DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS
The increasing use of thermostatically controlled loads (TCLs) like refrigerators poses a significant challenge to the grid due to their potential to increase peak demand. This study introduces a novel rule-based peak-shaving algorithm to effectively manage these loads. The algorithm operates in two...
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| Format: | Article |
| Language: | English |
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Technical University of Cluj-Napoca
2024-12-01
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| Series: | Carpathian Journal of Electrical Engineering |
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| Online Access: | https://cee.cunbm.utcluj.ro/wp-content/uploads/CJEE20245.pdf |
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| author | Daniel KWEGYIR Francis Effah BOAFO Daniel OPOKU Emmanuel Asuming FRIMPONG |
| author_facet | Daniel KWEGYIR Francis Effah BOAFO Daniel OPOKU Emmanuel Asuming FRIMPONG |
| author_sort | Daniel KWEGYIR |
| collection | DOAJ |
| description | The increasing use of thermostatically controlled loads (TCLs) like refrigerators poses a significant challenge to the grid due to their potential to increase peak demand. This study introduces a novel rule-based peak-shaving algorithm to effectively manage these loads. The algorithm operates in two modes: day-ahead and real-time. In the day-ahead mode, Long Short-Term Memory (LSTM) neural networks are utilized to forecast demand and generation. A Parameter tuned Grey Wolf Optimizer (GWOP) is proposed and employed to determine the optimal generation for the initial timestep of the scheduling period. The GWOP is tuned using a brute-force grid search method to optimize its parameters. In the real-time mode, the algorithm dynamically adjusts refrigerator operations based on real-time mismatch calculations between predicted demand and generation. Dynamic flexibility thresholds are employed to determine the optimal operation of refrigerators during peak and off-peak periods. This approach aims to minimize energy consumption while maintaining thermal comfort. The algorithm’s performance was evaluated using real-world data from the Spanish Transmission Service Operators (TSO). The results demonstrate a significant reduction in peak demand and total energy consumption. The algorithm with dynamic flexibility achieved a substantial 18.89% reduction in peak demand and a notable 12.12% decrease in total energy consumption. |
| format | Article |
| id | doaj-art-2d3eca5d56b942e88f79c047d4245061 |
| institution | DOAJ |
| issn | 1843-7583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Technical University of Cluj-Napoca |
| record_format | Article |
| series | Carpathian Journal of Electrical Engineering |
| spelling | doaj-art-2d3eca5d56b942e88f79c047d42450612025-08-20T02:58:41ZengTechnical University of Cluj-NapocaCarpathian Journal of Electrical Engineering1843-75832024-12-011818511610.34302/CJEE/QSEG2220DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORSDaniel KWEGYIR0Francis Effah BOAFO1Daniel OPOKU2Emmanuel Asuming FRIMPONG 3Kwame Nkrumah University of Science and Technology, Kumasi, GhanaKwame Nkrumah University of Science and Technology, Kumasi, GhanaKwame Nkrumah University of Science and Technology, Kumasi, GhanaKwame Nkrumah University of Science and Technology, Kumasi, GhanaThe increasing use of thermostatically controlled loads (TCLs) like refrigerators poses a significant challenge to the grid due to their potential to increase peak demand. This study introduces a novel rule-based peak-shaving algorithm to effectively manage these loads. The algorithm operates in two modes: day-ahead and real-time. In the day-ahead mode, Long Short-Term Memory (LSTM) neural networks are utilized to forecast demand and generation. A Parameter tuned Grey Wolf Optimizer (GWOP) is proposed and employed to determine the optimal generation for the initial timestep of the scheduling period. The GWOP is tuned using a brute-force grid search method to optimize its parameters. In the real-time mode, the algorithm dynamically adjusts refrigerator operations based on real-time mismatch calculations between predicted demand and generation. Dynamic flexibility thresholds are employed to determine the optimal operation of refrigerators during peak and off-peak periods. This approach aims to minimize energy consumption while maintaining thermal comfort. The algorithm’s performance was evaluated using real-world data from the Spanish Transmission Service Operators (TSO). The results demonstrate a significant reduction in peak demand and total energy consumption. The algorithm with dynamic flexibility achieved a substantial 18.89% reduction in peak demand and a notable 12.12% decrease in total energy consumption.https://cee.cunbm.utcluj.ro/wp-content/uploads/CJEE20245.pdfthermostatically controlled loadspeak shavingrule-based algorithmdynamic flexibility |
| spellingShingle | Daniel KWEGYIR Francis Effah BOAFO Daniel OPOKU Emmanuel Asuming FRIMPONG DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS Carpathian Journal of Electrical Engineering thermostatically controlled loads peak shaving rule-based algorithm dynamic flexibility |
| title | DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS |
| title_full | DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS |
| title_fullStr | DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS |
| title_full_unstemmed | DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS |
| title_short | DATA DRIVEN RULE-BASED PEAK SHAVING ALGORITHM FOR SCHEDULING REFRIGERATORS |
| title_sort | data driven rule based peak shaving algorithm for scheduling refrigerators |
| topic | thermostatically controlled loads peak shaving rule-based algorithm dynamic flexibility |
| url | https://cee.cunbm.utcluj.ro/wp-content/uploads/CJEE20245.pdf |
| work_keys_str_mv | AT danielkwegyir datadrivenrulebasedpeakshavingalgorithmforschedulingrefrigerators AT franciseffahboafo datadrivenrulebasedpeakshavingalgorithmforschedulingrefrigerators AT danielopoku datadrivenrulebasedpeakshavingalgorithmforschedulingrefrigerators AT emmanuelasumingfrimpong datadrivenrulebasedpeakshavingalgorithmforschedulingrefrigerators |