Appliance Load Profile Assessment for Automated DR Program in Residential Buildings
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초록
The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers’ willingness to participate in the program.
Abstract
The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers’ willingness to participate in the program.
참고문헌 (12)
- K.Wang, "A survey on energy internet: architecture, approach and emerging technologies", IEEE Systems Journal, 12(3), pp.2403~2416 (2018)
- W. Li, "Demand Response Management for Residential Smart Grid:From Theory to Practice", IEEE Access, 3, pp.2431~2440 (2015)
- S. Althaher, "Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints", IEEE Transactions on Smart Grid, 6(4), pp.1874~1883 (2015)
- International Electrotechnical Commission (IEC), "System Interface between customer energy management system and power management system – Part 10-1: Open automated demand response (OpenADR2.0b profile specification)", IEC, 2014
- G.D. Nugraha, "A design of data-driven energy-use profiling in residential buildings:poster abstract", Proc. of the 5th Conference on System for Built Environments (BuildSys 18), pp.200~201 (2018), ACM
- Y. Chen, "Consumer operational comfort level based power demand management in the smart grid", Innovative Smart Grid Technologies (ISGT Europe)IEEE PES, pp.1~16 (2012)
- N. Zhang, "Investigating the impact of demand-side management on residential customers", Innovative Smart Grid Technologies (ISGT Europe) IEEE PES, pp.1~6 (2011)
- H. Rashid, "Revisiting Selection of Residential Consumers for Demand Response Programs", Proc. of 4th ACM International Conference for Energy-Efficient Built Environments (BuildSys `17) (2017)
- M. Afzalan, "Efficient integration of smart appliances for demand response programs", Proc. of the 5th Conference on Systems for Built Environments (BuildSys `18) (2018), ACM
- J. Z. Kolter, "REDD: A Public Data Set for Energy Disaggregation Research", Proc. of SustKDD Workshop on Data Mining Applications in Sustainability, pp.1~6 (2011)
- D. Murray, "An electrical load measurements dataset of united kingdom households from a two-year longitudinal study", Scientific data, 4, pp.160122~ (2017)
- M. Pipattanasomporn, "Load Profiles of selected major household appliances and their demand response opportunities", IEEE Transactions and Smart Grid, 5(2), pp.742~750 (2014)