基于自适应量子遗传算法的电力系统机组组合问题

采用了一种自适应量子遗传算法来解决机组组合问题.其数学模型以最小燃料成本作为目标函数.考虑的约束条件有功率平衡约束、机组容量约束、最大启停次数约束,以及最小连续停运和运行时间约束.该算法采用了新的量子个体来表示机组的启停状态,提出了针对个体适应值和进化代数的自适应量子旋转角,使个体向更好的解靠近.仿真实验结果验证了自适应量子遗传算法的可行性和优越性.

Research on Unit Commitment Problem in Power System Based on Adaptive Quantum Genetic Algorithm

Adaptive Quantum Genetic Algorithm(AQGA) is presented to solve unit commitment problems. A mathematic model is constructed with the objective function of minimizing the fuel cost. Meanwhile, constraints are considered including power balance constraint, capacity constraint of units, up and down generation constraints of units and minimum turn on and turn off constraint. In AQGA, a Q-bit individual is defined to represent for unit commitment states. An adaptive rotating angle is proposed according to the fitness and iteration.The individual is driven toward better solutions by this adaptive rotating operator. The algorithm is simulated and analyzed by Matlab software. It is shown that AQGA has the features of good convergence and adaptable ability. It is feasible and effective for solving the unit commitment problem.