Mehrdad Aghamohamadi, A. Mahmoudi, J. Ward, Meta-Analyses. Sleep, M. H. Haque
{"title":"Recourse-based BCD Robust Integrated Bidding Strategy for Multi-energy Systems under Uncertainties of Load and Energy Prices","authors":"Mehrdad Aghamohamadi, A. Mahmoudi, J. Ward, Meta-Analyses. Sleep, M. H. Haque","doi":"10.1109/ECCE47101.2021.9595428","DOIUrl":null,"url":null,"abstract":"This study proposes a recourse-based robust integrated bidding strategy for multi-energy systems under uncertainties of load and energy prices. A tri-level min-max-min robust problem is developed which is solved through a column-and-constraint (C& C) generation technique to recast the min-max-min problem into a min master problem and a max-min sub-problem. Unlike previous conventional dual-based robust models which solved the max-min sub-problem by duality theory, this study employs block-coordinate decent (BCD) technique to solve it, using first-order Taylor series approximation of the sub-problem. The benefit of such approach is that the bidding binary variables can be obtained in the sub-problem as recourse decisions after uncertainty realizations which results in considerably more practical and realistic bidding solutions. This was not applicable in previous dual-based robust models due to the lack of tractability of a dualized mixed-integer model. Another advantage is that the linearization of the dualized inner problem is avoided as Lagrange multipliers are eliminated in this study which results in a more moderate computational time. In addition, it is possible to consider different buying/selling bids for the model as bidding binary variables are modeled in the sub-problem. A comprehensive case study as well as a post-event analysis are developed for an energy hub to illustrate the optimal bidding decisions and compare the effectiveness of the proposed model with conventional dual-based models.","PeriodicalId":349891,"journal":{"name":"2021 IEEE Energy Conversion Congress and Exposition (ECCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Energy Conversion Congress and Exposition (ECCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECCE47101.2021.9595428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposes a recourse-based robust integrated bidding strategy for multi-energy systems under uncertainties of load and energy prices. A tri-level min-max-min robust problem is developed which is solved through a column-and-constraint (C& C) generation technique to recast the min-max-min problem into a min master problem and a max-min sub-problem. Unlike previous conventional dual-based robust models which solved the max-min sub-problem by duality theory, this study employs block-coordinate decent (BCD) technique to solve it, using first-order Taylor series approximation of the sub-problem. The benefit of such approach is that the bidding binary variables can be obtained in the sub-problem as recourse decisions after uncertainty realizations which results in considerably more practical and realistic bidding solutions. This was not applicable in previous dual-based robust models due to the lack of tractability of a dualized mixed-integer model. Another advantage is that the linearization of the dualized inner problem is avoided as Lagrange multipliers are eliminated in this study which results in a more moderate computational time. In addition, it is possible to consider different buying/selling bids for the model as bidding binary variables are modeled in the sub-problem. A comprehensive case study as well as a post-event analysis are developed for an energy hub to illustrate the optimal bidding decisions and compare the effectiveness of the proposed model with conventional dual-based models.