TY - JOUR
T1 - Blockchain-Based Peer-to-Peer Energy Trading Method
AU - Thompson, Myles J.
AU - Sun, Hongjian
AU - Jiang, Jing
N1 - Funding information: This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 872172 TESTBED2 project.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Blockchain-enabled peer-to-peer energy trading provides a method for neighbours and communities to trade energy generated from local and distributed renewable energy sources. Effective matching can facilitate greater energy efficiency during transmission, increases user welfare through preference and improves power quality. The proposed algorithm builds upon work to develop a system of scoring an energy transaction. It uses a McAfee-priced double auction, and scores based upon preference of price, locality, and energy generation type, alongside the quantity of energy being traded. The algorithm pre-evaluates transactions to determine the optimal transactional pathway. The transaction carried out is that leading to the greatest cumulative score. Simulated over a range of scenarios, the proposed algorithm provides an average increase in user welfare of 75%. Commercially, the algorithm may be deployed in small to large settlements whilst remaining stable. By reducing power loss, the algorithm allows consumers to save 25% on their cost of energy, whilst providing a 50% increase in the revenue earned by prosumers.
AB - Blockchain-enabled peer-to-peer energy trading provides a method for neighbours and communities to trade energy generated from local and distributed renewable energy sources. Effective matching can facilitate greater energy efficiency during transmission, increases user welfare through preference and improves power quality. The proposed algorithm builds upon work to develop a system of scoring an energy transaction. It uses a McAfee-priced double auction, and scores based upon preference of price, locality, and energy generation type, alongside the quantity of energy being traded. The algorithm pre-evaluates transactions to determine the optimal transactional pathway. The transaction carried out is that leading to the greatest cumulative score. Simulated over a range of scenarios, the proposed algorithm provides an average increase in user welfare of 75%. Commercially, the algorithm may be deployed in small to large settlements whilst remaining stable. By reducing power loss, the algorithm allows consumers to save 25% on their cost of energy, whilst providing a 50% increase in the revenue earned by prosumers.
KW - Peer-to-peer energy trading
KW - smart grid
KW - blockchain
KW - matching algorithm
KW - renewable energy source
UR - https://www.scopus.com/pages/publications/85139447905
U2 - 10.17775/cseejpes.2021.00010
DO - 10.17775/cseejpes.2021.00010
M3 - Article
SN - 2096-0042
VL - 8
SP - 1318
EP - 1326
JO - CSEE Journal of Power and Energy Systems
JF - CSEE Journal of Power and Energy Systems
IS - 5
ER -