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

José Diogo Forte de Oliveira Luna, Amir Naspolini, Guilherme Nascimento Gouvêa dos Reis, Paulo Renato da Costa Mendes and Julio Elias Normey-Rico

Applied Energy, 2024, vol. 369, issue C, No S0306261924008493

Abstract: 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.

Keywords: Quality of Experience; Demand management; Model Predictive Control; Smart house; Renewable hydrogen (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123466

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