A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances
In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be ab...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9599666/ |
| _version_ | 1818344925509451776 |
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| author | Claudia De Vizia Edoardo Patti Enrico Macii Lorenzo Bottaccioli |
| author_facet | Claudia De Vizia Edoardo Patti Enrico Macii Lorenzo Bottaccioli |
| author_sort | Claudia De Vizia |
| collection | DOAJ |
| description | In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be able to communicate its preferences “a-priori”, leaving to the central entity the task of understanding preferences that should be learnt without causing discomfort to the user. With this premise, this study aims at exploring a DSM program that learns the acceptance of realistic simulated users to shift in time of home appliances, such as washing machines and dishwashers, analysing the benefits that arise from their inclusion. To this end, the proposed Acceptance Learning Algorithm 2.0 (ALA 2.0) minimises costs in scenarios with different energy sources and with a certain level of acceptance to shift in time, optimally scheduling the appliances according to the boundaries found by the proposed algorithm. ALA 2.0 is able to understand preferences also when modelling a behaviour of the user which is influenced by external factors not directly observable and when users make very few requests, interacting with the user in a simple way. Experimental results highlight that it is possible to understand the acceptance to the shift in time of the simulated users without any prior knowledge and without causing too much discomfort, achieving a win-win situation. As an example, more than 90% of requests were accepted in December, which is chosen as a representative month. |
| first_indexed | 2024-12-13T16:54:13Z |
| format | Article |
| id | doaj.art-6505e0e70dab42aa973e8214a9f6020f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T16:54:13Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6505e0e70dab42aa973e8214a9f6020f2022-12-21T23:37:57ZengIEEEIEEE Access2169-35362021-01-01915049515050710.1109/ACCESS.2021.31252479599666A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential AppliancesClaudia De Vizia0https://orcid.org/0000-0002-4229-3417Edoardo Patti1https://orcid.org/0000-0002-6043-6477Enrico Macii2https://orcid.org/0000-0001-9046-5618Lorenzo Bottaccioli3https://orcid.org/0000-0001-7445-3975Department of Computer and Control Engineering, Politecnico di Torino, Turin, ItalyDepartment of Computer and Control Engineering, Politecnico di Torino, Turin, ItalyInteruniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, ItalyInteruniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, ItalyIn the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be able to communicate its preferences “a-priori”, leaving to the central entity the task of understanding preferences that should be learnt without causing discomfort to the user. With this premise, this study aims at exploring a DSM program that learns the acceptance of realistic simulated users to shift in time of home appliances, such as washing machines and dishwashers, analysing the benefits that arise from their inclusion. To this end, the proposed Acceptance Learning Algorithm 2.0 (ALA 2.0) minimises costs in scenarios with different energy sources and with a certain level of acceptance to shift in time, optimally scheduling the appliances according to the boundaries found by the proposed algorithm. ALA 2.0 is able to understand preferences also when modelling a behaviour of the user which is influenced by external factors not directly observable and when users make very few requests, interacting with the user in a simple way. Experimental results highlight that it is possible to understand the acceptance to the shift in time of the simulated users without any prior knowledge and without causing too much discomfort, achieving a win-win situation. As an example, more than 90% of requests were accepted in December, which is chosen as a representative month.https://ieeexplore.ieee.org/document/9599666/Demand side managementenergy aggregatoragent based modelinglearn user preferenceuser acceptance |
| spellingShingle | Claudia De Vizia Edoardo Patti Enrico Macii Lorenzo Bottaccioli A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances IEEE Access Demand side management energy aggregator agent based modeling learn user preference user acceptance |
| title | A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances |
| title_full | A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances |
| title_fullStr | A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances |
| title_full_unstemmed | A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances |
| title_short | A Win-Win Algorithm for Learning the Flexibility of Aggregated Residential Appliances |
| title_sort | win win algorithm for learning the flexibility of aggregated residential appliances |
| topic | Demand side management energy aggregator agent based modeling learn user preference user acceptance |
| url | https://ieeexplore.ieee.org/document/9599666/ |
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