Smart home energy flow optimization, a practical case study

This paper presents a practical framework for energy management in smart homes, based on a realistic case study of energy flow optimization involving controllable devices usually available in modern households, including both thermal and battery storage systems. The framework is defined based on a practical energy paradigm and formulates the optimization problem, including all equations and constraints. The smart home model includes a rooftop PV system, battery, a bidirectional Smart Charger, Deferrable Appliances, and an electrified Ground Source Heat Pump equipped with a Thermal Energy Storage. A dedicated dataset is built from peer-reviewed literature and statistical datasets following an easily replicable methodology. The period considered spans one year, capturing seasonal variations and hence providing a robust foundation for performance evaluation. Three case studies are developed using a Dynamic Programming (DP) algorithm, each targeting a distinct Objective Function: maximizing self-consumption, minimizing fluctuations in the power exchanged with the grid, and promoting a constant daily exchange. Compared to a baseline configuration without an energy management system, results show that minimizing power fluctuations leads to a nearly 100% improvement in squared temporal variations of grid energy exchanges. The self-consumption case yields a limited 13% improvement due to an unfavorable production–demand imbalance, while the third case achieves a 28% improvement in maintaining a flat daily grid energy exchange. As an additional element of novelty, this work wishes to provide a valuable reference for future research, as the proposed DP-based framework allows a simple and thorough evaluation of the flexibility contributions provided by each single energy device, an approach rarely explored in existing literature.