LAPSE:2023.35436
Published Article
LAPSE:2023.35436
Optimisation of Buyer and Seller Preferences for Peer-to-Peer Energy Trading in a Microgrid
Shama Naz Islam, Aiswarya Sivadas
April 28, 2023
In this paper, an optimisation approach to prioritise buyers and sellers in a peer-to-peer (P2P) energy trading market based on distances from the aggregator has been developed. The proposed approach assigns higher preferences to buyers/sellers with a smaller distance, as this will allow lower losses in the power transmission. Under this approach, the sellers and buyers operate in a decentralised manner to optimise the preference coefficients along with the energy sold/purchased to achieve certain profits/savings. The proposed approach is implemented using a real-life dataset, and the impacts of different parameters, such as seasonal variations in renewable generation, distances and profit thresholds for sellers, have been investigated. The results show that the proposed approach allows buyers and sellers to purchase/sell more energy from the P2P trading market (2.4 times increase when maximum energy sold is considered) in comparison to the case when all participants are equally preferred. It has been observed that, with increasing distances, sellers are assigned a smaller preference coefficient, which results in sellers being willing to sell a higher amount of energy so that they can achieve the same profit threshold.
Keywords
distance, energy mismatch, microgrid, peer-to-peer energy trading, preference coefficient
Suggested Citation
Islam SN, Sivadas A. Optimisation of Buyer and Seller Preferences for Peer-to-Peer Energy Trading in a Microgrid. (2023). LAPSE:2023.35436
Author Affiliations
Islam SN: School of Engineering, Deakin University, Geelong, VIC 3216, Australia [ORCID]
Sivadas A: School of Engineering, Deakin University, Geelong, VIC 3216, Australia
Journal Name
Energies
Volume
15
Issue
12
First Page
4212
Year
2022
Publication Date
2022-06-08
Published Version
ISSN
1996-1073
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Original Submission
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PII: en15124212, Publication Type: Journal Article
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LAPSE:2023.35436
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doi:10.3390/en15124212
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