基于蚁群算法的梯级水电站群优化调度

提出一种求解梯级水电站中长期优化调度问题的方法一蚁群算法（Ant Colony algorithm，ACA）。算法模拟了蚂蚁群体觅食路径的搜索过程来寻找梯级水电站中长期最优调度计划。算法把问题解抽象为蚂蚁路径，利用状态转移、信息素更新和邻域搜索以获取最短路径即最优解。实例计算结果表明，算法可以求解具有复杂约束条件的非线性梯级优化调度问题。算法求解精度高、收敛速度快，为解决梯级水电站中长期优化调度问题提供了一种有效的方法。

Optimal operation of cascade hydropower stations based on ant colony algorithm

An ant colony algorithm (ACA) to solve the long-term optimal operation of cascade hydropower stations is presented in this paper. The ACA is simulated with the search food process of ant colony to obtain the optimal scheduling. The ACA is abstracted as the ant's path from the solution of the problem. The state transition rule, global updating information and the neighbor search are also employed to get the optimal scheduling. Case study result proves that ACA can solve nonlinear problem with complex constrains. The ACA shows its advantages on computing speed and convergence. Therefore an new efficient method is provided for optimal operation of cascade hydropower stations.