Unit commitment by an enhanced simulated annealing algorithm

被引:20
|
作者
Simopoulos, Dimitris N. [1 ]
Kavatza, Stavroula D. [1 ]
Vournas, Costas D. [1 ]
机构
[1] Natl Tech Univ Athens, Sch Elect & Comp Engn, GR-15773 Athens, Greece
关键词
dynamic economic dispatch (DED); ramp rate constraints; simulated annealing (SA); unit commitment (UC);
D O I
10.1109/PSCE.2006.296296
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
A new simulated annealing (SA) algorithm combined with a dynamic economic dispatch method has been developed for solving the short-term unit commitment (UC) problem. SA is used for the scheduling of the generating units, while a dynamic economic dispatch method is applied incorporating the ramp rate constraints in the solution of the UC problem. New rules concerning the tuning of the control parameters of the SA algorithm are proposed. Three alternative mechanisms for generating feasible trial solutions in the neighborhood of the current one, contributing to the reduction of the required CPU time, are also presented. The ramp rates are taken into account by performing either a backward or a forward sequence of conventional economic dispatches with modified limits on the generating units. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Numerical simulations have proved the effectiveness of the proposed algorithm in solving large UC problems within a reasonable execution time.
引用
收藏
页码:193 / +
页数:2
相关论文
共 50 条
  • [41] Renewable energy unit commitment, with different acceptance of balanced power, solved by simulated annealing
    Garlik, Bohumir
    Krivan, Milos
    ENERGY AND BUILDINGS, 2013, 67 : 392 - 402
  • [42] Itinerary Recommendation Generation using Enhanced Simulated Annealing Algorithm
    Hanafiah, Novita
    Wijaya, Indra
    Xavier, Steffan
    Young, Choandrio Grolyus
    Adrianto, Dennise
    Shodiq, Muhsin
    4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMPUTATIONAL INTELLIGENCE (ICCSCI 2019) : ENABLING COLLABORATION TO ESCALATE IMPACT OF RESEARCH RESULTS FOR SOCIETY, 2019, 157 : 605 - 612
  • [43] Solution of Unit Commitment Problem Using Enhanced Genetic Algorithm
    Singhal, Prateek K.
    Naresh, R.
    Sharma, Veena
    Kumar, Goutham N.
    2014 EIGHTEENTH NATIONAL POWER SYSTEMS CONFERENCE (NPSC), 2014,
  • [44] Using an enhanced genetic algorithm to solve the unit commitment problem
    Zhu, MY
    Cen, WH
    Wang, MY
    Zhang, PC
    1997 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT PROCESSING SYSTEMS, VOLS 1 & 2, 1997, : 611 - 614
  • [45] Enhanced genetic algorithm with guarantee of feasibility for the Unit Commitment problem
    Sandou, Guillaume
    Font, Stephane
    Tebbani, Sihem
    Hiret, Arnaud
    Mondon, Christian
    ARTIFICIAL EVOLUTION, 2008, 4926 : 291 - +
  • [46] An evolutionary programming based simulated annealing method for unit commitment problem with cooling - banking constraints
    Rajan, C. Christober Asir
    Journal of the Institution of Engineers (India): Electrical Engineering Division, 2007, 87 (MAR.): : 12 - 16
  • [47] Optimal Dispatching Methods for Unit Commitment Based on Hybrid Genetic-Simulated Annealing Algorithmd
    Zheng, Xiaojing
    PROGRESS IN MECHATRONICS AND INFORMATION TECHNOLOGY, PTS 1 AND 2, 2014, 462-463 : 1076 - 1080
  • [48] An evolutionary programming based simulated annealing method for unit commitment problem with cooling - Bank constraints
    Rajan, CCA
    PROCEEDINGS OF THE IEEE INDICON 2004, 2004, : 435 - 440
  • [49] Simulated annealing, weighted simulated annealing and genetic algorithm at work
    Bergeret, F
    Besse, P
    COMPUTATIONAL STATISTICS, 1997, 12 (04) : 447 - 465
  • [50] Unit commitment problem using enhanced particle swarm optimization algorithm
    Xiaohui Yuan
    Anjun Su
    Hao Nie
    Yanbin Yuan
    Liang Wang
    Soft Computing, 2011, 15 : 139 - 148