Minimization of building energy cost by optimally managing PV and battery energy storage systems

Bonthu, RK; Pham, H; Aguilera, RP; Ha, QP

Minimization of building energy cost by optimally managing PV and battery energy storage systems

Bonthu, RK

Pham, H

Aguilera, RP

Ha, QP

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Publication Type:: Conference Proceeding
Citation:: 2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017
Issue Date:: 2017-10-02

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Field	Value	Language
dc.contributor.author	Bonthu, RK https://orcid.org/0000-0001-7779-3887	en_US
dc.contributor.author	Pham, H https://orcid.org/0000-0001-5122-5064	en_US
dc.contributor.author	Aguilera, RP https://orcid.org/0000-0003-4166-8341	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2017-10-02	en_US
dc.identifier.citation	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017	en_US
dc.identifier.isbn	9781538632468	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126223
dc.description.abstract	© 2017 IEEE. The combination of residential photovoltaic (PV) panels and battery energy storage system (BESS) is a promising solution in a building's microgrid. This paper presents an optimal energy management system (EMS) to minimize the electricity bill of residential buildings. The objective is to achieve peak shaving and electrical energy cost minimization for the owner, making use of a dynamic energy pricing model. Due to variability of PV generation and limitations of BESS, the optimization method should account for the utilisation of PV power generation and the conversion losses of power electronics converters. To deal with non-linearities involved, a particle swarm optimization (PSO) algorithm has been developed for minimization of the cost function. Simulation results show that the PSO-based EMS can achieve, as compared to standard schedule for a 24 hour period, a significant saving of daily electricity cost, which prompts for a suitable on-line control strategy.	en_US
dc.relation.ispartof	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017	en_US
dc.relation.isbasedon	10.1109/ICEMS.2017.8056442	en_US
dc.title	Minimization of building energy cost by optimally managing PV and battery energy storage systems	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0907 Environmental Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. The combination of residential photovoltaic (PV) panels and battery energy storage system (BESS) is a promising solution in a building's microgrid. This paper presents an optimal energy management system (EMS) to minimize the electricity bill of residential buildings. The objective is to achieve peak shaving and electrical energy cost minimization for the owner, making use of a dynamic energy pricing model. Due to variability of PV generation and limitations of BESS, the optimization method should account for the utilisation of PV power generation and the conversion losses of power electronics converters. To deal with non-linearities involved, a particle swarm optimization (PSO) algorithm has been developed for minimization of the cost function. Simulation results show that the PSO-based EMS can achieve, as compared to standard schedule for a 24 hour period, a significant saving of daily electricity cost, which prompts for a suitable on-line control strategy.

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http://hdl.handle.net/10453/126223