IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics036054422302830x.html
   My bibliography  Save this article

Energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading

Author

Listed:
  • Zhou, Kaile
  • Chu, Yibo
  • Hu, Rong

Abstract

With the penetration of large amounts of renewable energy resources into energy system, the interaction between energy supply and demand has become more complex and diverse. The complexity and diversity make it more difficult to achieve real-time, efficient, accurate and dynamic matching of energy supply and demand. Therefore, the study proposes an efficient energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading. First, to reduce the impact of supply and demand uncertainty on the energy supply and demand matching, gate recurrent unit and long short-term memory models are used to forecast power generation and consumption. Then, based on the results of forecasting, an energy supply-demand interaction model is proposed to assist the energy system in achieving dynamic energy supply-demand matching. Finally, the effectiveness of the proposed energy supply-demand interaction model has been verified through experiments. The proposed energy supply-demand interaction model that considers supply and demand uncertainty and economic benefits helps to better achieve transparent, efficient, stable, and sustainable matching of supply and demand. This study can reduce the impact of supply and demand uncertainty by forecasting power generation and consumption. In addition, this study considers the preferences of prosumers in their trading, reduces the cost of electricity for prosumers, and realizes the profitability of multiple subjects involved in the trading.

Suggested Citation

  • Zhou, Kaile & Chu, Yibo & Hu, Rong, 2023. "Energy supply-demand interaction model integrating uncertainty forecasting and peer-to-peer energy trading," Energy, Elsevier, vol. 285(C).
  • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s036054422302830x
    DOI: 10.1016/j.energy.2023.129436
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422302830X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.129436?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. Han, Dong & Zhang, Chengzhenghao & Ping, Jian & Yan, Zheng, 2020. "Smart contract architecture for decentralized energy trading and management based on blockchains," Energy, Elsevier, vol. 199(C).
    2. Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    3. Javadi, Mohammad Sadegh & Esmaeel Nezhad, Ali & Jordehi, Ahmad Rezaee & Gough, Matthew & Santos, Sérgio F. & Catalão, João P.S., 2022. "Transactive energy framework in multi-carrier energy hubs: A fully decentralized model," Energy, Elsevier, vol. 238(PB).
    4. Ravindra, Kumudhini & Iyer, Parameshwar P., 2014. "Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis," Energy, Elsevier, vol. 76(C), pages 32-41.
    5. 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).
    6. Clerjon, Arthur & Perdu, Fabien, 2022. "Matching intermittent electricity supply and demand with electricity storage - An optimization based on a time scale analysis," Energy, Elsevier, vol. 241(C).
    7. Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    8. Nykyri, Mikko & Kärkkäinen, Tommi J. & Levikari, Saku & Honkapuro, Samuli & Annala, Salla & Silventoinen, Pertti, 2022. "Blockchain-based balance settlement ledger for energy communities in open electricity markets," Energy, Elsevier, vol. 253(C).
    9. Klein, Lurian P. & Matos, Luisa M. & Allegretti, Giovanni, 2020. "A pragmatic approach towards end-user engagement in the context of peer-to-peer energy sharing," Energy, Elsevier, vol. 205(C).
    10. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    11. Zulfiqar, M. & Kamran, M. & Rasheed, M.B., 2022. "A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid," Energy, Elsevier, vol. 255(C).
    12. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    13. Dai, Yeming & Wang, Yanxin & Leng, Mingming & Yang, Xinyu & Zhou, Qiong, 2022. "LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method," Energy, Elsevier, vol. 256(C).
    14. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    15. Kim, Hansung & Cheon, Hyungkyu & Ahn, Young-Hwan & Choi, Dong Gu, 2019. "Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models," Energy, Elsevier, vol. 168(C), pages 370-379.
    16. Liu, Zhiyuan & Yu, Hang & Liu, Rui, 2019. "A novel energy supply and demand matching model in park integrated energy system," Energy, Elsevier, vol. 176(C), pages 1007-1019.
