A Multi-Period Mathematical Model to Optimize Smart Home Energy Management with Scheduling of Appliances and Solar Cell Cost Calculation

Document Type : Industry Article

Authors

1 MSc, Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

2 Assistant Professor, Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

Abstract

The smart home is equipped with programmable home appliances designed to be used during off-peak hours when energy prices are lower. Research in smart home energy management has focused on developing mathematical models for planning and scheduling programmable appliances for the next day. While many countries announce daily energy prices for the following day, some countries, like Iran, have fixed energy prices for longer durations. Previous research has primarily focused on selling energy back to the grid when considering smart homes connected to renewable energy sources. Meeting the home's energy needs with renewable energy is a secondary priority. This study presents two mathematical models for multi-period planning daily, weekly, monthly, and yearly of a smart home. The first model does not consider the integration of solar cells, while the second model is connected to solar cells to maximize the use of renewable energy. The proposed mathematical models are solved using the CPLEX solver embedded in the GAMS software.
By comparing the prices obtained from the two models, the cost of solar cells with the desired production capacity is calculated. The results demonstrate a significant reduction in energy consumption costs. These models provide optimized schedules for operating appliances in a smart home, taking into account fluctuating energy prices. The integration of solar cells enables homeowners to leverage renewable energy sources and reduce reliance on the grid.

Keywords

Main Subjects

References

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Journal of Modeling in Engineering
Volume 22, Issue 77
August 2024
Pages 129-156

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History

  • Receive Date: 16 May 2023
  • Revise Date: 12 November 2023
  • Accept Date: 23 December 2023

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