{"type":"ScholarlyArticle","title":"Integrating Demand Response and Renewable Energy In Wholesale Market","authors":[{"name":"Chaojie Li","slug":"chaojie-li","given":"Chaojie","family":"Li","affiliation":"School of Engineering , RMIT University"},{"name":"Chen Liu","slug":"chen-liu","given":"Chen","family":"Liu","affiliation":"School of Engineering , RMIT University"},{"name":"Xinghuo Yu","slug":"xinghuo-yu","given":"Xinghuo","family":"Yu","affiliation":"School of Engineering , RMIT University"},{"name":"Ke Deng","slug":"ke-deng","given":"Ke","family":"Deng","affiliation":"School of Science , RMIT University"},{"name":"Tingwen Huang","slug":"tingwen-huang","given":"Tingwen","family":"Huang","affiliation":"Texas A&M University at Qatar"},{"name":"Liangchen Liu","slug":"liangchen-liu","given":"Liangchen","family":"Liu","affiliation":"School of ITEE , University of Queensland"}],"abstract":"Demand response (DR) can provide a cost-effect approach for reducing peak loads while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasingly integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the main 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. To obtain a SSCN equilibrium, 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 proposed model is illustrated by extensive simulations.","doi":"10.24963/ijcai.2018/53","url":"https://ijcai.org/proceedings/2018/53","pdf":"https://ijcai.org/proceedings/2018/0053.pdf","markdown":"https://ijcai.org/proceedings/2018/53.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/53","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":53,"pages":{"first":382,"last":388},"keywords":["Machine Learning Applications: Big data ; Scalability","Agent-based and Multi-agent Systems: Noncooperative Games","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2011] Bo An, Milind Tambe, Fernando Ordonez, Eric Anyung Shieh, and Christopher Kiekintveld. 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