Open AccessClosest Energy Matching: Improving peer‐to‐peer energy trading auctions for EV owners
Author(s) -
Thomas Huw,
Sun Hongjian,
Kazemtabrizi Behzad
Publication year - 2021
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/stg2.12016
Subject(s) - common value auction , revenue , matching (statistics) , computer science , peer to peer , double auction , mechanism (biology) , energy (signal processing) , microeconomics , operations research , environmental economics , business , mathematical optimization , economics , distributed computing , engineering , finance , mathematics , statistics , philosophy , epistemology
Herein, a novel approach to conduct peer‐to‐peer energy auctions for electric vehicles (EVs) to benefit both buyers and sellers is presented. It considers a scenario where households can sell their surplus solar energy to visiting EVs that make use of the households' vacant charge points during the day. The aim of the energy trading is to maximise the amount of charge EVs receive from the solar energy, and increase the revenue for sellers. The novel Closest Energy Matching (CEM) double auction mechanism is proposed and it is compared with four other mechanisms. CEM allows the auction to take into account current energy requests as well as the potential future demand without requiring additional information. A novel algorithm, MARMES (MAtrix Ranking for Maximising Element Selection), is also presented to solve the optimisation problem that forms the basis of the CEM mechanism. The CEM mechanism on average results in 21.5% more solar energy used, lower cost to the consumer, a 24.9% increase in profits for sellers and a 71.4% reduction in required grid energy compared with the traditional double auction mechanism.