IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v259y2020ics030626191931829x.html
   My bibliography  Save this article

Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization

Author

Listed:
  • Wang, Han
  • Riaz, Shariq
  • Mancarella, Pierluigi

Abstract

The concept of Virtual Power Plant (VPP) is recognized as an effective option to aggregate and operate Distributed Energy Resources (DER) to participate in wholesale energy markets and provide flexibility and associated grid services that are needed in a renewable-rich energy system. Also, as most of the DER are available in urban areas, there are increasing interests in assessing the potential to develop urban VPP, for example in university campuses. However, exploiting the flexibility of VPP and developing robust business cases require advanced considerations on their technical and commercial constraints and trade-offs in deploying the VPP’s flexibility when simultaneously participating in multiple markets. In this context, this paper presents a comprehensive, integrated techno-economic modeling approach that assesses the technical and commercial flexibility opportunities and develops a relevant business case framework based on co-optimized participation in multiple markets for an urban VPP. A real-world case study based on the University of Melbourne’s new campus under development is used to demonstrate the proposed approach, including the VPP’s participation in the energy, frequency control ancillary services, demand response, and hedging contract markets. The technical analysis shows that diversity of DER portfolio results in improved participation of VPP in various markets. From an economic perspective, a multi-market co-optimization model such as the one proposed here, fully exploiting the DER’s aggregated flexibility, results in attractive business cases for operating DER in urban areas as a VPP. The proposed approach and examples provided may be seen as a blueprint for more VPP applications and unlocking the great flexibility available in urban areas.

Suggested Citation

  • Wang, Han & Riaz, Shariq & Mancarella, Pierluigi, 2020. "Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization," Applied Energy, Elsevier, vol. 259(C).
  • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s030626191931829x
    DOI: 10.1016/j.apenergy.2019.114142
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191931829X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.114142?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
    2. Martínez Ceseña, Eduardo A. & Good, Nicholas & Mancarella, Pierluigi, 2015. "Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users," Energy Policy, Elsevier, vol. 82(C), pages 222-232.
    3. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    4. 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.
    5. Moreno, Rodrigo & Moreira, Roberto & Strbac, Goran, 2015. "A MILP model for optimising multi-service portfolios of distributed energy storage," Applied Energy, Elsevier, vol. 137(C), pages 554-566.
    6. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    7. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    8. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2018. "Multi market bidding strategies for demand side flexibility aggregators in electricity markets," Energy, Elsevier, vol. 149(C), pages 120-134.
    Full references (including those not matched with items on IDEAS)

    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. Dodiek Ika Candra & Kilian Hartmann & Michael Nelles, 2018. "Economic Optimal Implementation of Virtual Power Plants in the German Power Market," Energies, MDPI, vol. 11(9), pages 1-24, September.
    2. Good, Nicholas & Martínez Ceseña, Eduardo A. & Heltorp, Christopher & Mancarella, Pierluigi, 2019. "A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems," Energy, Elsevier, vol. 184(C), pages 165-179.
    3. 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.
    4. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    5. Nitsch, Felix & Deissenroth-Uhrig, Marc & Schimeczek, Christoph & Bertsch, Valentin, 2021. "Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets," Applied Energy, Elsevier, vol. 298(C).
    6. Héctor Marañón-Ledesma & Asgeir Tomasgard, 2019. "Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market," Energies, MDPI, vol. 12(15), pages 1-24, August.
    7. Matthew Gough & Sérgio F. Santos & Mohammed Javadi & Rui Castro & João P. S. Catalão, 2020. "Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis," Energies, MDPI, vol. 13(11), pages 1-32, May.
    8. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    9. Lopez, A. & Ogayar, B. & Hernández, J.C. & Sutil, F.S., 2020. "Survey and assessment of technical and economic features for the provision of frequency control services by household-prosumers," Energy Policy, Elsevier, vol. 146(C).
    10. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    11. Kim, Seokwoo & Choi, Dong Gu, 2024. "A sample robust optimal bidding model for a virtual power plant," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1101-1113.
    12. Iria, José & Scott, Paul & Attarha, Ahmad, 2020. "Network-constrained bidding optimization strategy for aggregators of prosumers," Energy, Elsevier, vol. 207(C).
    13. Ayman Esmat & Julio Usaola & María Ángeles Moreno, 2018. "Distribution-Level Flexibility Market for Congestion Management," Energies, MDPI, vol. 11(5), pages 1-24, April.
    14. Bohlayer, Markus & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2020. "Energy-intense production-inventory planning with participation in sequential energy markets," Applied Energy, Elsevier, vol. 258(C).
    15. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    16. Hu, Mian & Wang, Yan-Wu & Xiao, Jiang-Wen & Lin, Xiangning, 2019. "Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters," Energy, Elsevier, vol. 185(C), pages 910-921.
    17. Iria, José & Scott, Paul & Attarha, Ahmad & Gordon, Dan & Franklin, Evan, 2022. "MV-LV network-secure bidding optimisation of an aggregator of prosumers in real-time energy and reserve markets," Energy, Elsevier, vol. 242(C).
    18. Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2023. "Stochastic optimization of trading strategies in sequential electricity markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 400-421.
    19. Good, Nicholas & Ellis, Keith A. & Mancarella, Pierluigi, 2017. "Review and classification of barriers and enablers of demand response in the smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 57-72.
    20. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.

    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:eee:appene:v:259:y:2020:i:c:s030626191931829x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.