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Development and evaluation of an online home energy management strategy for load coordination in smart homes with renewable energy sources

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  • Chen, Xiaoling
  • Miller, Cory
  • Goutham, Mithun
  • Hanumalagutti, Prasad Dev
  • Blaser, Rachel
  • Stockar, Stephanie

Abstract

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50% of the flexible loads, which is possible due to the use of the stationary energy storage.

Suggested Citation

  • Chen, Xiaoling & Miller, Cory & Goutham, Mithun & Hanumalagutti, Prasad Dev & Blaser, Rachel & Stockar, Stephanie, 2024. "Development and evaluation of an online home energy management strategy for load coordination in smart homes with renewable energy sources," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035284
    DOI: 10.1016/j.energy.2023.130134
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    References listed on IDEAS

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    1. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    2. Mahmoud H. Elkholy & Tomonobu Senjyu & Mohammed Elsayed Lotfy & Abdelrahman Elgarhy & Nehad S. Ali & Tamer S. Gaafar, 2022. "Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving," Sustainability, MDPI, vol. 14(21), pages 1-22, October.
    3. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    4. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
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