Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm

Fengji Luo; Gianluca Ranzi; Gaoqi Liang; Zhao Yang Dong

doi:10.1109/PESGM.2017.8274308

Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm

Fengji Luo
, Gianluca Ranzi
, Gaoqi Liang
, Zhao Yang Dong

The University of Sydney
University of Newcastle

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

This paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house's historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method.

Original language	English
Title of host publication	2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Publisher	IEEE Computer Society
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781538622124
DOIs	https://doi.org/10.1109/PESGM.2017.8274308
State	Published - 29 Jan 2018
Externally published	Yes
Event	2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States Duration: 16 Jul 2017 → 20 Jul 2017

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2018-January
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Country/Territory	United States
City	Chicago
Period	16/07/17 → 20/07/17

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Demand response
Multi-objective optimization
Natural aggregation algorithm
Smart grid
Smart home

Access to Document

10.1109/PESGM.2017.8274308

Cite this

@inproceedings{a68c943ec8a44ef3a9f3350ea20098e5,

title = "Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm",

abstract = "This paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house's historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method.",

keywords = "Demand response, Multi-objective optimization, Natural aggregation algorithm, Smart grid, Smart home",

author = "Fengji Luo and Gianluca Ranzi and Gaoqi Liang and Dong, \{Zhao Yang\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Power and Energy Society General Meeting, PESGM 2017 ; Conference date: 16-07-2017 Through 20-07-2017",

year = "2018",

month = jan,

day = "29",

doi = "10.1109/PESGM.2017.8274308",

language = "英语",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

pages = "1--5",

booktitle = "2017 IEEE Power and Energy Society General Meeting, PESGM 2017",

address = "美国",

}

Luo, F, Ranzi, G, Liang, G & Dong, ZY 2018, Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm. in 2017 IEEE Power and Energy Society General Meeting, PESGM 2017. IEEE Power and Energy Society General Meeting, vol. 2018-January, IEEE Computer Society, pp. 1-5, 2017 IEEE Power and Energy Society General Meeting, PESGM 2017, Chicago, United States, 16/07/17. https://doi.org/10.1109/PESGM.2017.8274308

Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm. / Luo, Fengji; Ranzi, Gianluca; Liang, Gaoqi et al.
2017 IEEE Power and Energy Society General Meeting, PESGM 2017. IEEE Computer Society, 2018. p. 1-5 (IEEE Power and Energy Society General Meeting; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm

AU - Luo, Fengji

AU - Ranzi, Gianluca

AU - Liang, Gaoqi

AU - Dong, Zhao Yang

PY - 2018/1/29

Y1 - 2018/1/29

N2 - This paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house's historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method.

AB - This paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house's historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method.

KW - Demand response

KW - Multi-objective optimization

KW - Natural aggregation algorithm

KW - Smart grid

KW - Smart home

UR - https://www.scopus.com/pages/publications/85046366499

U2 - 10.1109/PESGM.2017.8274308

DO - 10.1109/PESGM.2017.8274308

M3 - 会议稿件

AN - SCOPUS:85046366499

T3 - IEEE Power and Energy Society General Meeting

SP - 1

EP - 5

BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017

PB - IEEE Computer Society

T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017

Y2 - 16 July 2017 through 20 July 2017

ER -

Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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