Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs
Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging differe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/19/6130 |
| _version_ | 1797516671820234752 |
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| author | Minseok Jang Hyun-Cheol Jeong Taegon Kim Sung-Kwan Joo |
| author_facet | Minseok Jang Hyun-Cheol Jeong Taegon Kim Sung-Kwan Joo |
| author_sort | Minseok Jang |
| collection | DOAJ |
| description | Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth. |
| first_indexed | 2024-03-10T07:04:08Z |
| format | Article |
| id | doaj.art-bd2085152280444585310218274efe76 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:04:08Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bd2085152280444585310218274efe762023-11-22T15:59:42ZengMDPI AGEnergies1996-10732021-09-011419613010.3390/en14196130Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) TariffsMinseok Jang0Hyun-Cheol Jeong1Taegon Kim2Sung-Kwan Joo3The School of Electrical Engineering, Korea University, Seoul 02841, KoreaThe School of Electrical Engineering, Korea University, Seoul 02841, KoreaThe School of Electrical Engineering, Korea University, Seoul 02841, KoreaThe School of Electrical Engineering, Korea University, Seoul 02841, KoreaSmart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.https://www.mdpi.com/1996-1073/14/19/6130demand side managementdemand responsetime-of-use tariffsmart gridsload profileGaussian mixture model |
| spellingShingle | Minseok Jang Hyun-Cheol Jeong Taegon Kim Sung-Kwan Joo Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs Energies demand side management demand response time-of-use tariff smart grids load profile Gaussian mixture model |
| title | Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs |
| title_full | Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs |
| title_fullStr | Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs |
| title_full_unstemmed | Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs |
| title_short | Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs |
| title_sort | load profile based residential customer segmentation for analyzing customer preferred time of use tou tariffs |
| topic | demand side management demand response time-of-use tariff smart grids load profile Gaussian mixture model |
| url | https://www.mdpi.com/1996-1073/14/19/6130 |
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