IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i6p806-d101424.html
   My bibliography  Save this article

Implementation of a Real-Time Microgrid Simulation Platform Based on Centralized and Distributed Management

Author

Listed:
  • Omid Abrishambaf

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, Porto 4200-072, Portugal)

  • Pedro Faria

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, Porto 4200-072, Portugal)

  • Luis Gomes

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, Porto 4200-072, Portugal)

  • João Spínola

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, Porto 4200-072, Portugal)

  • Zita Vale

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, Porto 4200-072, Portugal)

  • Juan M. Corchado

    (BISITE—Bioinformatics, Intelligent Systems and Educational Technology Research Center, University of Salamanca, Salamanca 37008, Spain)

Abstract

Demand response and distributed generation are key components of power systems. Several challenges are raised at both technical and business model levels for integration of those resources in smart grids and microgrids. The implementation of a distribution network as a test bed can be difficult and not cost-effective; using computational modeling is not sufficient for producing realistic results. Real-time simulation allows us to validate the business model’s impact at the technical level. This paper comprises a platform supporting the real-time simulation of a microgrid connected to a larger distribution network. The implemented platform allows us to use both centralized and distributed energy resource management. Using an optimization model for the energy resource operation, a virtual power player manages all the available resources. Then, the simulation platform allows us to technically validate the actual implementation of the requested demand reduction in the scope of demand response programs. The case study has 33 buses, 220 consumers, and 68 distributed generators. It demonstrates the impact of demand response events, also performing resource management in the presence of an energy shortage.

Suggested Citation

  • Omid Abrishambaf & Pedro Faria & Luis Gomes & João Spínola & Zita Vale & Juan M. Corchado, 2017. "Implementation of a Real-Time Microgrid Simulation Platform Based on Centralized and Distributed Management," Energies, MDPI, vol. 10(6), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:6:p:806-:d:101424
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/6/806/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/6/806/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmadali Khatibzadeh & Mohammadreza Besmi & Aminollah Mahabadi & Mahmoud Reza Haghifam, 2017. "Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages," Energies, MDPI, vol. 10(2), pages 1-17, February.
    2. Pedro Faria & Zita Vale & José Baptista, 2015. "Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    3. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    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. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Pedro Faria, 2019. "Distributed Energy Resources Management," Energies, MDPI, vol. 12(3), pages 1-3, February.
    3. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    4. Muhammad Babar & Jakub Grela & Andrzej Ożadowicz & Phuong H. Nguyen & Zbigniew Hanzelka & I. G. Kamphuis, 2018. "Energy Flexometer: Transactive Energy-Based Internet of Things Technology," Energies, MDPI, vol. 11(3), pages 1-20, March.
    5. Zita Vale & Pedro Faria & Omid Abrishambaf & Luis Gomes & Tiago Pinto, 2021. "MARTINE—A Platform for Real-Time Energy Management in Smart Grids," Energies, MDPI, vol. 14(7), pages 1-18, March.
    6. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.
    7. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    8. Andrzej Ożadowicz, 2017. "A New Concept of Active Demand Side Management for Energy Efficient Prosumer Microgrids with Smart Building Technologies," Energies, MDPI, vol. 10(11), pages 1-22, November.
    9. Omid Abrishambaf & Pedro Faria & Zita Vale & Juan M. Corchado, 2019. "Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case Study," Energies, MDPI, vol. 12(20), pages 1-21, October.
    10. Omid Abrishambaf & Pedro Faria & Zita Vale, 2020. "Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator," Energies, MDPI, vol. 13(6), pages 1-18, March.
    11. Ibrahim Ahmad & Ghaeth Fandi & Zdenek Muller & Josef Tlusty, 2019. "Voltage Quality and Power Factor Improvement in Smart Grids Using Controlled DG Units," Energies, MDPI, vol. 12(18), pages 1-18, September.
    12. Giuseppe Barone & Giovanni Brusco & Alessandro Burgio & Daniele Menniti & Anna Pinnarelli & Michele Motta & Nicola Sorrentino & Pasquale Vizza, 2018. "A Real-Life Application of a Smart User Network," Energies, MDPI, vol. 11(12), pages 1-23, 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. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    2. Dong, Jun & Xue, Guiyuan & Li, Rong, 2016. "Demand response in China: Regulations, pilot projects and recommendations – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 13-27.
    3. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    4. Sun, Zeyi & Li, Lin & Bego, Andres & Dababneh, Fadwa, 2015. "Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system," International Journal of Production Economics, Elsevier, vol. 165(C), pages 112-119.
    5. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    6. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    7. Juliano Camargo & Fred Spiessens & Chris Hermans, 2018. "A Network Flow Model for Price-Responsive Control of Deferrable Load Profiles," Energies, MDPI, vol. 11(3), pages 1-17, March.
    8. Pedro Faria & Zita Vale & José Baptista, 2015. "Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    9. Qingwu Gong & Jiazhi Lei & Jun Ye, 2016. "Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk," Energies, MDPI, vol. 9(1), pages 1-18, January.
    10. Adriano A. Santos & Filipe Pereira & António Ferreira da Silva & Nídia Caetano & Carlos Felgueiras & José Machado, 2023. "Electrification of a Remote Rural Farm with Solar Energy—Contribution to the Development of Smart Farming," Energies, MDPI, vol. 16(23), pages 1-17, November.
    11. Zhang, Yunchao & Islam, Md Monirul & Sun, Zeyi & Yang, Sijia & Dagli, Cihan & Xiong, Haoyi, 2018. "Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program," International Journal of Production Economics, Elsevier, vol. 206(C), pages 261-267.
    12. Baratsas, Stefanos G. & Niziolek, Alexander M. & Onel, Onur & Matthews, Logan R. & Floudas, Christodoulos A. & Hallermann, Detlef R. & Sorescu, Sorin M. & Pistikopoulos, Efstratios N., 2022. "A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies," Applied Energy, Elsevier, vol. 305(C).
    13. Ferrari, Lorenzo & Esposito, Fabio & Becciani, Michele & Ferrara, Giovanni & Magnani, Sandro & Andreini, Mirko & Bellissima, Alessandro & Cantù, Matteo & Petretto, Giacomo & Pentolini, Massimo, 2017. "Development of an optimization algorithm for the energy management of an industrial Smart User," Applied Energy, Elsevier, vol. 208(C), pages 1468-1486.
    14. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    15. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    16. Tang, Rui & Wang, Shengwei & Shan, Kui & Cheung, Howard, 2018. "Optimal control strategy of central air-conditioning systems of buildings at morning start period for enhanced energy efficiency and peak demand limiting," Energy, Elsevier, vol. 151(C), pages 771-781.
    17. Lei Zhou & Yang Li & Beibei Wang & Zhe Wang & Xiaoqing Hu, 2015. "Provision of Supplementary Load Frequency Control via Aggregation of Air Conditioning Loads," Energies, MDPI, vol. 8(12), pages 1-20, December.
    18. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    19. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    20. Fotouhi Ghazvini, Mohammad Ali & Canizes, Bruno & Vale, Zita & Morais, Hugo, 2013. "Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market," Applied Energy, Elsevier, vol. 101(C), pages 667-677.

    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:10:y:2017:i:6:p:806-:d:101424. 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.