IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13640-d1238360.html
   My bibliography  Save this article

Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model

Author

Listed:
  • Mahfuzur Rahman

    (Department of Information and Computer Science, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Solaiman Chowdhury

    (Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh)

  • Mohammad Shorfuzzaman

    (Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Mohammad Kamal Hossain

    (Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Mohammad Hammoudeh

    (Department of Information and Computer Science, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

Abstract

The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading.

Suggested Citation

  • Mahfuzur Rahman & Solaiman Chowdhury & Mohammad Shorfuzzaman & Mohammad Kamal Hossain & Mohammad Hammoudeh, 2023. "Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13640-:d:1238360
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13640/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/18/13640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    2. Reddy, K.S. & Ananthsornaraj, C., 2020. "Design, development and performance investigation of solar Parabolic Trough Collector for large-scale solar power plants," Renewable Energy, Elsevier, vol. 146(C), pages 1943-1957.
    3. Jiang, Yanni & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "Electricity trading pricing among prosumers with game theory-based model in energy blockchain environment," Applied Energy, Elsevier, vol. 271(C).
    4. 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).
    5. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    6. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    7. Filipe Bandeiras & Álvaro Gomes & Mário Gomes & Paulo Coelho, 2023. "Exploring Energy Trading Markets in Smart Grid and Microgrid Systems and Their Implications for Sustainability in Smart Cities," Energies, MDPI, vol. 16(2), pages 1-41, January.
    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. Zeng, Yu & Wei, Xuan & Yao, Yuan & Xu, Yinliang & Sun, Hongbin & Kin Victor Chan, Wai & Feng, Wei, 2023. "Determining the pricing and deployment strategy for virtual power plants of peer-to-peer prosumers: A game-theoretic approach," Applied Energy, Elsevier, vol. 345(C).
    2. Dong, Jingya & Song, Chunhe & Liu, Shuo & Yin, Huanhuan & Zheng, Hao & Li, Yuanjian, 2022. "Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach," Applied Energy, Elsevier, vol. 325(C).
    3. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K. & Yuen, Chau & Smith, David, 2022. "Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    4. Liu, Jicheng & Sun, Jiakang & Yuan, Hanying & Su, Yihan & Feng, Shuxian & Lu, Chaoran, 2022. "Behavior analysis of photovoltaic-storage-use value chain game evolution in blockchain environment," Energy, Elsevier, vol. 260(C).
    5. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
    6. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).
    8. Maarten Evens & Patricia Ercoli & Alessia Arteconi, 2023. "Blockchain-Enabled Microgrids: Toward Peer-to-Peer Energy Trading and Flexible Demand Management," Energies, MDPI, vol. 16(18), pages 1-24, September.
    9. 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).
    10. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    11. Yiheng Sun & Tian Lu & Cong Wang & Yuan Li & Huaiyu Fu & Jingran Dong & Yunjie Xu, 2021. "TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion," Papers 2112.02365, arXiv.org, revised Dec 2021.
    12. Fernández-Blanco, Ricardo & Morales, Juan Miguel & Pineda, Salvador, 2021. "Forecasting the price-response of a pool of buildings via homothetic inverse optimization," Applied Energy, Elsevier, vol. 290(C).
    13. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    14. Zheyuan Sun & Sara Tavakoli & Kaveh Khalilpour & Alexey Voinov & Jonathan Paul Marshall, 2024. "Barriers to Peer-to-Peer Energy Trading Networks: A Multi-Dimensional PESTLE Analysis," Sustainability, MDPI, vol. 16(4), pages 1-23, February.
    15. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    16. Opoku, Richard & Obeng, George Y. & Adjei, Eunice A. & Davis, Francis & Akuffo, Fred O., 2020. "Integrated system efficiency in reducing redundancy and promoting residential renewable energy in countries without net-metering: A case study of a SHS in Ghana," Renewable Energy, Elsevier, vol. 155(C), pages 65-78.
    17. Cortade, Thomas & Poudou, Jean-Christophe, 2022. "Peer-to-peer energy platforms: Incentives for prosuming," Energy Economics, Elsevier, vol. 109(C).
    18. Shan, Shan & Ahmad, Munir & Tan, Zhixiong & Adebayo, Tomiwa Sunday & Man Li, Rita Yi & Kirikkaleli, Dervis, 2021. "The role of energy prices and non-linear fiscal decentralization in limiting carbon emissions: Tracking environmental sustainability," Energy, Elsevier, vol. 234(C).
    19. Esmat, Ayman & de Vos, Martijn & Ghiassi-Farrokhfal, Yashar & Palensky, Peter & Epema, Dick, 2021. "A novel decentralized platform for peer-to-peer energy trading market with blockchain technology," Applied Energy, Elsevier, vol. 282(PA).
    20. 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).

    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:jsusta:v:15:y:2023:i:18:p:13640-:d:1238360. 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.