Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids
The demand response (DR)-considered microgrid (MG) provides a large amount of electricity consumption information, and the value of these data has attracted increasing attention because accurately identifying customers’ electricity consumption behaviour patterns helps public utilities’ dispatch plan...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S014206152400591X |
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| author | Xidong Zheng Feifei Bai Tao Jin |
| author_facet | Xidong Zheng Feifei Bai Tao Jin |
| author_sort | Xidong Zheng |
| collection | DOAJ |
| description | The demand response (DR)-considered microgrid (MG) provides a large amount of electricity consumption information, and the value of these data has attracted increasing attention because accurately identifying customers’ electricity consumption behaviour patterns helps public utilities’ dispatch planning and precise services. This paper investigates how to achieve MGs’ optimal scheduling for analysing customer-side DR identification. To maintain the economics of the MG itself from the optimal scheduling of multiple MGs and the upper-level power system (ULP), a new master–slave management (MSM) is proposed. Then, by integrating the machine learning (ML)-based classifiers, the customer-side DR identification issues caused by abnormal data, such as data missing and label errors in MGs, are solved. A case study using the China State Grid data set proves the effectiveness of the proposed MSM and DR identification strategies. The assessment reveals that the integrated classification and identification centre (ICIC) helps ensure 4.615 average electricity purchase cost assessment (EPCA) and 4.835 for electricity power assessment (EPA), which is higher than abnormal situations without machine learning-based identification. The proposed method maximises customer satisfaction while reducing MG costs. |
| format | Article |
| id | doaj-art-f5409717ee9349048d75273016de44bb |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-f5409717ee9349048d75273016de44bb2025-01-19T06:23:50ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110368Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgridsXidong Zheng0Feifei Bai1Tao Jin2College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, AustraliaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; Fujian Province University Engineering Research Center of Smart Distribution Grid Equipment, Fuzhou 350108, China; Corresponding author at: College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.The demand response (DR)-considered microgrid (MG) provides a large amount of electricity consumption information, and the value of these data has attracted increasing attention because accurately identifying customers’ electricity consumption behaviour patterns helps public utilities’ dispatch planning and precise services. This paper investigates how to achieve MGs’ optimal scheduling for analysing customer-side DR identification. To maintain the economics of the MG itself from the optimal scheduling of multiple MGs and the upper-level power system (ULP), a new master–slave management (MSM) is proposed. Then, by integrating the machine learning (ML)-based classifiers, the customer-side DR identification issues caused by abnormal data, such as data missing and label errors in MGs, are solved. A case study using the China State Grid data set proves the effectiveness of the proposed MSM and DR identification strategies. The assessment reveals that the integrated classification and identification centre (ICIC) helps ensure 4.615 average electricity purchase cost assessment (EPCA) and 4.835 for electricity power assessment (EPA), which is higher than abnormal situations without machine learning-based identification. The proposed method maximises customer satisfaction while reducing MG costs.http://www.sciencedirect.com/science/article/pii/S014206152400591XMicrogrid optimal schedulingDemand response identificationMaster-slave managementMachine learning-based classifierCustomer satisfaction level |
| spellingShingle | Xidong Zheng Feifei Bai Tao Jin Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids International Journal of Electrical Power & Energy Systems Microgrid optimal scheduling Demand response identification Master-slave management Machine learning-based classifier Customer satisfaction level |
| title | Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids |
| title_full | Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids |
| title_fullStr | Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids |
| title_full_unstemmed | Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids |
| title_short | Assessing customer-side demand response identification for optimal scheduling considering satisfaction level for microgrids |
| title_sort | assessing customer side demand response identification for optimal scheduling considering satisfaction level for microgrids |
| topic | Microgrid optimal scheduling Demand response identification Master-slave management Machine learning-based classifier Customer satisfaction level |
| url | http://www.sciencedirect.com/science/article/pii/S014206152400591X |
| work_keys_str_mv | AT xidongzheng assessingcustomersidedemandresponseidentificationforoptimalschedulingconsideringsatisfactionlevelformicrogrids AT feifeibai assessingcustomersidedemandresponseidentificationforoptimalschedulingconsideringsatisfactionlevelformicrogrids AT taojin assessingcustomersidedemandresponseidentificationforoptimalschedulingconsideringsatisfactionlevelformicrogrids |