Cloud data center participation in smart demand response programs for energy cost minimisation
Abstract Demand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increas...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-10-01
|
| Series: | IET Smart Grid |
| Online Access: | https://doi.org/10.1049/stg2.12082 |
| _version_ | 1811269458416631808 |
|---|---|
| author | Seyed Mohammad Sheikholeslami Amir Masoud Rabiei Mahmoud Mohammad‐Taheri Jamshid Abouei |
| author_facet | Seyed Mohammad Sheikholeslami Amir Masoud Rabiei Mahmoud Mohammad‐Taheri Jamshid Abouei |
| author_sort | Seyed Mohammad Sheikholeslami |
| collection | DOAJ |
| description | Abstract Demand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increases resulting in high energy costs. In this paper, we address the problem of cloud data centre demand response in an SG area in presence of utility companies which compete to attract more customers. We formulate two optimization problems for cloud and data centres to minimise their costs by changing their energy demand. These problems are shown to be convex and can be readily solved by standard convex optimization techniques. We also propose an algorithm for cloud and data centres to participate in DRPs by concurrently performing regional and temporal workload management. The uncertainty of renewable energy generation in data centres is treated by training a multilayer perceptron to predict the generated energy. Numerical results show that the proposed algorithm outperforms other existing algorithms in terms of the energy cost. In addition, our algorithm flattens the energy demand profile of utility companies and balances the electric load across different locations. |
| first_indexed | 2024-04-12T21:42:25Z |
| format | Article |
| id | doaj.art-bada72ec8181479895e6d8be5167d3a5 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-12T21:42:25Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-bada72ec8181479895e6d8be5167d3a52022-12-22T03:15:44ZengWileyIET Smart Grid2515-29472022-10-015538039410.1049/stg2.12082Cloud data center participation in smart demand response programs for energy cost minimisationSeyed Mohammad Sheikholeslami0Amir Masoud Rabiei1Mahmoud Mohammad‐Taheri2Jamshid Abouei3School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran IranSchool of Electrical and Computer Engineering College of Engineering University of Tehran Tehran IranSchool of Electrical and Computer Engineering College of Engineering University of Tehran Tehran IranDepartment of Electrical Engineering Yazd University Yazd IranAbstract Demand Response Programs (DRPs) in Smart Grid (SG) are designed to encourage consumers to shift their loads to regions and hours with less load stress by alternating the price of electricity. On the other hand, with the emergence of cloud computing, the demand for cloud data centres increases resulting in high energy costs. In this paper, we address the problem of cloud data centre demand response in an SG area in presence of utility companies which compete to attract more customers. We formulate two optimization problems for cloud and data centres to minimise their costs by changing their energy demand. These problems are shown to be convex and can be readily solved by standard convex optimization techniques. We also propose an algorithm for cloud and data centres to participate in DRPs by concurrently performing regional and temporal workload management. The uncertainty of renewable energy generation in data centres is treated by training a multilayer perceptron to predict the generated energy. Numerical results show that the proposed algorithm outperforms other existing algorithms in terms of the energy cost. In addition, our algorithm flattens the energy demand profile of utility companies and balances the electric load across different locations.https://doi.org/10.1049/stg2.12082 |
| spellingShingle | Seyed Mohammad Sheikholeslami Amir Masoud Rabiei Mahmoud Mohammad‐Taheri Jamshid Abouei Cloud data center participation in smart demand response programs for energy cost minimisation IET Smart Grid |
| title | Cloud data center participation in smart demand response programs for energy cost minimisation |
| title_full | Cloud data center participation in smart demand response programs for energy cost minimisation |
| title_fullStr | Cloud data center participation in smart demand response programs for energy cost minimisation |
| title_full_unstemmed | Cloud data center participation in smart demand response programs for energy cost minimisation |
| title_short | Cloud data center participation in smart demand response programs for energy cost minimisation |
| title_sort | cloud data center participation in smart demand response programs for energy cost minimisation |
| url | https://doi.org/10.1049/stg2.12082 |
| work_keys_str_mv | AT seyedmohammadsheikholeslami clouddatacenterparticipationinsmartdemandresponseprogramsforenergycostminimisation AT amirmasoudrabiei clouddatacenterparticipationinsmartdemandresponseprogramsforenergycostminimisation AT mahmoudmohammadtaheri clouddatacenterparticipationinsmartdemandresponseprogramsforenergycostminimisation AT jamshidabouei clouddatacenterparticipationinsmartdemandresponseprogramsforenergycostminimisation |