Optimal residential model predictive control energy management performance with PV microgeneration

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorGodina, Raduen_US
dc.contributor.authorRodrigues, Eduardo M. G.en_US
dc.contributor.authorPouresmaeil, Edrisen_US
dc.contributor.authorCatalao, Joao P. S.en_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.groupauthorRenewable Energies for Power Systemsen
dc.contributor.organizationUniversity of Beira Interioren_US
dc.contributor.organizationUniversity of Portoen_US
dc.date.accessioned2018-11-02T08:43:51Z
dc.date.available2018-11-02T08:43:51Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2021-05-26en_US
dc.date.issued2018-08en_US
dc.description.abstractThe energy demand of the residential sector and the adjacent option for fossil fuels has negative consequences by both greenhouse gases (GHG) and other air pollutants emissions. Since home energy demand consists mainly of energy requirements for space and water heating along with the energy dedicated for appliances, different strategies that aim to stimulate an efficient use of energy need to be reinforced at all levels of human activity. In this paper, a comprehensive comparison is made between the thermostat (ON/OFF), proportional-integral-derivative (PID) and Model Predictive Control (MPC) control models of a domestic heating, ventilation and air conditioning (HVAC) system controlling the temperature of a room. A power interface that adjusts the MPC dynamic range of the output command signal into a discrete two level control signal is proposed, as a new contribution to earlier studies. The model of the house with local solar microgeneration is assumed to be located in a Portuguese city. The household of the case study is subject to the local solar irradiance, temperature and 5 Time-of-Use (Toll) electricity rates applied on an entire week of August 2016. The purpose of the optimisation is to achieve the best compromise between temperature comfort levels and energy costs and also to assess which is the best electricity ToU rate option provided by the electricity retailer for the residential sector. Also, for each electrical load of the HVAC system, the energy and cost are calculated and the results are presented by varying the different MPC weight combination in order to obtain the best possible solution and increase the quality of the model. Finally, after the best tariff and controller are determined, the impact of the solar generation is assessed. (C) 2017 Elsevier Ltd. All rights reserved.en
dc.description.versionPeer revieweden
dc.format.extent14
dc.format.extent143-156
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationGodina, R, Rodrigues, E M G, Pouresmaeil, E & Catalao, J P S 2018, ' Optimal residential model predictive control energy management performance with PV microgeneration ', Computers and Operations Research, vol. 96, pp. 143-156 . https://doi.org/10.1016/j.cor.2017.12.003en
dc.identifier.doi10.1016/j.cor.2017.12.003en_US
dc.identifier.issn0305-0548
dc.identifier.otherPURE UUID: 3a916eca-c857-43be-84dc-36386204adb3en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/3a916eca-c857-43be-84dc-36386204adb3en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/29082746/ELEC_Codina_etal_Optimal_Residential_Model_CompOpRes_96_142_2018.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/34514
dc.identifier.urnURN:NBN:fi:aalto-201811025567
dc.language.isoenen
dc.relation.ispartofseriesCOMPUTERS AND OPERATIONS RESEARCHen
dc.relation.ispartofseriesVolume 96en
dc.rightsopenAccessen
dc.subject.keywordEnergy optimizationen_US
dc.subject.keywordModel predictive controlen_US
dc.subject.keywordEnergy management controlleren_US
dc.subject.keywordPhotovoltaic microgenerationen_US
dc.subject.keywordResidential buildingen_US
dc.subject.keywordMPCen_US
dc.subject.keywordSYSTEMen_US
dc.subject.keywordOPTIMIZATIONen_US
dc.subject.keywordSUSTAINABILITYen_US
dc.subject.keywordEFFICIENCYen_US
dc.subject.keywordBUILDINGSen_US
dc.subject.keywordIMPACTen_US
dc.subject.keywordTIMEen_US
dc.titleOptimal residential model predictive control energy management performance with PV microgenerationen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
Files