Antonić, 2019. "Reactive Power Compensation with PV Inverters for System Loss Reduction," Energies, MDPI, vol. 12(21), pages 1-17, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4062-:d:280085
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/21/4062/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/21/4062/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shunjiang Lin & Sen He & Haipeng Zhang & Mingbo Liu & Zhiqiang Tang & Hao Jiang & Yunong Song, 2019. "Robust Optimal Allocation of Decentralized Reactive Power Compensation in Three-Phase Four-Wire Low-Voltage Distribution Networks Considering the Uncertainty of Photovoltaic Generation," Energies, MDPI, vol. 12(13), pages 1-20, June.
    2. Hua Li & Che Wen & Kuei-Hsiang Chao & Ling-Ling Li, 2017. "Research on Inverter Integrated Reactive Power Control Strategy in the Grid-Connected PV Systems," Energies, MDPI, vol. 10(7), pages 1-21, July.
    3. Fermín Barrero-González & Victor Fernão Pires & José L. Sousa & João F. Martins & María Isabel Milanés-Montero & Eva González-Romera & Enrique Romero-Cadaval, 2019. "Photovoltaic Power Converter Management in Unbalanced Low Voltage Networks with Ancillary Services Support," Energies, MDPI, vol. 12(6), pages 1-16, March.
    4. Yunqi Xiao & Yi Wang & Yanping Sun, 2018. "Reactive Power Optimal Control of a Wind Farm for Minimizing Collector System Losses," Energies, MDPI, vol. 11(11), pages 1-15, November.
    5. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Srinivasan, Dipti & Reindl, Thomas, 2018. "Economic and technical analysis of reactive power provision from distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 827-841.
    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. Cícero Augusto de Souza & Diego Jose da Silva & Priscila Rossoni & Edmarcio Antonio Belati & Ademir Pelizari & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2023. "Multi-Period Optimal Power Flow with Photovoltaic Generation Considering Optimized Power Factor Control," Sustainability, MDPI, vol. 15(19), pages 1-20, September.
    2. Paolo Tenti & Tommaso Caldognetto, 2023. "Integration of Local and Central Control Empowers Cooperation among Prosumers and Distributors towards Safe, Efficient, and Cost-Effective Operation of Microgrids," Energies, MDPI, vol. 16(5), pages 1-23, February.
    3. Zbigniew Kłosowski & Łukasz Mazur, 2023. "Influence of the Type of Receiver on Electrical Energy Losses in Power Grids," Energies, MDPI, vol. 16(15), pages 1-22, July.
    4. González-Ordiano, Jorge Ángel & Mühlpfordt, Tillmann & Braun, Eric & Liu, Jianlei & Çakmak, Hüseyin & Kühnapfel, Uwe & Düpmeier, Clemens & Waczowicz, Simon & Faulwasser, Timm & Mikut, Ralf & Hagenmeye, 2021. "Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow," Applied Energy, Elsevier, vol. 302(C).
    5. Nevena Srećković & Miran Rošer & Gorazd Štumberger, 2021. "Utilization of Active Distribution Network Elements for Optimization of a Distribution Network Operation," Energies, MDPI, vol. 14(12), pages 1-17, June.

    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. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    2. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.
    3. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    4. Temitayo O. Olowu & Aditya Sundararajan & Masood Moghaddami & Arif I. Sarwat, 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey," Energies, MDPI, vol. 11(7), pages 1-32, July.
    5. Alam, Mollah Rezaul & Alam, M.J.E. & Somani, Abhishek & Melton, Ronald B. & Tushar, Wayes & Bai, Feifei & Yan, Ruifeng & Saha, Tapan K., 2021. "Evaluating the feasibility of transactive approach for voltage management using inverters of a PV plant," Applied Energy, Elsevier, vol. 291(C).
    6. Wu, Raphael & Sansavini, Giovanni, 2020. "Integrating reliability and resilience to support the transition from passive distribution grids to islanding microgrids," Applied Energy, Elsevier, vol. 272(C).
    7. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    8. Marco Badami & Gabriele Fambri & Salvatore Mancò & Mariapia Martino & Ioannis G. Damousis & Dimitrios Agtzidis & Dimitrios Tzovaras, 2019. "A Decision Support System Tool to Manage the Flexibility in Renewable Energy-Based Power Systems," Energies, MDPI, vol. 13(1), pages 1-16, December.
    9. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    10. Luigi Costanzo & Massimo Vitelli, 2019. "A Novel MPPT Technique for Single Stage Grid-Connected PV Systems: T4S," Energies, MDPI, vol. 12(23), pages 1-13, November.
    11. Mohamed Derbeli & Oscar Barambones & Jose Antonio Ramos-Hernanz & Lassaad Sbita, 2019. "Real-Time Implementation of a Super Twisting Algorithm for PEM Fuel Cell Power System," Energies, MDPI, vol. 12(9), pages 1-20, April.
    12. Mir Sayed Shah Danish & Tomonobu Senjyu & Sayed Mir Shah Danish & Najib Rahman Sabory & Narayanan K & Paras Mandal, 2019. "A Recap of Voltage Stability Indices in the Past Three Decades," Energies, MDPI, vol. 12(8), pages 1-18, April.
    13. Xu, Jian & Wang, Jing & Liao, Siyang & Sun, Yuanzhang & Ke, Deping & Li, Xiong & Liu, Ji & Jiang, Yibo & Wei, Congying & Tang, Bowen, 2018. "Stochastic multi-objective optimization of photovoltaics integrated three-phase distribution network based on dynamic scenarios," Applied Energy, Elsevier, vol. 231(C), pages 985-996.
    14. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Reindl, Thomas & Srinivasan, Dipti, 2022. "Levelised cost of PV integration for distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    15. Sayyed Ahmad Ali & Arif Hussain & Waseem Haider & Habib Ur Rehman & Syed Ali Abbas Kazmi, 2023. "Optimal Energy Management System of Isolated Multi-Microgrids with Local Energy Transactive Market with Indigenous PV-, Wind-, and Biomass-Based Resources," Energies, MDPI, vol. 16(4), pages 1-38, February.
    16. Anaya, K. & Pollitt, M., 2018. "Reactive Power Procurement: Lessons from Three Leading Countries," Cambridge Working Papers in Economics 1854, Faculty of Economics, University of Cambridge.
    17. Junyong Wu & Chen Shi & Meiyang Shao & Ran An & Xiaowen Zhu & Xing Huang & Rong Cai, 2019. "Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network," Energies, MDPI, vol. 12(17), pages 1-24, August.
    18. Ly Huu Pham & Minh Quan Duong & Van-Duc Phan & Thang Trung Nguyen & Hoang-Nam Nguyen, 2019. "A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem," Energies, MDPI, vol. 12(9), pages 1-25, May.
    19. Yingpei Liu & Yan Li & Haiping Liang & Jia He & Hanyang Cui, 2019. "Energy Routing Control Strategy for Integrated Microgrids Including Photovoltaic, Battery-Energy Storage and Electric Vehicles," Energies, MDPI, vol. 12(2), pages 1-16, January.
    20. Lei Zhang & Yingqi Liu & Beibei Pang & Bingxiang Sun & Ari Kokko, 2020. "Second Use Value of China’s New Energy Vehicle Battery: A View Based on Multi-Scenario Simulation," Sustainability, MDPI, vol. 12(1), pages 1-25, January.

    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:12:y:2019:i:21:p:4062-:d:280085. 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.