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Real-time provision of multiple electricity market products by an aggregator of prosumers

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  • Iria, José
  • Soares, Filipe

Abstract

The foreseen participation of aggregators of prosumers in the electricity markets will require the development of computational tools to support them in the definition and delivery of market products. This paper proposes a new hierarchical model predictive control (MPC) to support an aggregator in the delivery of multiple market products through the real-time control of heterogeneous flexible resources. The hierarchical MPC covers the participation of an aggregator in both energy and secondary reserve markets. The results show that the aggregator is capable of delivering several combinations of energy and secondary reserve without compromising the comfort and preferences of its clients.

Suggested Citation

  • Iria, José & Soares, Filipe, 2019. "Real-time provision of multiple electricity market products by an aggregator of prosumers," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919314795
    DOI: 10.1016/j.apenergy.2019.113792
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    Cited by:

    1. Agostini, Marco & Bertolini, Marina & Coppo, Massimiliano & Fontini, Fulvio, 2021. "The participation of small-scale variable distributed renewable energy sources to the balancing services market," Energy Economics, Elsevier, vol. 97(C).
    2. 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).
    3. Ieva Pakere & Armands Gravelsins & Girts Bohvalovs & Liga Rozentale & Dagnija Blumberga, 2021. "Will Aggregator Reduce Renewable Power Surpluses? A System Dynamics Approach for the Latvia Case Study," Energies, MDPI, vol. 14(23), pages 1-21, November.
    4. 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).
    5. 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.
    6. Iria, José & Scott, Paul & Attarha, Ahmad, 2020. "Network-constrained bidding optimization strategy for aggregators of prosumers," Energy, Elsevier, vol. 207(C).
    7. 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.
    8. Seong-Hyeon Cha & Sun-Hyeok Kwak & Woong Ko, 2023. "A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market," Energies, MDPI, vol. 16(20), pages 1-27, October.
    9. Adrian Tantau & András Puskás-Tompos & Laurentiu Fratila & Costel Stanciu, 2021. "Acceptance of Demand Response and Aggregators as a Solution to Optimize the Relation between Energy Producers and Consumers in order to Increase the Amount of Renewable Energy in the Grid," Energies, MDPI, vol. 14(12), pages 1-19, June.
    10. Pratik Mochi & Kartik Pandya & Ricardo Faia & Joao Soares, 2023. "Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    11. 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).
    12. 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).
    13. Chi-Keung Woo & Jay Zarnikau & Asher Tishler & Kang Hua Cao, 2022. "Insuring a Small Retail Electric Provider’s Procurement Cost Risk in Texas," Energies, MDPI, vol. 16(1), pages 1-12, December.
    14. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    15. 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).
    16. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Singh, Kamini & Gadh, Rajit & Singh, Anoop & Lal Dewangan, Chaman, 2022. "Design of an optimal P2P energy trading market model using bilevel stochastic optimization," Applied Energy, Elsevier, vol. 328(C).
    18. 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.
    19. 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.
    20. Esfahani, Moein & Alizadeh, Ali & Amjady, Nima & Kamwa, Innocent, 2024. "A distributed VPP-integrated co-optimization framework for energy scheduling, frequency regulation, and voltage support using data-driven distributionally robust optimization with Wasserstein metric," Applied Energy, Elsevier, vol. 361(C).

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