ORCID Profile
0000-0001-5182-7938
Current Organisation
Monash University
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Electrical energy transmission networks and systems | Electrical engineering
Publisher: IEEE
Date: 10-2015
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 27-04-2021
Publisher: IEEE
Date: 11-2015
Publisher: Elsevier BV
Date: 06-2021
Publisher: ACM
Date: 22-06-2021
Publisher: IEEE
Date: 02-08-2020
Publisher: Elsevier BV
Date: 07-2021
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 11-11-2020
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: IEEE
Date: 11-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Publisher: IEEE
Date: 09-10-2020
Publisher: IEEE
Date: 03-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 12-2011
Publisher: Wiley
Date: 17-05-2012
DOI: 10.1002/WCM.2237
Publisher: Elsevier BV
Date: 12-2023
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: ACM
Date: 16-06-2023
Publisher: IEEE
Date: 03-2018
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: IEEE
Date: 25-10-2022
Publisher: ACM
Date: 22-06-2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: IEEE
Date: 12-2201
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 23-09-2023
Publisher: ACM
Date: 12-03-2120
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Elsevier BV
Date: 12-2020
Publisher: ACM
Date: 17-11-2021
Publisher: Elsevier BV
Date: 11-2021
Publisher: IEEE
Date: 17-07-2022
Publisher: IEEE
Date: 24-03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 10-2011
Publisher: Association for Computing Machinery (ACM)
Date: 10-01-2200
Abstract: The operation of a data center consumes a tremendous amount of electricity, and the energy cost accounts for a large portion of the data center's operation cost. This leads to a growing interest towards reducing the energy cost of data centers. One approach advocated in recent studies is to distribute the computation workload among multiple geographically dispersed data centers by exploiting the electricity price differences. However, the impact of load redistributions on the power grid is not well understood yet. This paper takes the first step towards tackling this important issue, by studying how the power grid can take advantage of the data center's load distribution proactively for the purpose of power load balancing. We model the interactions between power grid and data centers as a two-stage problem, where the power grid operator aims to balance the electric power load in the first stage, and the data centers seek to minimize their total energy cost in the second stage. We show that this two-stage problem is a bilevel program with an indefinite quadratic objective function, which cannot be solved efficiently using standard convex optimization algorithms. Therefore, we reformulate this bilevel optimization problem as a linear program with additional finite complementarity slackness conditions, and propose a branch and bound algorithm to attain the globally optimal solution. The simulation results demonstrate that our proposed scheme can improve the load balancing performance by around 12% in terms of the electric load index and reduce the energy cost of data centers by 46% on average.
Publisher: IEEE
Date: 09-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: IEEE
Date: 08-2018
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2021
DOI: 10.1049/ENC2.12043
Publisher: Institution of Engineering and Technology (IET)
Date: 04-01-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 08-2010
Start Date: 2014
End Date: 2018
Funder: Research Grants Council, University Grants Committee
View Funded ActivityStart Date: 01-2023
End Date: 01-2026
Amount: $439,454.00
Funder: Australian Research Council
View Funded Activity