Abstract
Demand response (DR) can provide a cost-effectiveness approach for reducing peak load while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasing integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. The sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of the proposed model is illustrated by extensive simulations. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 382-388 |
| Volume | 2018-July |
| ISBN (Print) | 9780999241127 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden Duration: 13 Jul 2018 → 19 Jul 2018 |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| Volume | 2018-July |
| ISSN (Print) | 1045-0823 |
Conference
| Conference | 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 |
|---|---|
| Place | Sweden |
| City | Stockholm |
| Period | 13/07/18 → 19/07/18 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This research is supported in part by the Australian Research Council (ARC) under the Discovery Program grant DP170102303, DP160102114, and in part by the National Priorities Research Program from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 9-166-1-031.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy -
SDG 8 Decent Work and Economic Growth -
SDG 13 Climate Action
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