Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid
The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encoura...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/9373204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832556865187217408 |
|---|---|
| author | Bingjie He Jinxiu Xiao Qiaorong Dai |
| author_facet | Bingjie He Jinxiu Xiao Qiaorong Dai |
| author_sort | Bingjie He |
| collection | DOAJ |
| description | The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit. |
| format | Article |
| id | doaj-art-066d9d8ed5b047238b75b360a998a5d3 |
| institution | Kabale University |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-066d9d8ed5b047238b75b360a998a5d32025-02-03T05:44:09ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/93732049373204Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart GridBingjie He0Jinxiu Xiao1Qiaorong Dai2Advanced Vocational Technical College, Shanghai University of Engineering Science, Shanghai 200437, ChinaAdvanced Vocational Technical College, Shanghai University of Engineering Science, Shanghai 200437, ChinaAdvanced Vocational Technical College, Shanghai University of Engineering Science, Shanghai 200437, ChinaThe enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.http://dx.doi.org/10.1155/2021/9373204 |
| spellingShingle | Bingjie He Jinxiu Xiao Qiaorong Dai Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid Journal of Mathematics |
| title | Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid |
| title_full | Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid |
| title_fullStr | Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid |
| title_full_unstemmed | Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid |
| title_short | Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid |
| title_sort | data driven consumption load monitoring and adjustment strategy in smart grid |
| url | http://dx.doi.org/10.1155/2021/9373204 |
| work_keys_str_mv | AT bingjiehe datadrivenconsumptionloadmonitoringandadjustmentstrategyinsmartgrid AT jinxiuxiao datadrivenconsumptionloadmonitoringandadjustmentstrategyinsmartgrid AT qiaorongdai datadrivenconsumptionloadmonitoringandadjustmentstrategyinsmartgrid |