Forte De Oliveira Luna, José DiogoJosé DiogoForte De Oliveira LunaNaspolini, AmirAmirNaspoliniReis, Guilherme Nascimento Gouvêa DosGuilherme Nascimento Gouvêa DosReisCosta Mendes, Paulo Renato daPaulo Renato daCosta MendesNormey-Rico, Julio EliasJulio EliasNormey-Rico2024-08-052024-08-052024https://publica.fraunhofer.de/handle/publica/47242710.1016/j.apenergy.2024.1234662-s2.0-85194917925The present work introduces a general and novel quality-of-experience-aware energy management system. The said system is designed to be responsible for supervising the operation of a smart house, where it accounts for both economic performance and demand management (DM) actions taking into account user comfort, and adopting a quality of experience (QoE) metric. Considering the availability of distributed generation (DG), smart houses are taken as hybrid nano-grids (NGs), and the Energy Management System (EMS) works as a Nano-Grid-Central Controller. Part of the energy storage in this NG is done using renewable hydrogen, which results in a reduction of pollutant emissions. A Model Predictive Control (MPC) algorithm is the foundation for the proposed smart-house EMS, and its formulation as a mixed-integer quadratic programming (MIQP) optimization problem is given, which avoids the use of nonlinear optimization tools. Validated by simulation, the system achieves the required standards: runs the smart house for a year with a 21% electricity bill reduction and 77% reduction in user discomfort.enDemand managementModel Predictive ControlQuality of ExperienceRenewable hydrogenSmart housejournal article