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Article

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

1
School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
2
Korea Electric Power Research Institute, 105 Munji-ro, Yuseong-gu, Daejeon 34056, Korea
*
Author to whom correspondence should be addressed.
Energies 2020, 13(6), 1348; https://doi.org/10.3390/en13061348
Received: 26 February 2020 / Revised: 9 March 2020 / Accepted: 11 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Demand Response in Smart Grids)
Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers. View Full-Text
Keywords: data analysis; demand response (DR), load profile clustering; k-means; targeting of customer data analysis; demand response (DR), load profile clustering; k-means; targeting of customer
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MDPI and ACS Style

Lee, E.; Kim, J.; Jang, D. Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study. Energies 2020, 13, 1348. https://doi.org/10.3390/en13061348

AMA Style

Lee E, Kim J, Jang D. Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study. Energies. 2020; 13(6):1348. https://doi.org/10.3390/en13061348

Chicago/Turabian Style

Lee, Eunjung; Kim, Jinho; Jang, Dongsik. 2020. "Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study" Energies 13, no. 6: 1348. https://doi.org/10.3390/en13061348

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