Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study
| Autor: | Jinho Kim, Dongsik Jang, Eunjung Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Demand reduction Operations research Computer science k-means 020209 energy data analysis Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology 01 natural sciences Load profile Demand response 010104 statistics & probability Peak demand Market segmentation 0202 electrical engineering electronic engineering information engineering Segmentation 0101 mathematics Electrical and Electronic Engineering Engineering (miscellaneous) Consumption (economics) lcsh:T Renewable Energy Sustainability and the Environment business.industry targeting of customer Incentive demand response (DR) load profile clustering demand response (dr) load profile clustering Electricity business Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 6; Pages: 1348 Energies, Vol 13, Iss 6, p 1348 (2020) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13061348 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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