IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1549-d1057545.html
   My bibliography  Save this article

Developing an Appropriate Energy Trading Algorithm and Techno-Economic Analysis between Peer-to-Peer within a Partly Independent Microgrid

Author

Listed:
  • Fahim Muntasir

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John’s, NL A1C 5S7, Canada)

  • Anusheel Chapagain

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John’s, NL A1C 5S7, Canada)

  • Kishan Maharjan

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John’s, NL A1C 5S7, Canada)

  • Mirza Jabbar Aziz Baig

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John’s, NL A1C 5S7, Canada)

  • Mohsin Jamil

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John’s, NL A1C 5S7, Canada)

  • Ashraf Ali Khan

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John’s, NL A1C 5S7, Canada)

Abstract

The intimidating surge in the procurement of Distributed Energy Resources (DER) has increased the number of prosumers, creating a new possibility of local energy trading across the community. This project aims to formulate the peer-to-peer energy (P2P) sharing model to encourage the DERs to share surplus energy among the consumers. An effective pricing method is developed based on the supply-demand ratio (SDR) with the importance of self-optimization, which allows the prosumers to maximize their energy sharing and profits. To implement this pricing method, a simplified dynamic matchmaking algorithm has been deployed to introduce the Outstanding Prosumer to interact with existing consumers to increase the efficiency and profitability of the trade network. Consumers also benefit from this model, as they can pick the most economical energy supplier instead of relying on the utility grid. The prosumer with high excess energy and the consumer with the highest energy demand will be prioritized to maintain the SDR ratio to one or greater than one. Here, all the above-stated features of the peer-to-peer energy trading have been demonstrated with some calculations to back up some tangible results. Finally, a case study is simulated among the residents of Dhaka, Bangladesh, to demonstrate how peers can profit from participating in trading at a given time. Comparing the results with and without P2P trading, there has been a 17.54% reduction in an electric bill on a typical day of July, and a 49.53% reduction in the interaction with the grid.

Suggested Citation

  • Fahim Muntasir & Anusheel Chapagain & Kishan Maharjan & Mirza Jabbar Aziz Baig & Mohsin Jamil & Ashraf Ali Khan, 2023. "Developing an Appropriate Energy Trading Algorithm and Techno-Economic Analysis between Peer-to-Peer within a Partly Independent Microgrid," Energies, MDPI, vol. 16(3), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1549-:d:1057545
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1549/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1549/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alam, Muhammad Raisul & St-Hilaire, Marc & Kunz, Thomas, 2019. "Peer-to-peer energy trading among smart homes," Applied Energy, Elsevier, vol. 238(C), pages 1434-1443.
    2. Muhammad Babar Rasheed & Nadeem Javaid & Ashfaq Ahmad & Mohsin Jamil & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh, 2016. "Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing," Energies, MDPI, vol. 9(8), pages 1-25, July.
    3. Mirza Jabbar Aziz Baig & M. Tariq Iqbal & Mohsin Jamil & Jahangir Khan, 2022. "A Low-Cost, Open-Source Peer-to-Peer Energy Trading System for a Remote Community Using the Internet-of-Things, Blockchain, and Hypertext Transfer Protocol," Energies, MDPI, vol. 15(13), pages 1-22, July.
    4. Spiliopoulos, Nikolas & Sarantakos, Ilias & Nikkhah, Saman & Gkizas, George & Giaouris, Damian & Taylor, Phil & Rajarathnam, Uma & Wade, Neal, 2022. "Peer-to-peer energy trading for improving economic and resilient operation of microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 517-535.
    5. Yujian Ye & Dawei Qiu & Huiyu Wang & Yi Tang & Goran Strbac, 2021. "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning," Energies, MDPI, vol. 14(3), pages 1-22, January.
    6. Camilo, Henrique Fernandes & Udaeta, Miguel Edgar Morales & Veiga Gimenes, André Luiz & Grimoni, Jose Aquiles Baesso, 2017. "Assessment of photovoltaic distributed generation – Issues of grid connected systems through the consumer side applied to a case study of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 712-719.
    7. Juhar Abdella & Khaled Shuaib, 2018. "Peer to Peer Distributed Energy Trading in Smart Grids: A Survey," Energies, MDPI, vol. 11(6), pages 1-22, June.
    8. Wang, Ziyang & Sun, Mei & Gao, Cuixia & Wang, Xin & Ampimah, Benjamin Chris, 2021. "A new interactive real-time pricing mechanism of demand response based on an evaluation model," Applied Energy, Elsevier, vol. 295(C).
    9. Bilal Naji Alhasnawi & Basil H. Jasim & Zain-Aldeen S. A. Rahman & Josep M. Guerrero & M. Dolores Esteban, 2021. "A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources," IJERPH, MDPI, vol. 18(15), pages 1-24, July.
    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. Jhanvi Gupta & Sanskar Jain & Suprava Chakraborty & Vladimir Panchenko & Alexandr Smirnov & Igor Yudaev, 2023. "Advancing Sustainable Energy Transition: Blockchain and Peer-to-Peer Energy Trading in India’s Green Revolution," Sustainability, MDPI, vol. 15(18), pages 1-19, September.

    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. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2024. "Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources," Applied Energy, Elsevier, vol. 364(C).
    3. Álvaro Gutiérrez, 2022. "Optimization Trends in Demand-Side Management," Energies, MDPI, vol. 15(16), pages 1-3, August.
    4. Yanan Liu & Yixuan Gao & Yu Hao & Hua Liao, 2016. "The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data," Energies, MDPI, vol. 9(10), pages 1-11, October.
    5. 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).
    6. 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).
    7. You, Zhengjie & Lumpp, Sebastian Dirk & Doepfert, Markus & Tzscheutschler, Peter & Goebel, Christoph, 2024. "Leveraging flexibility of residential heat pumps through local energy markets," Applied Energy, Elsevier, vol. 355(C).
    8. Lee, Juyong & Cho, Youngsang, 2020. "Estimation of the usage fee for peer-to-peer electricity trading platform: The case of South Korea," Energy Policy, Elsevier, vol. 136(C).
    9. Ionica Oncioiu & Anca Gabriela Petrescu & Eugenia Grecu & Marius Petrescu, 2017. "Optimizing the Renewable Energy Potential: Myth or Future Trend in Romania," Energies, MDPI, vol. 10(6), pages 1-14, May.
    10. Mariko Almeida Carneiro & Diogo Da Fonseca-Soares & Lucian Hendyo Max Pereira & Angel Firmín Ramos-Ridao, 2022. "An Approach for Water and Energy Savings in Public Buildings: A Case Study of Brazilian Rail Company," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    11. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    12. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    13. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    14. Ma, Li & Wang, Lingfeng & Liu, Zhaoxi, 2021. "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, Elsevier, vol. 285(C).
    15. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "Low-carbon economic dispatch and energy sharing method of multiple Integrated Energy Systems from the perspective of System of Systems," Energy, Elsevier, vol. 244(PA).
    16. 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).
    17. 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).
    18. 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).
    19. Hu, Qian & Zhu, Ziqing & Bu, Siqi & Wing Chan, Ka & Li, Fangxing, 2021. "A multi-market nanogrid P2P energy and ancillary service trading paradigm: Mechanisms and implementations," Applied Energy, Elsevier, vol. 293(C).
    20. 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).

    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:3:p:1549-:d:1057545. 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.