Optimal energy management of a grid-connected multiple energy carrier micro-grid

Moghaddas-Tafreshi, SM; Mohseni, S; Karami, ME; Kelly, S

Optimal energy management of a grid-connected multiple energy carrier micro-grid

Moghaddas-Tafreshi, SM Mohseni, S Karami, ME Kelly, S

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Publication Type:: Journal Article
Citation:: Applied Thermal Engineering, 2019, 152 pp. 796 - 806
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Moghaddas-Tafreshi, SM	en_US
dc.contributor.author	Mohseni, S	en_US
dc.contributor.author	Karami, ME	en_US
dc.contributor.author	Kelly, S https://orcid.org/0000-0002-8454-6144	en_US
dc.date.available	2021-04-01T18:02:19Z
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	Applied Thermal Engineering, 2019, 152 pp. 796 - 806	en_US
dc.identifier.issn	1359-4311	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138152
dc.description.abstract	© 2019 Elsevier Ltd This paper presents a novel modeling approach to optimize the electrical and thermal energy management of a multiple energy carrier micro-grid with the aim of minimizing the operation cost such that system constraints are satisfied. The proposed micro-grid includes a micro-turbine, a fuel cell, a rubbish burning power plant, a wind turbine generator system, a boiler, an anaerobic reactor-reformer system, an inverter, a rectifier, and some energy storage units. The model uses day-ahead forecasting (24 h) to estimate the electrical and thermal loads on a micro-grid network. A day-ahead forecast is also used to estimate electricity generation from wind turbines. Due to the uncertainty associated with day-ahead forecasts, a Monte Carlo simulation is used to estimate thermal loads, electrical loads, and wind power generation. Also, a real-time pricing demand response program is used to shift non-vital loads. The operating cost of the micro-grid is minimized through the particle swarm optimization algorithm. The simulation results demonstrate the proposed modeling framework is superior over conventional centralized optimal scheduling models widely used in the literature in terms of reducing operating cost and computational complexity. In addition, the results obtained by applying the proposed modeling framework are analyzed and validated through scenario testing.	en_US
dc.relation.ispartof	Applied Thermal Engineering	en_US
dc.relation.isbasedon	10.1016/j.applthermaleng.2019.02.113	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Energy	en_US
dc.title	Optimal energy management of a grid-connected multiple energy carrier micro-grid	en_US
dc.type	Journal Article
utslib.citation.volume	152	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
pubs.organisational-group	/University of Technology Sydney/Strength - ISF - Institute for Sustainable Futures
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	152	en_US

Abstract:

© 2019 Elsevier Ltd This paper presents a novel modeling approach to optimize the electrical and thermal energy management of a multiple energy carrier micro-grid with the aim of minimizing the operation cost such that system constraints are satisfied. The proposed micro-grid includes a micro-turbine, a fuel cell, a rubbish burning power plant, a wind turbine generator system, a boiler, an anaerobic reactor-reformer system, an inverter, a rectifier, and some energy storage units. The model uses day-ahead forecasting (24 h) to estimate the electrical and thermal loads on a micro-grid network. A day-ahead forecast is also used to estimate electricity generation from wind turbines. Due to the uncertainty associated with day-ahead forecasts, a Monte Carlo simulation is used to estimate thermal loads, electrical loads, and wind power generation. Also, a real-time pricing demand response program is used to shift non-vital loads. The operating cost of the micro-grid is minimized through the particle swarm optimization algorithm. The simulation results demonstrate the proposed modeling framework is superior over conventional centralized optimal scheduling models widely used in the literature in terms of reducing operating cost and computational complexity. In addition, the results obtained by applying the proposed modeling framework are analyzed and validated through scenario testing.

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http://hdl.handle.net/10453/138152