IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5735-d1522237.html
   My bibliography  Save this article

A Robust-Based Home Energy Management Model for Optimal Participation of Prosumers in Competitive P2P Platforms

Author

Listed:
  • Alaa Al Zetawi

    (Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

  • Marcos Tostado-Véliz

    (Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

  • Hany M. Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
    Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Francisco Jurado

    (Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

Abstract

Nowadays, advanced metering and communication infrastructures make it possible to enable decentralized control and market schemes. In this context, prosumers can interact with their neighbors in an active manner, thus sharing resources. This practice, known as peer-to-peer (P2P), can be put into practice under cooperative or competitive premises. This paper focuses on the second case, where the peers partaking in the P2P platform compete among themselves to improve their monetary balances. In such contexts, the domestic assets, such as on-site generators and storage systems, should be optimally scheduled to maximize participation in the P2P platform and thus enable the possibility of obtaining monetary incomes or exploiting surplus renewable energy from adjacent prosumers. This paper addresses this issue by developing a home energy management model for optimal participation of prosumers in competitive P2P platforms. The new proposal is cast in a three-stage procedure, in which the first and last stages are focused on domestic asset scheduling, while the second step decides the optimal offering/bidding strategy for the concerned prosumer. Moreover, uncertainties are introduced using interval notation and equivalent scenarios, resulting in an amicable computational framework that can be efficiently solved by average machines and off-the-shelf solvers. The new methodology is tested on a benchmark four-prosumer community. Results prove that the proposed procedure effectively maximizes the participation of prosumers in the P2P platform, thus increasing their monetary benefits. The role of storage systems is also discussed, in particular their capability of increasing exportable energy. Finally, the influence of uncertainties on the final results is illustrated.

Suggested Citation

  • Alaa Al Zetawi & Marcos Tostado-Véliz & Hany M. Hasanien & Francisco Jurado, 2024. "A Robust-Based Home Energy Management Model for Optimal Participation of Prosumers in Competitive P2P Platforms," Energies, MDPI, vol. 17(22), pages 1-22, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5735-:d:1522237
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5735/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/22/5735/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dinh, Huy Truong & Lee, Kyu-haeng & Kim, Daehee, 2022. "Supervised-learning-based hour-ahead demand response for a behavior-based home energy management system approximating MILP optimization," Applied Energy, Elsevier, vol. 321(C).
    2. Khorasany, Mohsen & Razzaghi, Reza & Shokri Gazafroudi, Amin, 2021. "Two-stage mechanism design for energy trading of strategic agents in energy communities," Applied Energy, Elsevier, vol. 295(C).
    3. Asjad Ali & Abdullah Aftab & Muhammad Nadeem Akram & Shoaib Awan & Hafiz Abdul Muqeet & Zeeshan Ahmad Arfeen, 2024. "Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response," Sustainability, MDPI, vol. 16(5), pages 1-27, March.
    4. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Amir Mansouri, Seyed & Jurado, Francisco, 2022. "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model," Applied Energy, Elsevier, vol. 328(C).
    5. Mehrjerdi, Hasan, 2020. "Peer-to-peer home energy management incorporating hydrogen storage system and solar generating units," Renewable Energy, Elsevier, vol. 156(C), pages 183-192.
    6. Rücker, Fabian & Schoeneberger, Ilka & Wilmschen, Till & Sperling, Dustin & Haberschusz, David & Figgener, Jan & Sauer, Dirk Uwe, 2022. "Self-sufficiency and charger constraints of prosumer households with vehicle-to-home strategies," Applied Energy, Elsevier, vol. 317(C).
    7. Long, Chao & Wu, Jianzhong & Zhou, Yue & Jenkins, Nick, 2018. "Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid," Applied Energy, Elsevier, vol. 226(C), pages 261-276.
    8. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Jurado, Francisco, 2022. "A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems," Energy, Elsevier, vol. 253(C).
    9. Thomas Kleinert & Martine Labbé & Fr¨ank Plein & Martin Schmidt, 2020. "Technical Note—There’s No Free Lunch: On the Hardness of Choosing a Correct Big-M in Bilevel Optimization," Operations Research, INFORMS, vol. 68(6), pages 1716-1721, November.
    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. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(C).
    2. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    3. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    4. Wanessa Guedes & Lucas Deotti & Bruno Dias & Tiago Soares & Leonardo Willer de Oliveira, 2022. "Community Energy Markets with Battery Energy Storage Systems: A General Modeling with Applications," Energies, MDPI, vol. 15(20), pages 1-22, October.
    5. Tao, Miaomiao, 2024. "Dynamics between electric vehicle uptake and green development: Understanding the role of local government competition," Transport Policy, Elsevier, vol. 146(C), pages 227-240.
    6. Park, Sung-Won & Zhang, Zhong & Li, Furong & Son, Sung-Yong, 2021. "Peer-to-peer trading-based efficient flexibility securing mechanism to support distribution system stability," Applied Energy, Elsevier, vol. 285(C).
    7. Kirchhoff, Hannes & Strunz, Kai, 2019. "Key drivers for successful development of peer-to-peer microgrids for swarm electrification," Applied Energy, Elsevier, vol. 244(C), pages 46-62.
    8. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    9. Ning Wang & Weisheng Xu & Weihui Shao & Zhiyu Xu, 2019. "A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids," Energies, MDPI, vol. 12(15), pages 1-26, July.
    10. Wang, Zibo & Yu, Xiaodan & Mu, Yunfei & Jia, Hongjie, 2020. "A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System," Applied Energy, Elsevier, vol. 260(C).
    11. Tsao, Yu-Chung & Thanh, Vo-Van, 2021. "Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    12. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    13. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    14. Fränk Plein & Johannes Thürauf & Martine Labbé & Martin Schmidt, 2022. "A bilevel optimization approach to decide the feasibility of bookings in the European gas market," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(3), pages 409-449, June.
    15. Liu, Jia & Yang, Hongxing & Zhou, Yuekuan, 2021. "Peer-to-peer trading optimizations on net-zero energy communities with energy storage of hydrogen and battery vehicles," Applied Energy, Elsevier, vol. 302(C).
    16. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Fernández-Lobato, Lázuli & Jurado, Francisco, 2023. "Robust energy management in isolated microgrids with hydrogen storage and demand response," Applied Energy, Elsevier, vol. 345(C).
    17. Xie, Xuehua & Qian, Tong & Li, Weiwei & Tang, Wenhu & Xu, Zhao, 2024. "An individualized adaptive distributed approach for fast energy-carbon coordination in transactive multi-community integrated energy systems considering power transformer loading capacity," Applied Energy, Elsevier, vol. 375(C).
    18. Fioriti, Davide & Frangioni, Antonio & Poli, Davide, 2021. "Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users," Applied Energy, Elsevier, vol. 299(C).
    19. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    20. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Hong, Juwon & Jeoung, Jaewon & Hong, Taehoon, 2023. "Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(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:gam:jeners:v:17:y:2024:i:22:p:5735-:d:1522237. 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.