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<p>The efficient operation of IoT-integrated energy systems in optimizing household energy consumption is crucial for enhancing the effectiveness and economic advantages of a smart home (SH). This study introduces a mixed integer linear programming (MILP)-based two-layer optimization algorithm for managing household energy consumption, including flexible loads (FL), non-flexible loads (NFL), and temperature regulation appliances such as heating, ventilation, and air conditioning (HVAC) systems. The model also considers charging/discharging strategies for energy storage systems (ESS) and electric vehicles (EV) to efficiently utilize energy produced by renewables like photovoltaic (PV) systems and wind turbines (WT). In the first optimization layer, the model determines the operational schedule of households and facilitates active power exchange between devices to minimize electricity costs, with decision variables including appliance schedules, ESS and EV charging/discharging rates, and power transactions with the grid. The objective is to minimize the total energy cost based on real-time pricing (RTP) or time-of-use (TOU) schemes. The second optimization layer focuses on maximizing the power factor by minimizing reactive power drawn from the grid, using surplus energy from ESS and EV to supply reactive power through bidirectional inverters. Simulations demonstrate a significant improvement in the SH power factor, increasing from an average of 0.61 to 0.97, and a 62.39% reduction in the overall energy consumption bill, validating the model’s efficiency.</p>

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Two-layer optimization algorithm for cost-effective energy management and power factor improvement in IoT-integrated smart home

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Ubaid ur Rehman

摘要

The efficient operation of IoT-integrated energy systems in optimizing household energy consumption is crucial for enhancing the effectiveness and economic advantages of a smart home (SH). This study introduces a mixed integer linear programming (MILP)-based two-layer optimization algorithm for managing household energy consumption, including flexible loads (FL), non-flexible loads (NFL), and temperature regulation appliances such as heating, ventilation, and air conditioning (HVAC) systems. The model also considers charging/discharging strategies for energy storage systems (ESS) and electric vehicles (EV) to efficiently utilize energy produced by renewables like photovoltaic (PV) systems and wind turbines (WT). In the first optimization layer, the model determines the operational schedule of households and facilitates active power exchange between devices to minimize electricity costs, with decision variables including appliance schedules, ESS and EV charging/discharging rates, and power transactions with the grid. The objective is to minimize the total energy cost based on real-time pricing (RTP) or time-of-use (TOU) schemes. The second optimization layer focuses on maximizing the power factor by minimizing reactive power drawn from the grid, using surplus energy from ESS and EV to supply reactive power through bidirectional inverters. Simulations demonstrate a significant improvement in the SH power factor, increasing from an average of 0.61 to 0.97, and a 62.39% reduction in the overall energy consumption bill, validating the model’s efficiency.