    17. Singh, Kamini & Gadh, Rajit & Singh, Anoop & Lal Dewangan, Chaman, 2022. "Design of an optimal P2P energy trading market model using bilevel stochastic optimization," Applied Energy, Elsevier, vol. 328(C).
    18. Li, Longxi & Cao, Xilin & Wang, Peng, 2021. "Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties," Energy, Elsevier, vol. 227(C).
    19. Kong, Xiangyu & Xiao, Jie & Wang, Chengshan & Cui, Kai & Jin, Qiang & Kong, Deqian, 2019. "Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant," Applied Energy, Elsevier, vol. 249(C), pages 178-189.
    20. Mehdinejad, Mehdi & Shayanfar, Heidarali & Mohammadi-Ivatloo, Behnam, 2022. "Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers," Energy, Elsevier, vol. 244(PA).
    21. An, Jongbaek & Lee, Minhyun & Yeom, Seungkeun & Hong, Taehoon, 2020. "Determining the Peer-to-Peer electricity trading price and strategy for energy prosumers and consumers within a microgrid," Applied Energy, Elsevier, vol. 261(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. Zhang, Rongquan & Bu, Siqi & Li, Gangqiang, 2024. "Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach," Applied Energy, Elsevier, vol. 367(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. Mehdinejad, Mehdi & Shayanfar, Heidarali & Mohammadi-Ivatloo, Behnam, 2022. "Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers," Energy, Elsevier, vol. 244(PA).
    2. 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.
    3. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    4. Lopez, Hector K. & Zilouchian, Ali, 2023. "Peer-to-peer energy trading for photo-voltaic prosumers," Energy, Elsevier, vol. 263(PA).
    5. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    6. Ruan, Hebin & Gao, Hongjun & Qiu, Haifeng & Gooi, Hoay Beng & Liu, Junyong, 2023. "Distributed operation optimization of active distribution network with P2P electricity trading in blockchain environment," Applied Energy, Elsevier, vol. 331(C).
    7. Dai, Yeming & Yu, Weijie & Leng, Mingming, 2024. "A hybrid ensemble optimized BiGRU method for short-term photovoltaic generation forecasting," Energy, Elsevier, vol. 299(C).
    8. Salla Annala & Lurian Klein & Luisa Matos & Sirpa Repo & Olli Kilkki & Arun Narayanan & Samuli Honkapuro, 2021. "Framework to Facilitate Electricity and Flexibility Trading within, to, and from Local Markets," Energies, MDPI, vol. 14(11), pages 1-20, May.
    9. Liu, Xiangjie & Liu, Yuanyan & Kong, Xiaobing & Ma, Lele & Besheer, Ahmad H. & Lee, Kwang Y., 2023. "Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis," Energy, Elsevier, vol. 271(C).
    10. Arnob Das & Susmita Datta Peu & Md. Abdul Mannan Akanda & Abu Reza Md. Towfiqul Islam, 2023. "Peer-to-Peer Energy Trading Pricing Mechanisms: Towards a Comprehensive Analysis of Energy and Network Service Pricing (NSP) Mechanisms to Get Sustainable Enviro-Economical Energy Sector," Energies, MDPI, vol. 16(5), pages 1-27, February.
    11. Hussain, Sadam & Azim, M. Imran & Lai, Chunyan & Eicker, Ursula, 2023. "New coordination framework for smart home peer-to-peer trading to reduce impact on distribution transformer," Energy, Elsevier, vol. 284(C).
    12. Franko Pandžić & Tomislav Capuder, 2023. "Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources," Energies, MDPI, vol. 17(1), pages 1-19, December.
    13. Michael, Neethu Elizabeth & Bansal, Ramesh C. & Ismail, Ali Ahmed Adam & Elnady, A. & Hasan, Shazia, 2024. "A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation," Renewable Energy, Elsevier, vol. 222(C).
    14. 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).
    15. Rosen, Karol & Angeles-Camacho, César & Elvira, Víctor & Guillén-Burguete, Servio Tulio, 2023. "Intra-hour photovoltaic forecasting through a time-varying Markov switching model," Energy, Elsevier, vol. 278(PB).
    16. Osório, G.J. & Lujano-Rojas, J.M. & Matias, J.C.O. & Catalão, J.P.S., 2015. "A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources," Energy, Elsevier, vol. 82(C), pages 949-959.
    17. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    18. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    19. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    20. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(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:energy:v:285:y:2023:i:c:s036054422302830x. 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.journals.elsevier.com/energy .

    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.