IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v207y2020ics0360544220313736.html
   My bibliography  Save this article

Network-constrained bidding optimization strategy for aggregators of prosumers

Author

Listed:
  • Iria, José
  • Scott, Paul
  • Attarha, Ahmad

Abstract

The large-scale deployment of smart home technologies will unlock the flexibility of prosumers, which in turn will be transformed into electricity market services by aggregators. This paper proposes a new network-constrained bidding optimization strategy to coordinate the participation of aggregators of prosumers in the day-ahead energy and secondary reserve markets. This bidding optimization strategy consists of a decentralized approach based on the alternating direction method of multipliers, where aggregators negotiate with the distribution system operator to obtain network-constrained energy and secondary reserve bids. For a case study of 2 aggregators and 1 distribution system operator, the results show that the network-constrained bidding strategy computes cost-effective and network-feasible energy and secondary reserve bids, as opposed to a network-free bidding strategy. In addition, the network-constrained bidding strategy preserves the independent roles of aggregators and the distribution system operator, and the data privacy of all agents.

Suggested Citation

  • Iria, José & Scott, Paul & Attarha, Ahmad, 2020. "Network-constrained bidding optimization strategy for aggregators of prosumers," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313736
    DOI: 10.1016/j.energy.2020.118266
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220313736
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118266?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. Luo, Lizi & Wu, Zhi & Gu, Wei & Huang, He & Gao, Song & Han, Jun, 2020. "Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction," Energy, Elsevier, vol. 192(C).
    3. Iria, José & Heleno, Miguel & Cardoso, Gonçalo, 2019. "Optimal sizing and placement of energy storage systems and on-load tap changer transformers in distribution networks," Applied Energy, Elsevier, vol. 250(C), pages 1147-1157.
    4. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    5. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    6. 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).
    7. GhaffarianHoseini, AmirHosein & Dahlan, Nur Dalilah & Berardi, Umberto & GhaffarianHoseini, Ali & Makaremi, Nastaran, 2013. "The essence of future smart houses: From embedding ICT to adapting to sustainability principles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 593-607.
    8. 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.
    9. 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.
    10. Iria, José & Soares, Filipe, 2019. "Real-time provision of multiple electricity market products by an aggregator of prosumers," Applied Energy, Elsevier, vol. 255(C).
    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. 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).
    2. Đorđe Lazović & Željko Đurišić, 2023. "Advanced Flexibility Support through DSO-Coordinated Participation of DER Aggregators in the Balancing Market," Energies, MDPI, vol. 16(8), pages 1-26, April.
    3. Domagoj Badanjak & Hrvoje Pandžić, 2021. "Distribution-Level Flexibility Markets—A Review of Trends, Research Projects, Key Stakeholders and Open Questions," Energies, MDPI, vol. 14(20), pages 1-26, October.
    4. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Sun, Guoqiang & Shen, Sichen & Chen, Sheng & Zhou, Yizhou & Wei, Zhinong, 2022. "Bidding strategy for a prosumer aggregator with stochastic renewable energy production in energy and reserve markets," Renewable Energy, Elsevier, vol. 191(C), pages 278-290.
    6. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Zhang, Jingrui & Zhou, Yulu & Li, Zhuoyun & Cai, Junfeng, 2021. "Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach," Energy, Elsevier, vol. 219(C).
    8. Nykyri, Mikko & Kärkkäinen, Tommi J. & Levikari, Saku & Honkapuro, Samuli & Annala, Salla & Silventoinen, Pertti, 2022. "Blockchain-based balance settlement ledger for energy communities in open electricity markets," Energy, Elsevier, vol. 253(C).
    9. 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).
    10. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.
    11. Coelho, António & Iria, José & Soares, Filipe, 2021. "Network-secure bidding optimization of aggregators of multi-energy systems in electricity, gas, and carbon markets," Applied Energy, Elsevier, vol. 301(C).
    12. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2022. "Integrated pricing strategy for coordinating load levels in coupled power and transportation networks," Applied Energy, Elsevier, vol. 307(C).
    13. Piao, Longjian & de Vries, Laurens & de Weerdt, Mathijs & Yorke-Smith, Neil, 2021. "Electricity markets for DC distribution systems: Locational pricing trumps wholesale pricing," Energy, Elsevier, vol. 214(C).

    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. Xiao, Xiangsheng & Wang, Jianxiao & Lin, Rui & Hill, David J. & Kang, Chongqing, 2020. "Large-scale aggregation of prosumers toward strategic bidding in joint energy and regulation markets," Applied Energy, Elsevier, vol. 271(C).
    2. Coelho, António & Iria, José & Soares, Filipe, 2021. "Network-secure bidding optimization of aggregators of multi-energy systems in electricity, gas, and carbon markets," Applied Energy, Elsevier, vol. 301(C).
    3. 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).
    4. 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).
    5. Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).
    6. 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).
    7. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    8. Subramanian, Vignesh & Das, Tapas K., 2019. "A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes," Energy, Elsevier, vol. 167(C), pages 1266-1277.
    9. Oprea, Simona-Vasilica & Bâra, Adela & Ciurea, Cristian-Eugen, 2022. "A novel cost-revenue allocation computation for the competitiveness of balancing responsible parties, including RES. Insights from the electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 881-894.
    10. 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.
    11. Stig Ødegaard Ottesen & Martin Haug & Heidi S. Nygård, 2020. "A Framework for Offering Short-Term Demand-Side Flexibility to a Flexibility Marketplace," Energies, MDPI, vol. 13(14), pages 1-17, July.
    12. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    13. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    14. 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).
    15. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
    16. Afzal S. Siddiqui & Sauleh A. Siddiqui, 2022. "Ambiguities and nonmonotonicities under prosumer power," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 492-532, October.
    17. Chen Zhao & Jiaqi Sun & Ping He & Shaohua Zhang & Yuqi Ji, 2023. "Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market," Energies, MDPI, vol. 16(8), pages 1-18, April.
    18. M. Ebrahim Adabi & Bogdan Marinescu, 2022. "Direct Participation of Dynamic Virtual Power Plants in Secondary Frequency Control," Energies, MDPI, vol. 15(8), pages 1-15, April.
    19. Backe, Stian & Kara, Güray & Tomasgard, Asgeir, 2020. "Comparing individual and coordinated demand response with dynamic and static power grid tariffs," Energy, Elsevier, vol. 201(C).
    20. 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).

    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:energy:v:207:y:2020:i:c:s0360544220313736. 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.journals.elsevier.com/energy .

    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.