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dc.contributor.authorAhmad, Aziz
dc.contributor.authorAnderson, T.N.
dc.contributor.authorSwain, A.
dc.contributor.authorLie, T.T.
dc.contributor.authorCurrie, J.
dc.contributor.authorHolmes, Wayne
dc.date.accessioned2017-05-25T21:30:48Z
dc.date.available2017-05-25T21:30:48Z
dc.date.issued2016-12
dc.identifier.urihttps://hdl.handle.net/10652/3746
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 (dish washer, washing machine and dryer) and use battery energy if necessary.en_NZ
dc.language.isoenen_NZ
dc.subjectphotovoltaic cellsen_NZ
dc.subjecthousehold appliancesen_NZ
dc.subjectgrid-connected photovoltaic (PV) based power generationen_NZ
dc.titleResidential household electrical appliance management using model predictive control of a grid connected photovoltaic-battery systemen_NZ
dc.typeConference Contribution - Oral Presentationen_NZ
dc.date.updated2017-05-10T05:35:41Z
dc.rights.holderAuthorsen_NZ
dc.subject.marsden090605 Photodetectors, Optical Sensors and Solar Cellsen_NZ
dc.identifier.bibliographicCitationAhmad, A., Anderson, T., Swain, A., Lie, T., Currie, J., & Holmes, W. (2016, December). Residential Household Electrical Appliance Management Using Model Predictive Control of a Grid Connected Photovoltaic-Battery System. Paper presented at 2016 Asia-Pacific Solar Research Conference, Canberra, Australia. (pp.97-108)en_NZ
unitec.publication.title2016 Asia-Pacific Solar Research Conferenceen_NZ
unitec.conference.title2016 Asia-Pacific Solar Research Conferenceen_NZ
unitec.conference.locationCanberra, Australiaen_NZ
unitec.conference.sdate2016-12
unitec.conference.edate2016-12
unitec.peerreviewedyesen_NZ
unitec.identifier.roms59769en_NZ
unitec.identifier.roms60550
unitec.institution.studyareaConstruction + Engineering


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