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在分析潮汐电站运行特性基础上,建立其多维优化的月周期优化调度模型。该模型含有线性和非线性约束,而且目标函数呈非线性。利用整体算术交叉和精英保留策略,设计出一种全局寻优的浮点数编码改进遗传算法。算法实例求解结果与电站实际月发电量和动态规划法求解结果比较后表明,该算法可行、有效,提高了求解质量和运行效率。
Based on the analysis of the operation characteristics of tidal power station, a multi-dimensional optimized monthly cycle optimization scheduling model is established. The model contains linear and nonlinear constraints, and the objective function is nonlinear. Using global arithmetic crossover and elite retention strategy, a globally optimized floating-point coding genetic algorithm is designed. The comparison of the result of the algorithm with the actual monthly generating capacity of the power station and the result of the dynamic programming shows that the algorithm is feasible and effective and improves the quality of the solution and the operating efficiency.