Improving the scalability of a prosumer cooperative game with K-means clustering
Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number o...
| প্রধান লেখক: | , , , |
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| বিন্যাস: | Conference item |
| ভাষা: | English |
| প্রকাশিত: |
IEEE
2019
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| _version_ | 1826285446255083520 |
|---|---|
| author | Han, L Morstyn, T Crozier, C McCulloch, M |
| author_facet | Han, L Morstyn, T Crozier, C McCulloch, M |
| author_sort | Han, L |
| collection | OXFORD |
| description | Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the “clustered players” to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers. |
| first_indexed | 2024-03-07T01:28:57Z |
| format | Conference item |
| id | oxford-uuid:92ef24e4-a0fc-40de-84b0-fd59805f1a89 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:28:57Z |
| publishDate | 2019 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:92ef24e4-a0fc-40de-84b0-fd59805f1a892022-03-26T23:29:01ZImproving the scalability of a prosumer cooperative game with K-means clusteringConference itemhttp://purl.org/coar/resource_type/c_5794uuid:92ef24e4-a0fc-40de-84b0-fd59805f1a89EnglishSymplectic ElementsIEEE2019Han, LMorstyn, TCrozier, CMcCulloch, MAmong the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the “clustered players” to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers. |
| spellingShingle | Han, L Morstyn, T Crozier, C McCulloch, M Improving the scalability of a prosumer cooperative game with K-means clustering |
| title | Improving the scalability of a prosumer cooperative game with K-means clustering |
| title_full | Improving the scalability of a prosumer cooperative game with K-means clustering |
| title_fullStr | Improving the scalability of a prosumer cooperative game with K-means clustering |
| title_full_unstemmed | Improving the scalability of a prosumer cooperative game with K-means clustering |
| title_short | Improving the scalability of a prosumer cooperative game with K-means clustering |
| title_sort | improving the scalability of a prosumer cooperative game with k means clustering |
| work_keys_str_mv | AT hanl improvingthescalabilityofaprosumercooperativegamewithkmeansclustering AT morstynt improvingthescalabilityofaprosumercooperativegamewithkmeansclustering AT crozierc improvingthescalabilityofaprosumercooperativegamewithkmeansclustering AT mccullochm improvingthescalabilityofaprosumercooperativegamewithkmeansclustering |