{"title":"安全受限机组承诺的可行整数可变热启动策略","authors":"","doi":"10.1016/j.ijepes.2024.110137","DOIUrl":null,"url":null,"abstract":"<div><p>Security-constrained unit commitment (SCUC) is a crucial procedure in power system planning and operation. As renewable resources are integrated, it is suggested to perform sub-hourly SCUC with a 15-minute interval. This change increases the computational burden due to more binary commitment variables. Despite the use of advanced MIP solvers, poor performance continues to be a challenge. Therefore, this paper proposes a feasible-enabled integer-variable warm-start strategy to provide feasible estimated starting values for MIP solvers before optimization. To achieve this objective, a data-driven model based on a deep neural network architecture is designed. This data-driven model takes into consideration the structural characteristics of input data, allowing it to predict the corresponding value of binary commitment variables effectively. Subsequently, an auxiliary optimization model is constructed by combining predicted values with the physical constraints of SCUC, ensuring estimated starting values are within the feasible region and mitigating the adverse effects of incorrect predicted values. Case studies conducted on two large-scale testing systems illustrate the effectiveness of the proposed method.</p></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0142061524003582/pdfft?md5=147c675e152e396508355bda09ab4ed8&pid=1-s2.0-S0142061524003582-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Feasible-enabled integer variable warm start strategy for security-constrained unit commitment\",\"authors\":\"\",\"doi\":\"10.1016/j.ijepes.2024.110137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Security-constrained unit commitment (SCUC) is a crucial procedure in power system planning and operation. As renewable resources are integrated, it is suggested to perform sub-hourly SCUC with a 15-minute interval. This change increases the computational burden due to more binary commitment variables. Despite the use of advanced MIP solvers, poor performance continues to be a challenge. Therefore, this paper proposes a feasible-enabled integer-variable warm-start strategy to provide feasible estimated starting values for MIP solvers before optimization. To achieve this objective, a data-driven model based on a deep neural network architecture is designed. This data-driven model takes into consideration the structural characteristics of input data, allowing it to predict the corresponding value of binary commitment variables effectively. Subsequently, an auxiliary optimization model is constructed by combining predicted values with the physical constraints of SCUC, ensuring estimated starting values are within the feasible region and mitigating the adverse effects of incorrect predicted values. Case studies conducted on two large-scale testing systems illustrate the effectiveness of the proposed method.</p></div>\",\"PeriodicalId\":50326,\"journal\":{\"name\":\"International Journal of Electrical Power & Energy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0142061524003582/pdfft?md5=147c675e152e396508355bda09ab4ed8&pid=1-s2.0-S0142061524003582-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Electrical Power & Energy Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0142061524003582\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrical Power & Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142061524003582","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Feasible-enabled integer variable warm start strategy for security-constrained unit commitment
Security-constrained unit commitment (SCUC) is a crucial procedure in power system planning and operation. As renewable resources are integrated, it is suggested to perform sub-hourly SCUC with a 15-minute interval. This change increases the computational burden due to more binary commitment variables. Despite the use of advanced MIP solvers, poor performance continues to be a challenge. Therefore, this paper proposes a feasible-enabled integer-variable warm-start strategy to provide feasible estimated starting values for MIP solvers before optimization. To achieve this objective, a data-driven model based on a deep neural network architecture is designed. This data-driven model takes into consideration the structural characteristics of input data, allowing it to predict the corresponding value of binary commitment variables effectively. Subsequently, an auxiliary optimization model is constructed by combining predicted values with the physical constraints of SCUC, ensuring estimated starting values are within the feasible region and mitigating the adverse effects of incorrect predicted values. Case studies conducted on two large-scale testing systems illustrate the effectiveness of the proposed method.
期刊介绍:
The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces.
As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.