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<p>This study introduces a novel Home Energy Management System (HEMS) that integrates a Mixed Integer Linear Programming (MILP) model with a digital twin and blockchain framework to optimize energy consumption in smart homes. Addressing the need for cost-effective and sustainable energy management, the proposed methodology schedules Energy Storage Systems (ESS), Electric Vehicles (EV), Heating, Ventilation, and Air Conditioning (HVAC), water heaters, and appliances over a 24-h horizon with 96&#xa0;time steps, leveraging real-time data from the digital twin to ensure adaptability to dynamic conditions like Real-Time Pricing (RTP) and renewable generation from Photovoltaic (PV) and wind turbine systems. Blockchain technology enhances transparency by securely logging grid transactions, supporting potential Peer-to-Peer (P2P) trading. The model’s robust optimization ensures user comfort and operational reliability while achieving a 70.49% cost reduction (€4.83 vs. €16.36 baseline) in simulations conducted in Python using the Coin-or Branch and Cut (CBC) solver. Sensitivity analysis demonstrates robustness, with costs varying ± 13% under fluctuations in RTP, PV/wind generation, ESS capacity, and EV availability, positioning the model as a practical, scalable solution for smart home energy management with significant cost savings and secure transaction capabilities. In this case study, the digital twin is emulated by streaming a real-life smart home dataset, while the overall architecture is designed for real‑time deployment.</p>

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Optimizing smart home energy management: a mixed integer linear programming model with digital twin and blockchain integration

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Ubaid ur Rehman

摘要

This study introduces a novel Home Energy Management System (HEMS) that integrates a Mixed Integer Linear Programming (MILP) model with a digital twin and blockchain framework to optimize energy consumption in smart homes. Addressing the need for cost-effective and sustainable energy management, the proposed methodology schedules Energy Storage Systems (ESS), Electric Vehicles (EV), Heating, Ventilation, and Air Conditioning (HVAC), water heaters, and appliances over a 24-h horizon with 96 time steps, leveraging real-time data from the digital twin to ensure adaptability to dynamic conditions like Real-Time Pricing (RTP) and renewable generation from Photovoltaic (PV) and wind turbine systems. Blockchain technology enhances transparency by securely logging grid transactions, supporting potential Peer-to-Peer (P2P) trading. The model’s robust optimization ensures user comfort and operational reliability while achieving a 70.49% cost reduction (€4.83 vs. €16.36 baseline) in simulations conducted in Python using the Coin-or Branch and Cut (CBC) solver. Sensitivity analysis demonstrates robustness, with costs varying ± 13% under fluctuations in RTP, PV/wind generation, ESS capacity, and EV availability, positioning the model as a practical, scalable solution for smart home energy management with significant cost savings and secure transaction capabilities. In this case study, the digital twin is emulated by streaming a real-life smart home dataset, while the overall architecture is designed for real‑time deployment.