Ma Liu-yang, Meng An-bo, Yin Hao. Unit Commitment Optimization Based on Hybrid Crisscross Optimization Algorithm[J]. 2017, 34(6): 68-72.DOI: 10.12052/gdutxb.160161.
Unit Commitment Optimization Based on Hybrid Crisscross Optimization Algorithm
With regard to the characteristics of the unit commitment problem
a hybrid crisscross optimization algorithm (CSO) is proposed
discrete CSO and continuous CSO are used for the unit status scheduling and power economic dispatch
respectively. In the process of evolution
horizontal crossover performs crossover on the same dimension of different particles to guarantee the global convergence ability
and vertical crossover performs crossover on different dimensions of the same particle to overcome the local optimal convergence
and the perfect combination of the two crossovers by a competitive algorithm not only speeds up the convergence rate but also ensures the ability of the global constraints. The heuristic strategy is introduced to modify the constraint conditions
such as the minimum up and down time and the power balance
which can effectively improve the accuracy of the solution. The feasibility and superiority of the proposed algorithm are verified by the simulation of two examples and the comparison with other algorithms.
关键词
Keywords
references
SENJYU T, SHIMABUKURO K, UEZATO K, et al . A fast technique for unit commitment problem by extended priority list [J ] . IEEE Transactions on Power Systems, 2003, 18(2): 882-888.
BAI X M, YU E K. Optimization for unit commitment of electric power system by dynamic programming [J]. Proceedings of the CSEE, 1984, 4(1): 13-21.
HAN X S, LIU Z. Optimal unit commitment considering unit's ramp-rate limits [J]. Power System Technology, 1994, 18(06): 11-16.
DATTA D. Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm [J]. Applied Soft Computing, 2013, 13(9): 3873-3883.
CAI C H, CAI Y Y. Optimization of units commitment by genetic algorithm [J]. Power System Technology, 1997, 21(1): 44-47.
周俊. 机会约束规划下含风电场的机组组合优化[D]. 广州: 广东工业大学自动化学院, 2014.
DATTA D, DUTTA S. A binary-real-coded differential evolution for unit commitment problem [J]. International Journal of Electrical Power Energy Systems, 2012, 42(1): 517-524.
ZHANG J, LIU Q T, SHU P, et al . Unit commitment optimization based on genetic algorithm and particle swarm optimization hybrid algorithm [J ] . Power System Protection and Control, 2009, 37(9): 25-29.
TRIVEDI A, SRINIVASAN D, BISWAS S, et al . A genetic algorithm-differential evolution based hybrid framework: Case study on unit commitment scheduling problem [J ] . Information Sciences, 2016, 354: 275-300.
CHEN J H, LIANG L L, DING L J, et al . Unit commitment optimization based on chance-constrained programming in wind power integrated system [J ] . Journal of Guangdong University of Technology, 2017, 34(1): 50-54.
YUAN X, JI B, ZHANG S, et al . A new approach for unit commitment problem via binary gravitational search algorithm [J ] . Applied Soft Computing, 2014, 22(5): 249-260.
MORADI S, KHANMOHAMMA S, HAGH M T, et al . A semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem [J ] . Energy, 2015, 88: 244-259.
LI Z, TAN W, QIN J L. An improved dual particle swarm optimization algorithm for unit commitment problem [J]. Proceedings of the CSEE, 2012, 32(25): 189-195.
CHEN H L, GUO R P. Unit commitment based on improved discrete particle swarm optimization [J]. Power System Technology, 2011, 35(12): 94-99.
MENG A, CHEN Y, YIN H, et al . Crisscross optimization algorithm and its application [J ] . Knowledge-based Systems, 2014, 67(4): 218-229.
WU J H, WU Y W, XIONG X Y. Optimization of unit commitment by improved Hopfield neural network algorithm [J]. Automation of Electric Power Systems, 2003, 27(7): 41-44.
YUAN X, NIE H, SU A, et al . An improved binary particle swarm optimization for unit commitment problem [J ] . Expert Systems with Applications, 2009, 36(4): 8049-8055.
JEONG Y, PARK J, SHIN J, et al . A thermal unit commitment approach using an improved quantum evolutionary algorithm [J ] . Electric Power Components Systems, 2009, 37(7): 770-786.