Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Impact of User Convenience on Appliance Scheduling of a Home Energy Management System

  • Shin, Je-Seok (Dept. of Electrical Engineering, Hanyang University) ;
  • Bae, In-Su (Dept. of Electrical Engineering, Kangwon National University) ;
  • Kim, Jin-O (Dept. of Electrical Engineering, Hanyang University)
  • Received : 2017.02.22
  • Accepted : 2017.08.21
  • Published : 2018.01.01
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Abstract

Regarding demand response (DR) by residential users (R-users), the users try to reduce electricity costs by adjusting their power consumption in response to the time-varying price. However, their power consumption may be affected not only by the price, but also by user convenience for using appliances. This paper proposes a methodology for appliance scheduling (AS) that considers the user convenience based on historical data. The usage pattern for appliances is first modeled applying the copula function or clustering method to evaluate user convenience. As the modeling results, the comfort distribution or representative scenarios are obtained, and then used to formulate a discomfort index (DI) to assess the degree of the user convenience. An AS optimization problem is formulated in terms of cost and DI. In the case study, various AS tasks are performed depending on the weights for cost and DI. The results show that user convenience has significant impacts on AS. The proposed methodology can contribute to induce more DR participation from R-users by reflecting properly user convenience to AS problem.

Keywords

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Fig. 1. Procedure of GC-based modeling

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Fig. 2. Cumulative and sampling data generated by GC

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Fig. 3. Extracted sample data for Tp in and Tp Out

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Fig. 4. Comfort distribution for Tp in (76)

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Fig. 5. Procedure for clustering-based modeling

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Fig. 6. Comfort distribution of DA

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Fig. 7. Comfort distribution for Tp in at a certain time t

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Fig. 8. Representative scenarios for hot water usage

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Fig. 9. Cumulative and forecasted DARTP

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Fig. 10. Cumulative data of Tp Out and Tp In

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Fig. 11. Comfort distributions of Das

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Fig. 12. Comfort distributions for Tp In

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Fig. 13. Representative scenarios for hot water usage

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Fig. 14. Results of AS for HVAC in Base Case and Case I

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Fig. 15. Results of Tp In in Base Case and Case I

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Fig. 16. Results of AS for EWH

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Fig. 17. AS results for all appliances

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Fig. 18. AS results for all appliances with ESS

Table 1. Detailed Information on Appliances

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Table 2. AS Results for Das

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Table 3. Numerical Results for HVAC and EWH

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