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A fair and privacy-aware EV discharging strategy using decentralized whale optimization algorithm for minimizing cost of EVs and the EV aggregator

Gu, Yingqi orcid logoORCID: 0000-0001-5807-6102 and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2021) A fair and privacy-aware EV discharging strategy using decentralized whale optimization algorithm for minimizing cost of EVs and the EV aggregator. IEEE System Journal . ISSN 1932-8184

Abstract
A key motivation to fasten the roll-out of electric vehicles (EVs) to the market is to implement Vehicle-to-Grid (V2G) functionalities. With V2G in place, EV owners can have extra freedom to interact their battery energy with power grids, namely by selling their energy to the grid when their EVs are not in use. On the other hand, EV aggregators and utility companies can leverage the flexibility of the collected energy to implement various ancillary services to the grids, which may significantly reduce costs of, for instance, running spinning reserve of traditional power plants on the grid side. However, this extra freedom also poses practical challenges in terms of how to devise a discharging strategy for a group of EVs that is fair and in some sense optimal. In this paper, we present a new design of EV discharging strategy in a typical V2G energy trading framework whilst leveraging the whale optimization algorithm in a decentralized manner, a metaheuristic algorithm that has been shown effective in solving large-scale centralized optimization problems. We demonstrate that by using simple ideas of data shuffling and aggregation, one can design an EV discharging strategy in a fair, optimal and privacy-aware manner, where privacy refers to the fact that no critical information of EVs should be exchanged with the EV aggregator, and vice versa. The fairness implies that a common discharge rate needs to be sought for all EVs so that no one gets better benefits than others in the same V2G programme. Simulation results are presented to illustrate the efficacy of our proposed system.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Decentralized optimization; electric vehicles (EVs); vehicle-to-grid (V2G); whale optimization algorithm (WOA) Vehicle-to-grid; Discharges (electric); Batteries; Degradation; Cost function; Urban areas; Frequency control
Subjects:Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Computer simulation
Engineering > Systems engineering
Engineering > Electronic engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:IEEE
Official URL:https://dx.doi.org/10.1109/JSYST.2021.3050565
Copyright Information:© 2021 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Spain’s Ministry of Science and Innovation through projects TEC2010-21619- C04-03, TEC2008-06715-C02-02, CDTI - CENIT (AMIT project) and INNPACTO (PRECISION project), Comunidad de Madrid (ARTEMIS S2009/DPI-1802), France research programs ANR and ARC, the European Regional Development Funds (FEDER), and the European FP6 New Emerging Science and Technology
ID Code:25740
Deposited On:15 Apr 2021 11:34 by Mingming Liu . Last Modified 02 Feb 2023 04:30
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