基于改进遗传算法的AGC机组优化组合研究

为降低发电成本,该文对自动发电控制(AGC)机组优化组合问题进行了研究.基于改进遗传算法,建立了包含AGC的机组优化组合模型;针对遗传算法存在的不足,结合包含AGC机组优化组合模型的特殊性,提出了可变长二进制编码;设计了专门的遗传操作,并采用等微增法对其中的连续变量进行了处理.将所研究的算法和模型应用于包含16台机组24时段的机组优化系统中,仿真结果表明该改进遗传算法的计算结果优于实数编码方法结果11.33%,并在搜索区间及收敛速度等方面都具有较好的性能,适用于大、中型发电系统.

Optimization of Generator Unit Commitment Including AGC Based on Improved Genetic Algorithm

To reduce the generating cost, a method for generator unit commitment including automatic generation control(AGC) is studied here. Based on the improved genetic algorithm, a new model of generator unit commitment including AGC is established. For the existing deficiencies of the standard genetic algorithm and particularity of the model on generator unit commitment including AGC, a variable-length binary encoding is proposed and a special genetic operation is designed, in which the principle of equal incremental rate is used for the continuous variables. The simulations of the 16-machine and 24-hour system show that the results from the improved genetic algorithms and mode optimize 11.33% compared with the results from real encoding. A preferable performance is achieved in search range and convergence speed. The method is suitable for large and medium generating systems.