IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5501-d1198418.html
   My bibliography  Save this article

A Peer-to-Peer Energy Trading Model for Optimizing Both Efficiency and Fairness

Author

Listed:
  • Eiichi Kusatake

    (Graduate School of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji-shi 192-8577, Japan
    These authors contributed equally to this work.)

  • Mitsue Imahori

    (Graduate School of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji-shi 192-8577, Japan
    These authors contributed equally to this work.)

  • Norihiko Shinomiya

    (Graduate School of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji-shi 192-8577, Japan)

Abstract

In recent years, there has been a growing global trend towards transitioning from centralized energy systems to distributed or decentralized models, with the aim of promoting the widespread utilization of renewable energy sources. As a result, the concept of direct energy trading among consumers has garnered considerable attention as a means to effectively harness the potential of distributed energy systems. However, in this decentralized trading scenario, certain consumers may encounter challenges in receiving electricity from their preferred suppliers due to limited supply capacities. As a result of this constraint, there is a reduction in the advantages enjoyed by consumers. While previous studies have predominantly focused on optimizing resource allocation efficiency, the issue of equitable consumer benefits has often been overlooked. Therefore, it is crucial to develop a trading mechanism that considers the preferences of market participants, in addition to balancing supply and demand. Such a mechanism aims to enhance both fairness and efficiency in the market. This paper introduces the formulation of a single-objective optimization and multi-objective optimization problem for an electricity market trading mechanism. To address this challenge, two single-objective algorithms and six evolutionary algorithms (EAs) are employed to solve the optimization problem. By analyzing the simulation results, this study demonstrates the efficacy of the chosen evolutionary algorithms (EAs) and a single-objective optimization approach in effectively optimizing both the utilization of resources and the equitable distribution of consumer benefits.

Suggested Citation

  • Eiichi Kusatake & Mitsue Imahori & Norihiko Shinomiya, 2023. "A Peer-to-Peer Energy Trading Model for Optimizing Both Efficiency and Fairness," Energies, MDPI, vol. 16(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5501-:d:1198418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5501/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/14/5501/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Argyris, Nikolaos & Karsu, Özlem & Yavuz, Mirel, 2022. "Fair resource allocation: Using welfare-based dominance constraints," European Journal of Operational Research, Elsevier, vol. 297(2), pages 560-578.
    2. Lampropoulos, Ioannis & van den Broek, Machteld & van der Hoofd, Erik & Hommes, Klaas & van Sark, Wilfried, 2018. "A system perspective to the deployment of flexibility through aggregator companies in the Netherlands," Energy Policy, Elsevier, vol. 118(C), pages 534-551.
    3. Jing, Rui & Xie, Mei Na & Wang, Feng Xiang & Chen, Long Xiang, 2020. "Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management," Applied Energy, Elsevier, vol. 262(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. Zhao, Bingxu & Cao, Xiaodong & Duan, Pengfei, 2024. "Cooperative operation of multiple low-carbon microgrids: An optimization study addressing gaming fraud and multiple uncertainties," Energy, Elsevier, vol. 297(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. 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.
    2. Wang, Juan & Zheng, Junjun & Yu, Liukai & Goh, Mark & Tang, Yunying & Huang, Yongchao, 2023. "Distributed Reputation-Distance iterative auction system for Peer-To-Peer power trading," Applied Energy, Elsevier, vol. 345(C).
    3. Jianhong Hao & Ting Huang & Yi Sun & Xiangpeng Zhan & Yu Zhang & Peng Wu, 2024. "Optimal Prosumer Operation with Consideration for Bounded Rationality in Peer-to-Peer Energy Trading Systems," Energies, MDPI, vol. 17(7), pages 1-22, April.
    4. Zare, Amir & Mehdinejad, Mehdi & Abedi, Mehrdad, 2024. "Designing a decentralized peer-to-peer energy market for an active distribution network considering loss and transaction fee allocation, and fairness," Applied Energy, Elsevier, vol. 358(C).
    5. Gudmundsson, Jens & Hougaard, Jens Leth & Platz, Trine Tornøe, 2023. "Decentralized task coordination," European Journal of Operational Research, Elsevier, vol. 304(2), pages 851-864.
    6. Xie, Shiwei & Zheng, Jieyun & Hu, Zhijian & Wang, Jueying & Chen, Yuwei, 2020. "Urban multi-energy network optimization: An enhanced model using a two-stage bound-tightening approach," Applied Energy, Elsevier, vol. 277(C).
    7. Zhaonian Ye & Yongzhen Wang & Kai Han & Changlu Zhao & Juntao Han & Yilin Zhu, 2023. "Bi-Objective Optimization and Emergy Analysis of Multi-Distributed Energy System Considering Shared Energy Storage," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    8. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    9. García-Muñoz, Fernando & Dávila, Sebastián & Quezada, Franco, 2023. "A Benders decomposition approach for solving a two-stage local energy market problem under uncertainty," Applied Energy, Elsevier, vol. 329(C).
    10. Soto, Esteban A. & Bosman, Lisa B. & Wollega, Ebisa & Leon-Salas, Walter D., 2021. "Peer-to-peer energy trading: A review of the literature," Applied Energy, Elsevier, vol. 283(C).
    11. Li, Kai & Ma, Minda & Xiang, Xiwang & Feng, Wei & Ma, Zhili & Cai, Weiguang & Ma, Xin, 2022. "Carbon reduction in commercial building operations: A provincial retrospection in China," Applied Energy, Elsevier, vol. 306(PB).
    12. 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).
    13. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    14. Charis Vlados & Dimos Chatzinikolaou & Foteini Kapaltzoglou, 2021. "Energy Market Liberalisation in Greece: Structures, Policy and Prospects," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 115-126.
    15. Wang, Tonghe & Guo, Jian & Ai, Songpu & Cao, Junwei, 2021. "RBT: A distributed reputation system for blockchain-based peer-to-peer energy trading with fairness consideration," Applied Energy, Elsevier, vol. 295(C).
    16. Nayeem Rahman & Rodrigo Rabetino & Arto Rajala & Jukka Partanen, 2021. "Ushering in a New Dawn: Demand-Side Local Flexibility Platform Governance and Design in the Finnish Energy Markets," Energies, MDPI, vol. 14(15), pages 1-23, July.
    17. Niyam Haque & Anuradha Tomar & Phuong Nguyen & Guus Pemen, 2020. "Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks," Energies, MDPI, vol. 13(2), pages 1-16, January.
    18. Wu, Qiong & Xie, Zhun & Ren, Hongbo & Li, Qifen & Yang, Yongwen, 2022. "Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: A comparison study," Energy, Elsevier, vol. 254(PC).
    19. Qimiao Xie & Qidi Jiang & Jarek Kurnitski & Jiahang Yang & Zihao Lin & Shiqi Ye, 2024. "Quantitative Carbon Emission Prediction Model to Limit Embodied Carbon from Major Building Materials in Multi-Story Buildings," Sustainability, MDPI, vol. 16(13), pages 1-21, June.
    20. Bochun Zhan & Changsen Feng & Zhemin Lin & Xiaoyu Shao & Fushuan Wen, 2023. "Peer-to-Peer Energy Trading among Prosumers with Voltage Regulation Services Provision," Energies, MDPI, vol. 16(14), pages 1-22, July.

    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:16:y:2023:i:14:p:5501-:d:1198418. 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.