A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

Rezaeimozafar, M; Eskandari, M; Amini, MH; Moradi, MH; Siano, P

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

Rezaeimozafar, M Eskandari, M

Amini, MH Moradi, MH Siano, P

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Energies, 13 (7), pp. 1706 - 1706

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Field	Value	Language
dc.contributor.author	Rezaeimozafar, M	en_US
dc.contributor.author	Eskandari, M https://orcid.org/0000-0003-1185-9585	en_US
dc.contributor.author	Amini, MH	en_US
dc.contributor.author	Moradi, MH	en_US
dc.contributor.author	Siano, P https://orcid.org/0000-0002-0975-0241	en_US
dc.date.accessioned	2020-04-07T14:13:02Z
dc.date.available	2020-04-07T14:13:02Z
dc.identifier.citation	Energies, 13 (7), pp. 1706 - 1706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139871
dc.description.abstract	<jats:p>The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p>	en_US
dc.language	en	en_US
dc.publisher	MDPI AG	en_US
dc.relation.ispartof	Energies	en_US
dc.relation.isbasedon	10.3390/en13071706	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	13	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.issue	7	en_US
pubs.publication-status	Published online	en_US
pubs.volume	13	en_US

Abstract:

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.

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http://hdl.handle.net/10453/139871