Microgrid energy management system for academic building

Usman Bashir Tayab; Junwei Lu; Fuwen Yang; Mojaharul Islam; Ali Zia; Jahangir Hossain

doi:10.1109/AUPEC48547.2019.211931

Microgrid energy management system for academic building

Usman Bashir Tayab, Junwei Lu, Fuwen Yang, Mojaharul Islam, Ali Zia, Jahangir Hossain

School of Engineering

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

1 Citation (Scopus)

Abstract

In this paper, an optimal energy management system (EMS) for grid-connected microgrid is proposed. The gridconnected microgrid system comprises of photovoltaic (PV) panel, and battery as an energy storage unit. The optimal EMS is aimed to minimize the total operating cost of grid-connected microgrid for academic building. The feedforward neural network with improved salp swarm alogrithm based on weight factor is used to determine the 24-hours ahead data forecasting of load demand and PV power, while improved salp swarm alogrithm based on weight factor (WSSA) is used to perform the day-ahead optimal scheduling to control the power flow between PV, energy storage unit, load and main grid. The proposed microgrid EMS (MGEMS) is simulated using MATLAB/Simulink. The simulation result shows the effectiveness and validity of presented EMS with academic load.

Original language	English
Title of host publication	2019 29th Australasian Universities Power Engineering Conference (AUPEC)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781728150437, 9781728150420
ISBN (Print)	9781728150444
DOIs	https://doi.org/10.1109/AUPEC48547.2019.211931
Publication status	Published - 2019
Event	2019 29th Australasian Universities Power Engineering Conference - Nadi, Fiji Duration: 26 Nov 2019 → 29 Nov 2019

Publication series

Name
ISSN (Print)	2474-1493
ISSN (Electronic)	2474-1507

Conference

Conference	2019 29th Australasian Universities Power Engineering Conference
Abbreviated title	AUPEC
Country/Territory	Fiji
City	Nadi
Period	26/11/19 → 29/11/19

Access to Document

10.1109/AUPEC48547.2019.211931

Cite this

@inproceedings{39ba242a5e5847188ba32535ad395799,

title = "Microgrid energy management system for academic building",

abstract = "In this paper, an optimal energy management system (EMS) for grid-connected microgrid is proposed. The gridconnected microgrid system comprises of photovoltaic (PV) panel, and battery as an energy storage unit. The optimal EMS is aimed to minimize the total operating cost of grid-connected microgrid for academic building. The feedforward neural network with improved salp swarm alogrithm based on weight factor is used to determine the 24-hours ahead data forecasting of load demand and PV power, while improved salp swarm alogrithm based on weight factor (WSSA) is used to perform the day-ahead optimal scheduling to control the power flow between PV, energy storage unit, load and main grid. The proposed microgrid EMS (MGEMS) is simulated using MATLAB/Simulink. The simulation result shows the effectiveness and validity of presented EMS with academic load.",

author = "Tayab, {Usman Bashir} and Junwei Lu and Fuwen Yang and Mojaharul Islam and Ali Zia and Jahangir Hossain",

year = "2019",

doi = "10.1109/AUPEC48547.2019.211931",

language = "English",

isbn = "9781728150444",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "1--5",

booktitle = "2019 29th Australasian Universities Power Engineering Conference (AUPEC)",

address = "United States",

note = "2019 29th Australasian Universities Power Engineering Conference, AUPEC ; Conference date: 26-11-2019 Through 29-11-2019",

}

Tayab, UB, Lu, J, Yang, F, Islam, M, Zia, A & Hossain, J 2019, Microgrid energy management system for academic building. in 2019 29th Australasian Universities Power Engineering Conference (AUPEC). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-5, 2019 29th Australasian Universities Power Engineering Conference, Nadi, Fiji, 26/11/19. https://doi.org/10.1109/AUPEC48547.2019.211931

Microgrid energy management system for academic building. / Tayab, Usman Bashir; Lu, Junwei; Yang, Fuwen et al.
2019 29th Australasian Universities Power Engineering Conference (AUPEC). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 1-5.

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

TY - GEN

T1 - Microgrid energy management system for academic building

AU - Tayab, Usman Bashir

AU - Lu, Junwei

AU - Yang, Fuwen

AU - Islam, Mojaharul

AU - Zia, Ali

AU - Hossain, Jahangir

PY - 2019

Y1 - 2019

N2 - In this paper, an optimal energy management system (EMS) for grid-connected microgrid is proposed. The gridconnected microgrid system comprises of photovoltaic (PV) panel, and battery as an energy storage unit. The optimal EMS is aimed to minimize the total operating cost of grid-connected microgrid for academic building. The feedforward neural network with improved salp swarm alogrithm based on weight factor is used to determine the 24-hours ahead data forecasting of load demand and PV power, while improved salp swarm alogrithm based on weight factor (WSSA) is used to perform the day-ahead optimal scheduling to control the power flow between PV, energy storage unit, load and main grid. The proposed microgrid EMS (MGEMS) is simulated using MATLAB/Simulink. The simulation result shows the effectiveness and validity of presented EMS with academic load.

AB - In this paper, an optimal energy management system (EMS) for grid-connected microgrid is proposed. The gridconnected microgrid system comprises of photovoltaic (PV) panel, and battery as an energy storage unit. The optimal EMS is aimed to minimize the total operating cost of grid-connected microgrid for academic building. The feedforward neural network with improved salp swarm alogrithm based on weight factor is used to determine the 24-hours ahead data forecasting of load demand and PV power, while improved salp swarm alogrithm based on weight factor (WSSA) is used to perform the day-ahead optimal scheduling to control the power flow between PV, energy storage unit, load and main grid. The proposed microgrid EMS (MGEMS) is simulated using MATLAB/Simulink. The simulation result shows the effectiveness and validity of presented EMS with academic load.

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U2 - 10.1109/AUPEC48547.2019.211931

DO - 10.1109/AUPEC48547.2019.211931

M3 - Conference proceeding contribution

SN - 9781728150444

SP - 1

EP - 5

BT - 2019 29th Australasian Universities Power Engineering Conference (AUPEC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 2019 29th Australasian Universities Power Engineering Conference

Y2 - 26 November 2019 through 29 November 2019

ER -

Microgrid energy management system for academic building

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