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Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System

AMEENA AL-SOROUR, Meghdad Fazeli Orcid Logo, Mohammad Monfared Orcid Logo, Ashraf Fahmy Abdo Orcid Logo

IEEE Access, Volume: 11, Pages: 12489 - 12503

Swansea University Authors: AMEENA AL-SOROUR, Meghdad Fazeli Orcid Logo, Mohammad Monfared Orcid Logo, Ashraf Fahmy Abdo Orcid Logo

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Abstract

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62478
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Abstract: The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. Mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community.
College: Faculty of Science and Engineering
Funders: This work was supported by Qatar National Research Fund (a member of Qatar Foundation) through QRLP10-G-19022034 grant
Start Page: 12489
End Page: 12503