Sensitivity-based chance-constrained Generation Expansion Planning

被引:20
|
作者
Manickavasagam, Monishaa [1 ]
Anjos, Miguel F. [2 ,3 ]
Rosehart, William D. [1 ]
机构
[1] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB, Canada
[2] Ecole Hautes Etud Commerciales, Gerad, Montreal, PQ, Canada
[3] Ecole Polytech, Montreal, PQ H3C 3A7, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Generation Expansion Planning; Chance-constrained programming; Sensitivity;
D O I
10.1016/j.epsr.2015.05.011
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A Generation Expansion Planning problem with load uncertainty is formulated based on joint chance-constrained programming (CCP) and is solved by incorporating sensitivity into iterative algorithms. These algorithms exploit the characteristics of the system and its response to load variations. Sensitivities help to classify buses according to stress level, and sensitivity-based iterative algorithms distinguish each bus based on its contribution to the overall system reliability. The use of sensitivity overcomes some of the mathematical obstacles to solving joint CCP problems and, in addition, leads to optimal expansion solutions because uncertain loads are correctly estimated. The IEEE 30- and 118-bus test systems are used to demonstrate the proposed algorithms, and the results Of these algorithms are compared with those of other algorithms for solving the joint CCP problem. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:32 / 40
页数:9
相关论文
共 50 条
  • [31] A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning
    Chen, F.
    Huang, G. H.
    Fan, Y. R.
    Chen, J. P.
    [J]. APPLIED ENERGY, 2017, 187 : 291 - 309
  • [32] Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles
    Chai, Runqi
    Tsourdos, Antonios
    Al Savvaris
    Wang, Shuo
    Xia, Yuanqing
    Chai, Senchun
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 57 (02) : 1028 - 1045
  • [33] GENERATION OF DATA-DRIVEN MODELS FOR CHANCE-CONSTRAINED OPTIMIZATION
    Weigert, J.
    Esche, E.
    Hoffmann, C.
    Repke, J. -U.
    [J]. PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON FOUNDATIONS OF COMPUTER-AIDED PROCESS DESIGN, 2019, 47 : 311 - 316
  • [34] Chance-Constrained Active Inference
    van de Laar, Thijs
    Senoz, Ismail
    Ozcelikkale, Ayca
    Wymeersch, Henk
    [J]. NEURAL COMPUTATION, 2021, 33 (10) : 2710 - 2735
  • [35] Trajectory Generation by Chance-Constrained Nonlinear MPC With Probabilistic Prediction
    Zhang, Xiaoxue
    Ma, Jun
    Cheng, Zilong
    Huang, Sunan
    Ge, Shuzhi Sam
    Lee, Tong Heng
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2021, 51 (07) : 3616 - 3629
  • [36] CHANCE-CONSTRAINED GENERALIZED NETWORKS
    CHARNES, A
    KIRBY, M
    RAIKE, W
    [J]. OPERATIONS RESEARCH, 1966, 14 (06) : 1113 - &
  • [37] CHANCE-CONSTRAINED EFFICIENCY EVALUATION
    OLESEN, OB
    PETERSEN, NC
    [J]. MANAGEMENT SCIENCE, 1995, 41 (03) : 442 - 457
  • [38] A NOTE ON CHANCE-CONSTRAINED PROGRAMMING
    HEILMANN, WR
    [J]. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 1983, 34 (06) : 533 - 537
  • [39] CHANCE-CONSTRAINED ACTIVITY ANALYSIS
    THORE, S
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1987, 30 (03) : 267 - 269
  • [40] Chance-constrained genetic algorithms
    Loughlin, DH
    Ranjithan, SR
    [J]. GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 1999, : 368 - 376