IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1304-d148042.html
   My bibliography  Save this article

Experimental Validation of Peer-to-Peer Distributed Voltage Control System

Author

Listed:
  • Hamada Almasalma

    (Departement Elektrotechniek, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
    EnergyVille Research Center, Thor Park 8310, 3600 Genk, Belgium)

  • Sander Claeys

    (Departement Elektrotechniek, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
    EnergyVille Research Center, Thor Park 8310, 3600 Genk, Belgium)

  • Konstantin Mikhaylov

    (Centre for Wireless Communications (CWC), University of Oulu, 90014 Oulu, Finland)

  • Jussi Haapola

    (Centre for Wireless Communications (CWC), University of Oulu, 90014 Oulu, Finland)

  • Ari Pouttu

    (Centre for Wireless Communications (CWC), University of Oulu, 90014 Oulu, Finland)

  • Geert Deconinck

    (Departement Elektrotechniek, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
    EnergyVille Research Center, Thor Park 8310, 3600 Genk, Belgium)

Abstract

This paper presents experimental validation of a distributed optimization-based voltage control system. The dual-decomposition method is used in this paper to solve the voltage optimization problem in a fully distributed way. Device-to-device communication is implemented to enable peer-to-peer data exchange between agents of the proposed voltage control system. The paper presents the design, development and hardware setup of a laboratory-based testbed used to validate the performance of the proposed dual-decomposition-based peer-to-peer voltage control. The architecture of the setup consists of four layers: microgrid, control, communication, and monitoring. The key question motivating this research was whether distributed voltage control systems are a technically effective alternative to centralized ones. The results discussed in this paper show that distributed voltage control systems can indeed provide satisfactory regulation of the voltage profiles.

Suggested Citation

  • Hamada Almasalma & Sander Claeys & Konstantin Mikhaylov & Jussi Haapola & Ari Pouttu & Geert Deconinck, 2018. "Experimental Validation of Peer-to-Peer Distributed Voltage Control System," Energies, MDPI, vol. 11(5), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1304-:d:148042
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1304/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1304/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cagnano, A. & De Tuglie, E., 2016. "A decentralized voltage controller involving PV generators based on Lyapunov theory," Renewable Energy, Elsevier, vol. 86(C), pages 664-674.
    2. Marzband, Mousa & Azarinejadian, Fatemeh & Savaghebi, Mehdi & Pouresmaeil, Edris & Guerrero, Josep M. & Lightbody, Gordon, 2018. "Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations," Renewable Energy, Elsevier, vol. 126(C), pages 95-106.
    3. Nijhuis, M. & Gibescu, M. & Cobben, J.F.G., 2015. "Assessment of the impacts of the renewable energy and ICT driven energy transition on distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1003-1014.
    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. Dudjak, Viktorija & Neves, Diana & Alskaif, Tarek & Khadem, Shafi & Pena-Bello, Alejandro & Saggese, Pietro & Bowler, Benjamin & Andoni, Merlinda & Bertolini, Marina & Zhou, Yue & Lormeteau, Blanche &, 2021. "Impact of local energy markets integration in power systems layer: A comprehensive review," Applied Energy, Elsevier, vol. 301(C).
    2. Almasalma, Hamada & Claeys, Sander & Deconinck, Geert, 2019. "Peer-to-peer-based integrated grid voltage support function for smart photovoltaic inverters," Applied Energy, Elsevier, vol. 239(C), pages 1037-1048.
    3. Thomas I. Strasser & Sebastian Rohjans & Graeme M. Burt, 2019. "Methods and Concepts for Designing and Validating Smart Grid Systems," Energies, MDPI, vol. 12(10), pages 1-5, May.
    4. Jean-François Toubeau & Bashir Bakhshideh Zad & Martin Hupez & Zacharie De Grève & François Vallée, 2020. "Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks," Energies, MDPI, vol. 13(15), pages 1-15, August.
    5. Daiva Stanelytė & Virginijus Radziukynas, 2022. "Analysis of Voltage and Reactive Power Algorithms in Low Voltage Networks," Energies, MDPI, vol. 15(5), pages 1-26, March.
    6. Francisco de Paula García-López & Manuel Barragán-Villarejo & Alejandro Marano-Marcolini & José María Maza-Ortega & José Luis Martínez-Ramos, 2018. "Experimental Assessment of a Centralised Controller for High-RES Active Distribution Networks," Energies, MDPI, vol. 11(12), pages 1-16, December.
    7. Olamide Jogunola & Bamidele Adebisi & Kelvin Anoh & Augustine Ikpehai & Mohammad Hammoudeh & Georgina Harris & Haris Gacanin, 2018. "Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links," Energies, MDPI, vol. 11(9), pages 1-16, September.
    8. Bashir Bakhshideh Zad & Jean-François Toubeau & François Vallée, 2021. "Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems," Energies, MDPI, vol. 14(16), pages 1-16, August.

