Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929225525624832 |
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| author | Chen, Xiaoling Miller, Cory Goutham, Mithun Hanumalagutti, Prasad Dev Blaser, Rachel Stockar, Stephanie |
| author_facet | Chen, Xiaoling Miller, Cory Goutham, Mithun Hanumalagutti, Prasad Dev Blaser, Rachel Stockar, Stephanie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2304_11770 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources Chen, Xiaoling Miller, Cory Goutham, Mithun Hanumalagutti, Prasad Dev Blaser, Rachel Stockar, Stephanie Systems and Control Optimization and Control 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. |
| title | Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2304.11770 |