Gas-steam combined cycle units, with gas as fuel, are more efficient than the thermal units. The output of the combined system includes gas turbine and steam turbine load. With the load variation during the operation, it is difficult for the main equipment to keep running under the economic load all the time, and the fuel consumption will increase compared with the optimal working conditions. In this paper, special two-one economic consuming models are established as the base of dispatch problems, with the steam extraction flow and low-pressure water extraction flow as the model input, while the heat output as the model output. A hybrid algorithm is proposed in this paper by integrating the SA strategy into the basic BBO algorithm, called SABBO algorithm, to solve optimization problem. In this modified algorithm, a simulated annealing operator is used to randomly perturb the best individual retains after migration and mutation operating in the basic BBO. A benchmark function is then taken to test the proposed method’s optimal capacity. The proposed algorithm is then used in load optimal distribution model for gas-steam combined cycle units (one-one units and one two-one units). The optimal fuel flow calculated with SABBO algorithm by Optimal 2 is 202.17 t/h, lower than 206.63 t/h by Optimal 1 when power and heat demands are 1000 MW and 575 MW, respectively. Moreover, optimization results by BBO and SABBO algorithms under Optimal 2 solution with demands as [1000, 575], [1000, 700], and [800, 575] MW are listed in the paper. This case tells SABBO algorithm under Optimal 2 solution can be used in gas-steam combined cycle units dispatch problems for lower fuel flow consumption as 202.17 t/h, 211.01 t/h, and 171.76 t/h.

以燃气为燃料的燃气-蒸汽联合循环装置比热力装置更有效率。组合系统的输出包括燃气轮机和蒸汽轮机负荷。在运行过程中，随着负载的变化，主要设备很难一直在经济负载下运行，与最佳工作条件相比，油耗会增加。本文建立了特殊的二合一经济消耗模型作为调度问题的基础，以蒸汽提取流量和低压提取流量为模型输入，热量输出为模型输出。通过将SA策略集成到基本的BBO算法SABBO算法中，提出了一种混合算法来解决优化问题。在此修改算法中，模拟的退火算子用于在基本BBO中进行迁移和突变后随机扰动最佳个体保留。然后采用基准函数来测试所提出方法的最佳容量。然后将所提出的算法用于燃气-蒸汽联合循环机组（一对一机组和一对二一机组）的负荷最优分配模型。当功率和热量需求分别为1000 MW和575 MW时，通过最佳2的SABBO算法计算的最佳燃料流量为202.17 t / h，低于最佳1的206.63 t / h。此外，本文还列出了在最优2解下，BBO和SABBO算法的优化结果，其需求分别为[1000，575]，[1000，700]和[800，575] MW。