日韩精品久久久久久久的张开腿让I日韩精品不卡I男女猛烈无遮挡一进一出视频I婷婷玖玖I亚洲欧美wwwI色婷婷综合久久久久I亚洲成人午夜精品I国产高清不卡视频I综合热久久I亚洲一区二区三区四区I毛片基地视频I天天操夜夜摸I欧美激情网站I欧美亚洲啪啪I亚洲国产成人精品女人I91黄色在线视频I青青草91视频I天天躁日日躁aaaaxxxxI法国做爰xxxⅹ性视频I亚洲天堂资源I色女孩综合网I国产欧美精品一区二区三区I蜜桃色在线I色综合久久88色综合天天I在线看91

    Abstract: With the increasing permeability of renewable energy, a large number of uncertain devices pose a threat to the safe and efficient operation of the integrated energy system (IES). In general, solving the uncertainty optimization problem relies on a large amount of historical data, and assists some artificial intelligence technology to predict the analysis of renewable energy, but the usual process of separate prediction and decision making can produce serious deterioration of the model's optimization objective due to excessive prediction errors. Therefore, this paper proposes a prediction-decision method based on K-nearest neighbor (KNN) and robust optimization(RO) to improve the uncertainty optimization problem of IES. Then, the KMV ellipsoid set is constructed using the KNN + minimum volume (KMV) ellipsoid set method, and the two-stage robust model under the set is solved to obtain the optimal multi-energy flow solution. In order to balance the robustness and economy of IES, robust adjustable parameters are used to represent the appropriate level of the uncertainty set. Finally, through the simulation example,the change rule of the interval size and the adjustable parameters of the KMV ellipsoid set is proved, and the superiority of the set is proved.

    Key words: integrated energy system; machine learning; robust optimization; uncertain optimization