research-article

ENTRUST

Authors:

Hussein T. Mouftah,

Alagan AnpalaganAuthors Info & Claims

Computer Networks: The International Journal of Computer and Telecommunications Networking, Volume 110, Issue C

Pages 232 - 242

https://doi.org/10.1016/j.comnet.2016.09.021

Published: 09 December 2016 Publication History

Abstract

In this paper, real-time energy trading in smart grid is modeled as an optimization process under uncertainties of demand and price information -źa problem perspective that is divergent from the ones in the existing literature. Energy trading in smart grid is affected by demand uncertainties -źintermittent behavior of renewable energy sources, packet loss in the communication network, and fluctuation in customers' demands. Energy trading is also affected by price uncertainty due to the demand uncertainties. In such uncertainty-prone scenario, we propose the algorithm named ENTRUST using the principles of robust game theory to maximize the payoff values for both sides -źcustomers, and grid. We show the existence of robust-optimization equilibrium for establishing the convergence of the game. Simulation results show that the proposed scheme performs better than the existing ones considered as benchmarks in this study. Utilities for the customers are also maximized in order to promote cost-effective and reliable energy management in the smart grid.

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Digital Library

Cited By

Nguyen THua CRehman UHussain MLee S(2023)CLEB: A Continual Learning Energy Bidding Framework For An Energy Market Bidding ApplicationProceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference10.1145/3639592.3639599(46-53)Online publication date: 16-Dec-2023
https://dl.acm.org/doi/10.1145/3639592.3639599

ENTRUST
1. General and reference
  1. Cross-computing tools and techniques

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Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 110, Issue C

December 2016

377 pages

ISSN:1389-1286

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Copyright © Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 09 December 2016

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Cited By

Nguyen THua CRehman UHussain MLee S(2023)CLEB: A Continual Learning Energy Bidding Framework For An Energy Market Bidding ApplicationProceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference10.1145/3639592.3639599(46-53)Online publication date: 16-Dec-2023
https://dl.acm.org/doi/10.1145/3639592.3639599

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