Weizhen Ou, Peijie Li, Zonglong Weng, Jiawen Xiao, Xiaoqing Bai
{"title":"Distributionally robust optimal power flow based on multi-transport hyperrectangle ambiguity set","authors":"Weizhen Ou, Peijie Li, Zonglong Weng, Jiawen Xiao, Xiaoqing Bai","doi":"10.1049/gtd2.13360","DOIUrl":null,"url":null,"abstract":"<p>The Wasserstein distributionally robust optimization has become the preferred method for addressing the uncertainties in optimal power flow problems caused by renewable energy sources. However, when the system involves high-dimensional random variables, such as multiple solar or wind farms, the curse of dimensionality associated with this method leads to a slow convergence rate of Wasserstein ambiguity sets. Therefore, it is essential to explore novel ambiguity sets which can effectively address the dimensionality problem. This paper proposes a distributionally robust optimal power flow model based on a multi-transport hyperrectangle ambiguity set to tackle the uncertainties in wind power. First, this paper presents the multi-transport hyperrectangle, which resolves the curse of dimensionality issue associated with Wasserstein ambiguity sets. Furthermore, the wind power curtailment cost in the objective function is reformulated into a tractable form using duality theory, enabling commercial solvers to provide efficient solutions. Finally, tests conducted on the modified IEEE 14-bus and IEEE 118-bus systems demonstrate that the proposed ambiguity set maintains a stable convergence rate under high-dimensional random variables without rapid deterioration as the sample size increases. Moreover, the model achieves significant cost reductions while ensuring system stability.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13360","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Generation Transmission & Distribution","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.13360","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The Wasserstein distributionally robust optimization has become the preferred method for addressing the uncertainties in optimal power flow problems caused by renewable energy sources. However, when the system involves high-dimensional random variables, such as multiple solar or wind farms, the curse of dimensionality associated with this method leads to a slow convergence rate of Wasserstein ambiguity sets. Therefore, it is essential to explore novel ambiguity sets which can effectively address the dimensionality problem. This paper proposes a distributionally robust optimal power flow model based on a multi-transport hyperrectangle ambiguity set to tackle the uncertainties in wind power. First, this paper presents the multi-transport hyperrectangle, which resolves the curse of dimensionality issue associated with Wasserstein ambiguity sets. Furthermore, the wind power curtailment cost in the objective function is reformulated into a tractable form using duality theory, enabling commercial solvers to provide efficient solutions. Finally, tests conducted on the modified IEEE 14-bus and IEEE 118-bus systems demonstrate that the proposed ambiguity set maintains a stable convergence rate under high-dimensional random variables without rapid deterioration as the sample size increases. Moreover, the model achieves significant cost reductions while ensuring system stability.
期刊介绍:
IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix.
The scope of IET Generation, Transmission & Distribution includes the following:
Design of transmission and distribution systems
Operation and control of power generation
Power system management, planning and economics
Power system operation, protection and control
Power system measurement and modelling
Computer applications and computational intelligence in power flexible AC or DC transmission systems
Special Issues. Current Call for papers:
Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf