Energy cost optimization in microgrids using model predictive control and mixed integer linear programming

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

Energy cost optimization in microgrids using model predictive control and mixed integer linear programming

Bonthu, RK

Aguilera, RP

Pham, H

Phung, MD

Ha, QP

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Industrial Technology, 2019, 2019-February pp. 1113 - 1118
Issue Date:: 2019-02-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (2.18 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Bonthu, RK https://orcid.org/0000-0001-7779-3887	en_US
dc.contributor.author	Aguilera, RP https://orcid.org/0000-0003-4166-8341	en_US
dc.contributor.author	Pham, H https://orcid.org/0000-0001-5122-5064	en_US
dc.contributor.author	Phung, MD https://orcid.org/0000-0002-8870-5114	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.available	2021-02-15T18:05:27Z
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	Proceedings of the IEEE International Conference on Industrial Technology, 2019, 2019-February pp. 1113 - 1118	en_US
dc.identifier.isbn	9781538663769	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130869
dc.description.abstract	© 2019 IEEE. This paper presents a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) to develop an optimal power management strategy (PMS) for minimizing the electricity bill of commercial buildings in a domestic on-grid system. The optimal PMS is first formulated as a MILP-MPC with time-varying constraints. The constraints are then linearized at each sampling time so that a receding horizon principle can be used to determine the control input applied to the plant and update the model. The time-varying efficiency of power electronic converters is evaluated for each time interval and assumed to be persistent for the prediction time horizon. The numerical results show that the proposed MILP-MPC strategy with variable efficiency is effective in utilizing photovoltaic power generation to save the cost on electricity for buildings.	en_US
dc.relation.ispartof	Proceedings of the IEEE International Conference on Industrial Technology	en_US
dc.relation.isbasedon	10.1109/ICIT.2019.8754971	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Energy cost optimization in microgrids using model predictive control and mixed integer linear programming	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-February	en_US
utslib.for	0906 Electrical and Electronic 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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-February	en_US

Abstract:

© 2019 IEEE. This paper presents a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) to develop an optimal power management strategy (PMS) for minimizing the electricity bill of commercial buildings in a domestic on-grid system. The optimal PMS is first formulated as a MILP-MPC with time-varying constraints. The constraints are then linearized at each sampling time so that a receding horizon principle can be used to determine the control input applied to the plant and update the model. The time-varying efficiency of power electronic converters is evaluated for each time interval and assumed to be persistent for the prediction time horizon. The numerical results show that the proposed MILP-MPC strategy with variable efficiency is effective in utilizing photovoltaic power generation to save the cost on electricity for buildings.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/130869