{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:46:00Z","timestamp":1761597960612},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>\u00a0Demand 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\u00a0 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.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/53","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"382-388","source":"Crossref","is-referenced-by-count":13,"title":["Integrating Demand Response and Renewable Energy In Wholesale Market"],"prefix":"10.24963","author":[{"given":"Chaojie","family":"Li","sequence":"first","affiliation":[{"name":"RMIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Liu","sequence":"additional","affiliation":[{"name":"RMIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinghuo","family":"Yu","sequence":"additional","affiliation":[{"name":"RMIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Deng","sequence":"additional","affiliation":[{"name":"RMIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingwen","family":"Huang","sequence":"additional","affiliation":[{"name":"Texas A&M University at Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangchen","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Queensland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:33Z","timestamp":1530755373000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/53"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/53","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}