A novel joint energy and demand management system for smart houses based on model predictive control, hybrid storage system and quality of experience concepts

• Published in: Applied energy 2024-09, Vol.369, p.123466, Article 123466
• Main Authors: Luna, José Diogo Forte de Oliveira, Naspolini, Amir, Reis, Guilherme Nascimento Gouvêa dos, Mendes, Paulo Renato da Costa, Normey-Rico, Julio Elias
• Format: Article
• Language: English
• Subjects: Demand management; Model Predictive Control; Quality of Experience; Renewable hydrogen; Smart house
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2024.123466

The 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. •A quality-of-experience-aware model predictive control energy management system-EMS.•The EMS accounts for economic performance, demand management and user comfort.•Part of the energy storage is done using renewable hydrogen, reducing emissions.