Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm
The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/18/3973 |
| _version_ | 1798004946829836288 |
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| author | Carlos Cruz Esther Palomar Ignacio Bravo Alfredo Gardel |
| author_facet | Carlos Cruz Esther Palomar Ignacio Bravo Alfredo Gardel |
| author_sort | Carlos Cruz |
| collection | DOAJ |
| description | The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community’s energy management. Initially conceived in a centralised way, a data collector called the “aggregator” will handle the operation scheduling requirements given the consumers’ time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment. |
| first_indexed | 2024-04-11T12:32:39Z |
| format | Article |
| id | doaj.art-c27d06902edb47d1a714be16c81893fd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:32:39Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c27d06902edb47d1a714be16c81893fd2022-12-22T04:23:43ZengMDPI AGSensors1424-82202019-09-011918397310.3390/s19183973s19183973Towards Sustainable Energy-Efficient Communities Based on a Scheduling AlgorithmCarlos Cruz0Esther Palomar1Ignacio Bravo2Alfredo Gardel3Department of Electronics, University of Alcala, Alcala de Henares, 28871 Madrid, SpainDepartment of Electronics, University of Alcala, Alcala de Henares, 28871 Madrid, SpainDepartment of Electronics, University of Alcala, Alcala de Henares, 28871 Madrid, SpainDepartment of Electronics, University of Alcala, Alcala de Henares, 28871 Madrid, SpainThe Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community’s energy management. Initially conceived in a centralised way, a data collector called the “aggregator” will handle the operation scheduling requirements given the consumers’ time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.https://www.mdpi.com/1424-8220/19/18/3973cooperative smart communityscheduling algorithmconsumer preferencesrenewables |
| spellingShingle | Carlos Cruz Esther Palomar Ignacio Bravo Alfredo Gardel Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm Sensors cooperative smart community scheduling algorithm consumer preferences renewables |
| title | Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm |
| title_full | Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm |
| title_fullStr | Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm |
| title_full_unstemmed | Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm |
| title_short | Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm |
| title_sort | towards sustainable energy efficient communities based on a scheduling algorithm |
| topic | cooperative smart community scheduling algorithm consumer preferences renewables |
| url | https://www.mdpi.com/1424-8220/19/18/3973 |
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