Distributed Foresighted Energy Management in Smart-Grid-Powered Cellular Networks

Xinruo Zhang; Mohammad Reza Nakhai; Gan Zheng; Sangarapillai Lambotharan; Jonathon A. Chambers

doi:10.1109/TVT.2019.2899464

Distributed Foresighted Energy Management in Smart-Grid-Powered Cellular Networks

Xinruo Zhang, Mohammad Reza Nakhai, Gan Zheng^*, Sangarapillai Lambotharan, Jonathon A. Chambers

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

151 Downloads (Pure)

Abstract

This paper studies energy management in a smart grid-powered cellular network consisting of an independent system operator (ISO) and multiple geographically distributed aggregators. The aggregators have energy storage devices and can purchase energy from the electric grid via the ISO to serve their users. To account for the uncertainty of the renewable energy supply as well as the impacts of multiple aggregators on the electric grid and energy prices, a foresighted strategy combined with the adaptive \epsilon-greedy method is developed for the aggregators to distributively and adaptively minimize the long-term overall cost of the system, based on the ahead-of-time decision making of the storage pre-charging amount. Simulation results validate that the proposed strategy surpasses a recent learning-based storage management design and a myopic design.

Original language	English
Article number	8643542
Pages (from-to)	4064-4068
Number of pages	5
Journal	IEEE Transactions on Vehicular Technology
Volume	68
Issue number	4
Early online date	18 Feb 2019
DOIs	https://doi.org/10.1109/TVT.2019.2899464
Publication status	Published - 1 Apr 2019

Keywords

Distributed management
energy management
online learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/TVT.2019.2899464Licence: CC BY

Distributed Foresighted Energy Management_ZHANG_Accepted9February2019_GOLD VoR (CC BY)Final published version, 787 KBLicence: CC BY

Cite this

@article{ab1dc2a973f440d5a169a27ad61ceeb0,

title = "Distributed Foresighted Energy Management in Smart-Grid-Powered Cellular Networks",

abstract = "This paper studies energy management in a smart grid-powered cellular network consisting of an independent system operator (ISO) and multiple geographically distributed aggregators. The aggregators have energy storage devices and can purchase energy from the electric grid via the ISO to serve their users. To account for the uncertainty of the renewable energy supply as well as the impacts of multiple aggregators on the electric grid and energy prices, a foresighted strategy combined with the adaptive \epsilon-greedy method is developed for the aggregators to distributively and adaptively minimize the long-term overall cost of the system, based on the ahead-of-time decision making of the storage pre-charging amount. Simulation results validate that the proposed strategy surpasses a recent learning-based storage management design and a myopic design.",

keywords = "Distributed management, energy management, online learning",

author = "Xinruo Zhang and Nakhai, {Mohammad Reza} and Gan Zheng and Sangarapillai Lambotharan and Chambers, {Jonathon A.}",

year = "2019",

month = apr,

day = "1",

doi = "10.1109/TVT.2019.2899464",

language = "English",

volume = "68",

pages = "4064--4068",

journal = "IEEE Transactions on Vehicular Technology",

issn = "0018-9545",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

TY - JOUR

T1 - Distributed Foresighted Energy Management in Smart-Grid-Powered Cellular Networks

AU - Zhang, Xinruo

AU - Nakhai, Mohammad Reza

AU - Zheng, Gan

AU - Lambotharan, Sangarapillai

AU - Chambers, Jonathon A.

PY - 2019/4/1

Y1 - 2019/4/1

N2 - This paper studies energy management in a smart grid-powered cellular network consisting of an independent system operator (ISO) and multiple geographically distributed aggregators. The aggregators have energy storage devices and can purchase energy from the electric grid via the ISO to serve their users. To account for the uncertainty of the renewable energy supply as well as the impacts of multiple aggregators on the electric grid and energy prices, a foresighted strategy combined with the adaptive \epsilon-greedy method is developed for the aggregators to distributively and adaptively minimize the long-term overall cost of the system, based on the ahead-of-time decision making of the storage pre-charging amount. Simulation results validate that the proposed strategy surpasses a recent learning-based storage management design and a myopic design.

AB - This paper studies energy management in a smart grid-powered cellular network consisting of an independent system operator (ISO) and multiple geographically distributed aggregators. The aggregators have energy storage devices and can purchase energy from the electric grid via the ISO to serve their users. To account for the uncertainty of the renewable energy supply as well as the impacts of multiple aggregators on the electric grid and energy prices, a foresighted strategy combined with the adaptive \epsilon-greedy method is developed for the aggregators to distributively and adaptively minimize the long-term overall cost of the system, based on the ahead-of-time decision making of the storage pre-charging amount. Simulation results validate that the proposed strategy surpasses a recent learning-based storage management design and a myopic design.

KW - Distributed management

KW - energy management

KW - online learning

UR - http://www.scopus.com/inward/record.url?scp=85064692776&partnerID=8YFLogxK

U2 - 10.1109/TVT.2019.2899464

DO - 10.1109/TVT.2019.2899464

M3 - Article

AN - SCOPUS:85064692776

SN - 0018-9545

VL - 68

SP - 4064

EP - 4068

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

IS - 4

M1 - 8643542

ER -

Distributed Foresighted Energy Management in Smart-Grid-Powered Cellular Networks

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this