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Grid-Forming Virtual Power Plants: Concepts, Technologies and Advantages

Author

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  • Khalil Gholami

    (Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 67146, Iran)

  • Behnaz Behi

    (Discipline of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

  • Ali Arefi

    (Discipline of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

  • Philip Jennings

    (Discipline of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

Abstract

Virtual Power Plants (VPPs) are efficient structures for attracting private investment, increasing the penetration of renewable energy and reducing the cost of electricity for consumers. It is expected that the number of VPPs will increase rapidly as their financial return is attractive to investors. VPPs will provide added value to consumers, to power systems and to electricity markets by contributing to different services such as the energy and load-following services. One of the capabilities that will become critical in the near future, when large power plants are retired, is grid-forming capability. This review paper introduces the concept of grid-forming VPPs along with their corresponding technologies and their advantages for the new generation of power systems with many connected VPPs.

Suggested Citation

  • Khalil Gholami & Behnaz Behi & Ali Arefi & Philip Jennings, 2022. "Grid-Forming Virtual Power Plants: Concepts, Technologies and Advantages," Energies, MDPI, vol. 15(23), pages 1-26, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9049-:d:988206
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    References listed on IDEAS

    as
    1. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    2. Spartaco Albertarelli & Piero Fraternali & Sergio Herrera & Mark Melenhorst & Jasminko Novak & Chiara Pasini & Andrea-Emilio Rizzoli & Cristina Rottondi, 2018. "A Survey on the Design of Gamified Systems for Energy and Water Sustainability," Games, MDPI, vol. 9(3), pages 1-34, June.
    3. Loßner, Martin & Böttger, Diana & Bruckner, Thomas, 2017. "Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis," Energy Economics, Elsevier, vol. 62(C), pages 125-138.
    4. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Moghaddam, Saeed Zolfaghari & Akbari, Tohid, 2018. "Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach," Energy, Elsevier, vol. 151(C), pages 478-489.
    6. Hany Elgamal, Ahmed & Kocher-Oberlehner, Gudrun & Robu, Valentin & Andoni, Merlinda, 2019. "Optimization of a multiple-scale renewable energy-based virtual power plant in the UK," Applied Energy, Elsevier, vol. 256(C).
    7. SoltaniNejad Farsangi, Alireza & Hadayeghparast, Shahrzad & Mehdinejad, Mehdi & Shayanfar, Heidarali, 2018. "A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs," Energy, Elsevier, vol. 160(C), pages 257-274.
    8. Behnaz Behi & Ali Arefi & Philip Jennings & Arian Gorjy & Almantas Pivrikas, 2021. "Advanced Monitoring and Control System for Virtual Power Plants for Enabling Customer Engagement and Market Participation," Energies, MDPI, vol. 14(4), pages 1-19, February.
    9. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    10. Tomasz Sikorski & Michal Jasiński & Edyta Ropuszyńska-Surma & Magdalena Węglarz & Dominika Kaczorowska & Paweł Kostyla & Zbigniew Leonowicz & Robert Lis & Jacek Rezmer & Wilhelm Rojewski & Marian Sobi, 2020. "A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects," Energies, MDPI, vol. 13(12), pages 1-30, June.
    11. Efaf Bikdeli & Md. Rabiul Islam & Md. Moktadir Rahman & Kashem M. Muttaqi, 2022. "State of the Art of the Techniques for Grid Forming Inverters to Solve the Challenges of Renewable Rich Power Grids," Energies, MDPI, vol. 15(5), pages 1-25, March.
    12. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    13. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    14. Scarabelot, Letícia T. & Rambo, Carlos R. & Rampinelli, Giuliano A., 2018. "A relative power-based adaptive hybrid model for DC/AC average inverter efficiency of photovoltaics systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 470-477.
    Full references (including those not matched with items on IDEAS)

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