    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. Doumen, Sjoerd C. & Nguyen, Phuong & Kok, Koen, 2022. "Challenges for large-scale Local Electricity Market implementation reviewed from the stakeholder perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    2. Jaber Valinejad & Mousa Marzband & Michael Elsdon & Ameena Saad Al-Sumaiti & Taghi Barforoushi, 2019. "Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO 2 Emissions," Energies, MDPI, vol. 12(24), pages 1-35, December.
    3. Chaabane Bouali & Horst Schulte & Abdelkader Mami, 2019. "A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search," Energies, MDPI, vol. 12(12), pages 1-32, June.
    4. Fan, Cheng & Huang, Gongsheng & Sun, Yongjun, 2018. "A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level," Energy, Elsevier, vol. 164(C), pages 536-549.
    5. Emad M. Ahmed & Mokhtar Aly & Ahmed Elmelegi & Abdullah G. Alharbi & Ziad M. Ali, 2019. "Multifunctional Distributed MPPT Controller for 3P4W Grid-Connected PV Systems in Distribution Network with Unbalanced Loads," Energies, MDPI, vol. 12(24), pages 1-19, December.
    6. Alizadeh, Ali & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud, 2023. "Energy management in microgrids using transactive energy control concept under high penetration of Renewables; A survey and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    7. Muhammed Y. Worku & Mohamed A. Hassan & Mohamed A. Abido, 2019. "Real Time Energy Management and Control of Renewable Energy based Microgrid in Grid Connected and Island Modes," Energies, MDPI, vol. 12(2), pages 1-18, January.
    8. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    9. Mahmoud Saleh & Yusef Esa & Ahmed Mohamed, 2018. "Applications of Complex Network Analysis in Electric Power Systems," Energies, MDPI, vol. 11(6), pages 1-16, May.
    10. Jiang, Huaiguang & Zhang, Yingchen & Chen, Yuche & Zhao, Changhong & Tan, Jin, 2018. "Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing – A hierarchical data-driven approach," Applied Energy, Elsevier, vol. 229(C), pages 756-766.
    11. Pasichnyi, Oleksii & Wallin, Jörgen & Kordas, Olga, 2019. "Data-driven building archetypes for urban building energy modelling," Energy, Elsevier, vol. 181(C), pages 360-377.
    12. Wei-Neng Chang & Chia-Min Chang & Shao-Kang Yen, 2018. "Improvements in Bidirectional Power-Flow Balancing and Electric Power Quality of a Microgrid with Unbalanced Distributed Generators and Loads by Using Shunt Compensators," Energies, MDPI, vol. 11(12), pages 1-14, November.
    13. Naderi, Yahya & Hosseini, Seyed Hossein & Ghassem Zadeh, Saeid & Mohammadi-Ivatloo, Behnam & Vasquez, Juan C. & Guerrero, Josep M., 2018. "An overview of power quality enhancement techniques applied to distributed generation in electrical distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 201-214.
    14. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    15. Zixiao Xu & Dechang Yang & Weilin Li, 2019. "Microgrid Group Trading Model and Solving Algorithm Based on Blockchain," Energies, MDPI, vol. 12(7), pages 1-19, April.
    16. Antonio Bracale & Guido Carpinelli & Pasquale De Falco, 2019. "Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method," Energies, MDPI, vol. 12(6), pages 1-16, March.
    17. Razmjoo, A. & Gakenia Kaigutha, L. & Vaziri Rad, M.A. & Marzband, M. & Davarpanah, A. & Denai, M., 2021. "A Technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO2 emissions in a high potential area," Renewable Energy, Elsevier, vol. 164(C), pages 46-57.
    18. Yu Min Hwang & Issac Sim & Young Ghyu Sun & Heung-Jae Lee & Jin Young Kim, 2018. "Game-Theory Modeling for Social Welfare Maximization in Smart Grids," Energies, MDPI, vol. 11(9), pages 1-23, September.
    19. Arsalan Najafi & Mousa Marzband & Behnam Mohamadi-Ivatloo & Javier Contreras & Mahdi Pourakbari-Kasmaei & Matti Lehtonen & Radu Godina, 2019. "Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response," Energies, MDPI, vol. 12(8), pages 1-20, April.
    20. Wang, Jen Chun, 2022. "Understanding the energy consumption of information and communications equipment: A case study of schools in Taiwan," Energy, Elsevier, vol. 249(C).

    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:11:y:2018:i:5:p:1304-:d:148042. 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.