P2P energy Trading Based on power generation and Load forecasting of Prosumers

Sui Zhang; Baoyue Wang; Siwan Huang; Jianheng Shi; Chen Jiang; Feng Chen; Shifo Dong; Yantao Wang; Xiaoxiang Li

doi:10.2991/978-94-6463-262-0_37

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P2P energy Trading Based on power generation and Load forecasting of Prosumers

Authors

Sui Zhang¹, Baoyue Wang¹^{, *}, Siwan Huang¹, Jianheng Shi¹, Chen Jiang¹, Feng Chen², Shifo Dong³, Yantao Wang⁴, Xiaoxiang Li¹

¹Huaneng Clean Energy Research Institute, Beijing, China

²Huaneng Hubei Branch Power Trading Operation Center, Wuhan, China

³Huaneng Lancang River Hydropower Inc, Kunming, China

⁴Qinbei Power Plant of Huaneng International Power Co., Ltd., Zhengzhou, China

^*Corresponding author. Email: by_wang2@qny.chng.com.cn

Corresponding Author

Baoyue Wang

Available Online 9 October 2023.

DOI: 10.2991/978-94-6463-262-0_37 How to use a DOI?
Keywords: P2P energy trading; combination of forecasts; prosumers; power generation and load forecasting
Abstract: With the development of distributed power sources for the community microgrid, an increasing number of energy consumers possessing local power generation abilities will gradually transform into prosumers, balancing the dual identities of power producers and consumers. Peer-to-peer (P2P) energy trading has the potential to reduce the total cost of prosumers in community microgrids. A P2P energy trading scenario with photovoltaic (PV) systems is designed in this paper. To substantiate the effects of accurate power generation and load forecasts on this scenario, an ensemble method integrating forecasting and P2P trading is proposed. Finally, as the accuracy of power generation and load forecasts improves, bills for individual prosumer and total cost within community microgrid will approach reality. The results of proposed method can be followed by participants within community microgrid.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
Series: Atlantis Highlights in Engineering
Publication Date: 9 October 2023
ISBN: 10.2991/978-94-6463-262-0_37
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-262-0_37 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Sui Zhang
AU  - Baoyue Wang
AU  - Siwan Huang
AU  - Jianheng Shi
AU  - Chen Jiang
AU  - Feng Chen
AU  - Shifo Dong
AU  - Yantao Wang
AU  - Xiaoxiang Li
PY  - 2023
DA  - 2023/10/09
TI  - P2P energy Trading Based on power generation and Load forecasting of Prosumers
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 326
EP  - 340
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_37
DO  - 10.2991/978-94-6463-262-0_37
ID  - Zhang2023
ER  -

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