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dc.contributor.authorAhmad, Aen_NZ
dc.contributor.authorAnderson, Ten_NZ
dc.contributor.authorSwain, AKen_NZ
dc.contributor.authorLie, TTen_NZ
dc.contributor.authorCurrie, Jen_NZ
dc.date.accessioned2018-12-19T00:20:22Z
dc.date.available2018-12-19T00:20:22Z
dc.date.copyright2016-11-28en_NZ
dc.identifier.citationProceedings of the Asia Pacific Solar Research Conference 2016, Publisher: Australian PV Institute, Editors: R. Egan and R. Passey, Dec 2016, ISBN: 978-0- 6480414-0-5
dc.identifier.urihttp://hdl.handle.net/10292/12122
dc.description.abstractGrid-connected photovoltaic (PV) based power generation technology is being pushed to the forefront as a viable alternative source of renewable energy, particularly in small-scale domestic applications. Due to the variable nature of solar energy, PV usually works well with battery storage to provide continuous and stable energy. However, by incorporating storage with such systems there is a need to develop controllers that allow the owners to maximize the benefit of such systems and so require sophisticated control strategies. In this work a multiple-input multiple-output (MIMO) state space model of a PV array, load energy demand, battery bank and utility grid was used to develop a model predictive control setup for a grid connected photovoltaic-battery power generation system. Artificial neural network (ANN) based energy demand prediction was used as the output measured disturbance for the MPC. Switched constraints were used for the MIMO state space model to mimic the dynamic behavior of the storage system. Simulation results show that the proposed MPC would activate non-critical electrical appliances usage at periods when excess PV energy was available from the PV array. Further, it would also allocate energy to the battery storage when this was available, and, when load energy demand was more than the PV array produced would deactivate non-critical appliances and use battery energy if necessary.en_NZ
dc.publisherAustralian PV Instituteen_NZ
dc.relation.urihttp://apvi.org.au/solar-research-conference/wp-content/uploads/2017/02/A.Ahmad-Residential-Household-Electrical-Appliance-Management-Using-Model-Predictive-Control-of-a-Grid-Connected-Photovoltaic-Battery-System.pdf
dc.rightsAuthors retain the right to place his/her publication version of the work on a personal website or institutional repository for non commercial purposes. The definitive version was published in (see Citation). The original publication is available at (see Publisher’s Version).
dc.titleResidential Household Electrical Appliance Management Using Model Predictive Control of a Grid Connected Photovoltaic-Battery Systemen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
pubs.elements-id219444
aut.relation.conference2016 Asia-Pacific Solar Research Conferenceen_NZ


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