IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v242y2019icp1121-1133.html
   My bibliography  Save this article

Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model

Author

Listed:
  • Chen, Kaixuan
  • Lin, Jin
  • Song, Yonghua

Abstract

With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions.

Suggested Citation

  • Chen, Kaixuan & Lin, Jin & Song, Yonghua, 2019. "Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model," Applied Energy, Elsevier, vol. 242(C), pages 1121-1133.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1121-1133
    DOI: 10.1016/j.apenergy.2019.03.094
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919305045
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.03.094?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Chunyu & Wang, Qi & Wang, Jianhui & Korpås, Magnus & Pinson, Pierre & Østergaard, Jacob & Khodayar, Mohammad E., 2016. "Trading strategies for distribution company with stochastic distributed energy resources," Applied Energy, Elsevier, vol. 177(C), pages 625-635.
    2. Bahrami, Shahab & Amini, M. Hadi, 2018. "A decentralized trading algorithm for an electricity market with generation uncertainty," Applied Energy, Elsevier, vol. 218(C), pages 520-532.
    3. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    4. Liang, Zheming & Bian, Desong & Zhang, Xiaohu & Shi, Di & Diao, Ruisheng & Wang, Zhiwei, 2019. "Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market," Applied Energy, Elsevier, vol. 236(C), pages 916-926.
    5. Rajeev, T. & Ashok, S., 2015. "Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources," Applied Energy, Elsevier, vol. 146(C), pages 141-149.
    6. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    7. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    8. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    9. Lüth, Alexandra & Zepter, Jan Martin & Crespo del Granado, Pedro & Egging, Ruud, 2018. "Local electricity market designs for peer-to-peer trading: The role of battery flexibility," Applied Energy, Elsevier, vol. 229(C), pages 1233-1243.
    10. Zhang, Chenghua & Wu, Jianzhong & Zhou, Yue & Cheng, Meng & Long, Chao, 2018. "Peer-to-Peer energy trading in a Microgrid," Applied Energy, Elsevier, vol. 220(C), pages 1-12.
    11. Mengelkamp, Esther & Gärttner, Johannes & Rock, Kerstin & Kessler, Scott & Orsini, Lawrence & Weinhardt, Christof, 2018. "Designing microgrid energy markets," Applied Energy, Elsevier, vol. 210(C), pages 870-880.
    12. Hvelplund, Frede, 2006. "Renewable energy and the need for local energy markets," Energy, Elsevier, vol. 31(13), pages 2293-2302.
    13. Yael Parag & Benjamin K. Sovacool, 2016. "Electricity market design for the prosumer era," Nature Energy, Nature, vol. 1(4), pages 1-6, April.
    14. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    15. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    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. 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).
    2. Siripha Junlakarn & Phimsupha Kokchang & Kulyos Audomvongseree, 2022. "Drivers and Challenges of Peer-to-Peer Energy Trading Development in Thailand," Energies, MDPI, vol. 15(3), pages 1-25, February.
    3. Karami, Mahdi & Madlener, Reinhard, 2022. "Business models for peer-to-peer energy trading in Germany based on households’ beliefs and preferences," Applied Energy, Elsevier, vol. 306(PB).
    4. Mengelkamp, Esther & Schlund, David & Weinhardt, Christof, 2019. "Development and real-world application of a taxonomy for business models in local energy markets," Applied Energy, Elsevier, vol. 256(C).
    5. Guerrero, Jaysson & Gebbran, Daniel & Mhanna, Sleiman & Chapman, Archie C. & Verbič, Gregor, 2020. "Towards a transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    6. Li, Zhenpeng & Ma, Tao, 2020. "Peer-to-peer electricity trading in grid-connected residential communities with household distributed photovoltaic," Applied Energy, Elsevier, vol. 278(C).
    7. Ableitner, Liliane & Tiefenbeck, Verena & Meeuw, Arne & Wörner, Anselma & Fleisch, Elgar & Wortmann, Felix, 2020. "User behavior in a real-world peer-to-peer electricity market," Applied Energy, Elsevier, vol. 270(C).
    8. Kobashi, Takuro & Yoshida, Takahiro & Yamagata, Yoshiki & Naito, Katsuhiko & Pfenninger, Stefan & Say, Kelvin & Takeda, Yasuhiro & Ahl, Amanda & Yarime, Masaru & Hara, Keishiro, 2020. "On the potential of “Photovoltaics + Electric vehicles” for deep decarbonization of Kyoto’s power systems: Techno-economic-social considerations," Applied Energy, Elsevier, vol. 275(C).
    9. Alexandra Lüth & Jens Weibezahn & Jan Martin Zepter, 2020. "On Distributional Effects in Local Electricity Market Designs—Evidence from a German Case Study," Energies, MDPI, vol. 13(8), pages 1-26, April.
    10. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K., 2021. "Cooperative negawatt P2P energy trading for low-voltage distribution networks," Applied Energy, Elsevier, vol. 299(C).
    11. Maarten Wolsink, 2020. "Framing in Renewable Energy Policies: A Glossary," Energies, MDPI, vol. 13(11), pages 1-31, June.
    12. Abdullah M. Alabdullatif & Enrico H. Gerding & Alvaro Perez-Diaz, 2020. "Market Design and Trading Strategies for Community Energy Markets with Storage and Renewable Supply," Energies, MDPI, vol. 13(4), pages 1-31, February.
    13. Anees, Amir & Dillon, Tharam & Chen, Yi-Ping Phoebe, 2019. "A novel decision strategy for a bilateral energy contract," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Wenting Zhao & Jun Lv & Xilong Yao & Juanjuan Zhao & Zhixin Jin & Yan Qiang & Zheng Che & Chunwu Wei, 2019. "Consortium Blockchain-Based Microgrid Market Transaction Research," Energies, MDPI, vol. 12(20), pages 1-22, October.
    15. Andreolli, Francesca & D'Alpaos, Chiara & Kort, Peter, 2023. "Does P2P Trading Favor Investments in PV-Battery Systems?," FEEM Working Papers 330498, Fondazione Eni Enrico Mattei (FEEM).
    16. Rodrigues, Daniel L. & Ye, Xianming & Xia, Xiaohua & Zhu, Bing, 2020. "Battery energy storage sizing optimisation for different ownership structures in a peer-to-peer energy sharing community," Applied Energy, Elsevier, vol. 262(C).
    17. 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.
    18. El-Baz, Wessam & Tzscheutschler, Peter & Wagner, Ulrich, 2019. "Integration of energy markets in microgrids: A double-sided auction with device-oriented bidding strategies," Applied Energy, Elsevier, vol. 241(C), pages 625-639.
    19. Elkazaz, Mahmoud & Sumner, Mark & Thomas, David, 2021. "A hierarchical and decentralized energy management system for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 291(C).
    20. Zhou, Yue & Wu, Jianzhong & Song, Guanyu & Long, Chao, 2020. "Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community," Applied Energy, Elsevier, vol. 278(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:eee:appene:v:242:y:2019:i:c:p:1121-1133. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.